diff --git a/.Rbuildignore b/.Rbuildignore index a75e3b5a..71533c3e 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -40,3 +40,4 @@ ^pkgdown$ ^CRAN-SUBMISSION$ ^LICENSE\.md$ +^\.github$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 00000000..2d19fc76 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 00000000..c9f0165d --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,50 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + release: + types: [published] + workflow_dispatch: + +name: pkgdown + +permissions: read-all + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.5.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.gitignore b/.gitignore index 8dd27b17..d24116c9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,21 +1,22 @@ -# Emacs backup files -*~ -\#*\# -# Emacs backups of hidden files, like ".gitignore.~3~" -.*.~*~ -.\#* -# RStudio/R GUI -.Rbuildignore -.RData -.Rhistory -.Rproj.user -*.Rproj -# Vignettes -vignettes/*.bbl -vignettes/*.log -vignettes/*.md -vignettes/*.pdf -vignettes/*.synctex.gz -vignettes/*.tex -revdep -pkgdown +# Emacs backup files +*~ +\#*\# +# Emacs backups of hidden files, like ".gitignore.~3~" +.*.~*~ +.\#* +# RStudio/R GUI +.Rbuildignore +.RData +.Rhistory +.Rproj.user +*.Rproj +# Vignettes +vignettes/*.bbl +vignettes/*.log +vignettes/*.md +vignettes/*.pdf +vignettes/*.synctex.gz +vignettes/*.tex +revdep +docs/ +docs diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 00000000..d71acfb9 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: ~ +template: + bootstrap: 5 + diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index 92c71306..00000000 --- a/docs/404.html +++ /dev/null @@ -1,131 +0,0 @@ - - - - - - - -Page not found (404) • agridat - - - - - - - - - - - - - - - - - - -
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- - - - - - - - diff --git a/docs/articles/agridat_data.html b/docs/articles/agridat_data.html deleted file mode 100644 index 7e34f280..00000000 --- a/docs/articles/agridat_data.html +++ /dev/null @@ -1,993 +0,0 @@ - - - - - - - - • agridat - - - - - - - - - - - - - - - - - - - -
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- - - Source: ../../../Users/wrightkevi/OneDrive/kw_work/rpack/agridat/vignettes/agridat_data.Rmd - - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
title: “Additional sources of agricultural data”
author: “Kevin Wright”
date: “2021-12-17”
output:
rmarkdown::html_vignette:
md_extensions: [
“-autolink_bare_uris”
]
vignette: >
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Books -

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-Die Landwirtschaftlichen Versuchs-Stations -

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https://catalog.hathitrust.org/Record/000549685

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Full view of research station reports 1859-1920. In German.

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D. F. Andrews and A. M. Herzberg (1985). Data. -

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https://www2.stat.duke.edu/courses/Spring01/sta114/data/andrews.html

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Table  2.1: agridat::darwin.maize 
-Table  5.1: agridat::broadbalk.wheat 
-Table  6.1: agridat::mercer.wheat.uniformity 
-Table  6.2: agridat::wiebe.wheat.uniformity 
-Table 58.1: agridat::caribbean.maize
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D. Bayisa (2010). Application of Spatial Mixed Model in Agricultural Field Experiment. -

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Master thesis. Department of Statistics, Addis Ababa University. One dataset from wheat, RCB, with field coordinates.

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M. N. Das & Narayan C. Giri (1987). Design and Analysis of Experiments. -

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 31 wool from 24 ewes, 6 cuttings
-116 grass NPK factorial, 3 years, 36 obs
-116 2^5 factorial, 1 rep, 32 obs
-117 2^3 factorial, 3 rep
-117 sugar beet 3^3 factorial, 2 rep, 54 obs
-139 alfalfa 3x2^2 factorial
-149 cabbage NPK split-plot, xy, 2 rep, 108 obs
-150 soybean nitro-variety split-plot
-193 wheat variety inc block, 9 block
-201 rice variety balanced lattice, 80 obs
-279 maize covariate, yield & plant count, 4 rep, 32 obs
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Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger. Analysis of Longitudinal Data. -

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https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html

-

Pig weight data is found in SemiPar::pig.weights

-

Sitka spruce data is found in: geepack::spruce

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Milk protein data is found in: nlme::Milk. A thorough description of this data can be found in Molenberghs & Kenward, Missing Data in Clinical Studies, p. 377. Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments.

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Federer, Walt (1955). Experimental Design. -

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192 3x3 factorial 
-204 3x2 factorial 
-236 2x2x2 factorial with confounding 
-257 2x3x2 factorial with confounding 
-276 split-plot with layout 
-285 nested multi-loc (Also problems page 22) 
-350 cubic lattice 
-420 balanced inc block 
-491 Latin square with covariate
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Finney 1972. An Introduction to Statistical Science in Agriculture. -

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Small, mostly simulated data.

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Galwey, N.W. (2014). Introduction to Mixed Modelling, 2nd ed. -

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https://www.wiley.com/en-us/Introduction+to+Mixed+Modelling%3A+Beyond+Regression+and+Analysis+of+Variance%2C+2nd+Edition-p-9781119945499

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 2  83 variety x nitro split-plot - agridat::yates.oats
- 3 104 doubled-haploid barley
- 3 135 wheat/rye competition, heritability
- 5 190 chickpea flowering in families
- 7 250 canola oil gxe, sowing date, rainfall, oil.  Si & Walton 2004.
- 7 284 pig growth, 4 diets
- 7 285 sheep milk fat and lactose
- 7 290 wheat anoxia root porosity, 9 gen
- 7 291 wool fibers, 3 trt, 21 animals
- 9 370 alphalpha design (row-column inc block), xy
-10 434 hollamby wheat trial, xy - agridat::gilmour.serpentine
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Grover, Deepak & Lajpat Rai. Experimental Designing And Data Analysis In Agriculture And Biology. -

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Agrotech Publishing Academy, 2010. https://archive.org/details/expldesnanddatanalinagblg00023

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43 Percent insect survival in 12 rice varieties, 3 reps
-50 CRD
-57 RCBD
-67 Latin Square
-85 Sampling, 4 rep, 9 trt, 4 sub-samples agridat::grover.rcb.subsample
-88 Split-plot, 3 rep, 2 measurements/plot, plant height (unusual subsample example)
-97 Missing plot
-105 Latin square with missing plot
-115 2^2 factorial, 6 block
-118 2^3 factorial, 3 block
-120 Two factor asymmetrical, 5 rep
-140 2^3 fractional factorial, 3 rep
-160 Split-plot (planting date, variety), 3 rep
-168 Strip-plot, 3 rep 
-176 Milk yield with covariate
-188 Multi-year nitrogen treatment
-197 BIBD 13 varieties
-205 Lattice 4 blocks, 3 reps, 16 trt
-226 Augmented BIBD
-236 Group-divisible
-239 PBIB
-241 Augmented group-divisible
-245 Augmented PBIB
-250 6x6 full diallel, 4 rep   agridat::grover.diallel
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O.V.S. Heath. Investigation by experiment. -

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https://archive.org/details/investigationbye0000heat

-
?  uniformity trial of raddish - agridat::heath.raddish.uniformity
-50 uniformity trial of cabbage 6x8 plots
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Kwanchai A. Gomez & Gomez (1984). Statistical Procedures for Agricultural Research. -

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Extensive collection of datasets from rice experiments. Many added to agridat.

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Cyril H. Goulden, Methods of Statistical Analysis. -

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First edition: https://archive.org/details/methodsofstatist031744mbp

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 18 Uniformity trial - agridat::goulden.barley.uniformity 
-153 Split-split plot with factorial sub-plot treatment - agridat::goulden.splitsplit 
-194 Incomplete block 
-197 Inc block 
-205 Latin square 
-208 Inc block 
-255 Covariates in feeding trial - agridat::crampton.pig
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Second edition: http://krishikosh.egranth.ac.in/handle/1/2034118 (broken)

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216 Latin square - agridat::goulden.latin 
-423 Control chart with egg weights - agridat::goulden.eggs
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Harry Love (1936). Applications of Statistical Methods to Agricultural Research. -

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379 MET 4 year, 2 field, 5 block, 5 gen
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Kang, Manjit (2003). Handbook of Formulas and Software for Plant Geneticists and Breeders -

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Kuehl, Robert. Design of Experiments, 2nd ed. -

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357 alfalfa quadruple lattice
-358 alpha design
-488 split-plot sorghum hybrid,density
-516 alfalfa rcb, two-year
-521 crossover design cattle feedstuff
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Erwin LeClerg, Warren Leonard, Andrew Clark (1962). Field Plot Technique -

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Many small datasets.

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 27 uniformity - agridat::goulden.barley.uniformity
-213 split-plot
-234 immer multi-environment
-260 lattice pinto-bean
-276 triple lattice cotton
-280 lattice sugar beet
-289 balanced lattice
-336 repeated wheat
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Thomas M Little & F. Jackson Hills (1978). Agricultural Experimentation. -

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 79 Latin square 
- 89 Split-plot 
-103 Split-split 
-117 Split-block - agridat::little.splitblock 
-126 Repeated harvests
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Harald Martens & Magni Martens. Multivariate Analysis of Quality -

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https://www.wiley.com/legacy/wileychi/chemometrics/datasets.html

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The ‘NIR’ data has NIR spectra measurements of wheat for the purpose of understanding protein quality.

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Roger Mead, Robert N. Curnow, Anne M. Hasted (2002). Statistical Methods in Agriculture and Experimental Biology, 3rd ed. -

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 10 weekly milk yields
- 24 carrot weight
- 96 cabbage fertilizer
-143 intercropping cowpea maize
-177 honeybee repellent non-normal
-251 cauliflower poisson - agridat::mead.cauliflower
-273 rhubarb RCB covariate
-296 onion density
-316 lambs
-341 germination
-350 germination factorial - agridat::mead.germination
-352 poppy
-359 lamb loglinear - agridat::mead.lambs
-375 rats
-386 intercrop
-390 intercrop cowpea maize - agridat::mead.cowpeamaize
-404 apple characteristics (incomplete)
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Roger Mead (1988). The Design of Experiments -

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https://books.google.com/books?id=CaFZPbCllrMC&pg=PA323

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323 Turnip spacing data - agridat::mead.turnip
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Leonard C. Onyiah (2008). -

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Design and Analysis of Experiments: Classical and Regression Approaches with SAS. https://books.google.com/books?id=_P3LBQAAQBAJ&pg=PA334

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334 Two examples of 5x5 Graeco-Latin squares in cassava and maize
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Bernard Ostle (1963). Statistics in Research, 2nd ed. -

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https://archive.org/details/secondeditionsta001000mbp

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455 2 factors, 1 covariate - agridat::woodman.pig
-458 1 factor, 2 covariates - agridat::crampton.pig
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V. G. Panse and P. V. Sukhatme (1957). Statistical Methods for Agricultural Workers. -

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  3 Length and number of grains per ear of wheat
-138 Uniformity trial - agridat::panse.cotton.uniformity
-154 RCB 8 blocks
-167 two factorial, 6 rep trial
-178 2^4 factorial, 8 blocks, partial confounding
-192 3^3 factorial, 3 reps/9 blocks, partial confounding
-200 split-plot, 6 rep
-212 strip-plot, 6 rep
-219 cotton variety trial, yield & stand counts
-256 8x8 simpple lattice, 4 reps
-282 5 varieties at 6 locations
-295 5 N levels at 5 locations
-332 4 regions, 9 villages each, 3 treatments
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D. D. Paterson (1939). Statistical Technique in Agricultural Research. -

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https://archive.org/details/statisticaltechn031729mbp

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 84 Distribution of purple/white starchy/sweet seeds from 11 ears
-190 Sugar cane MET: 2 year, 5 block, 5 variety
-199 Tea MET: 3 year, 2^2 factorial fertilizer
-206 Grass: 4 rep, 2 gen, 4 cutting treatments
-211 Cotton: 4 dates, 3 spacings, 3 irrigation, 2 nitro - agridat::gregory.cotton
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Roger Petersen, Agricultural Field Experiments -

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8 Uniformity trial 18 * 6 plots
-56 RCB 4 rep, 5 trt
-71 Latin square 5x5
-86 Factorial 4x2, 3 rep
-97 Factorial 2x3x2, 3 rep
-125 Fertilizer trial, 3 rep, 5 levels
-136 Split plot variety x planting date, 3 rep
-148 Strip plot 2 potash x 3 potassium, 3 rep
-170 Augmented breeding trial with 3 checks, 6 inc blocks
-174 Inc Block
-182 Lattice 5x5, 2 rep
-192 GxE 10 gen, 12 env. Stability analysis.
-208 Factorial 2x3 at 8 locs, homogeneous variance, early lentils
-217 GxE 8 gen, 5 loc, heterogeneous variance
-232 Factorial 2x3 at 8 locs, late lentils (see also page 208)
-249 On-farm trial, 24 entries, 3 rep RCB
-257 Demonstration trials, 5 locs
-272 Covariance example, RCB 6 rep, 4rt
-278 Multi-year 2x2 factorial, 4 rep
-309 Pasture trial
-323 On-farm trial, 2 variety 8 loc
-327 On-farm trial 6 trt, 5 loc
-334 On-farm trial 4 trt, 6 loc
-343 On-farm trial 2x3 factorial, 3 loc
-351 Feeding trial, 2 trt, 2 periods
-357 Intercrop, 2 crops
-372 Intercrop, 2 crop, 4 mixtures, 4 rep. agridat::petersen.sorghum.cowpea
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Richard Plant, Spatial Data Analysis in Ecology and Agriculture using R -

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https://psfaculty.plantsciences.ucdavis.edu/plant/

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Arthur Asquith Rayner (1969). A First Course In Biometry For Agriculture Students. -

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19 456 2x2x4 Factorial, 2 rep
-19 466 2x4 factorial, layout, plot size, kale (from Rothamsted)
-19 466 3x5 factorial, 3 rep, potato
-20 494 3x4 Split-plot with layout
-21 505 2x2x2 Factorial, 5 rep
-21 515 2x2x2x2 Factorial, 3 rep, with layout. (Evaluated, rejected as too variable)
-22 537 2x2x2 factorial, 6 rep, potato
-22 537 2x2x2x2 factorial, 2 rep, wheat, layout
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F.S.F. Shaw (1936). A Handbook of Statistics For Use in Plant Breeding and Agricultural Problems -

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https://archive.org/details/in.ernet.dli.2015.176662

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5 - Length of ear head and number of grains per ear, 400 ears.
-95 - variety RCB, 5 gen, 25 rep, diagonal layout
-107 - Latin square, 8 entries.
-117 - Factorial: 8 blocks, 3 varieties, 5 treatments, 2 infections
-126 - Multi-environment trial, 3 year, 13 varieties, 2 loc, 5 blocks  agridat::shaw.oats
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G. W. Snedecor & W. G. Cochran. Statistical Methods. -

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168 regression
-352 3x3 factorial, 4 blocks
-359 2x2x2 factorial, 8 blocks, daily pig gain
-362 2x3x4 factorial, 2 blocks, daily pig gain
-371 3x4 split-plot, 3 var, 4 date, 6 blocks
-374 2x3x3 split-split-plot, irrig, stand, fert, block
-378 4x4 split-plot, 4 block, 4 year, 4 cuttings asparagus
-384 regression with 2 predictors
-428 covariates, 6 varieties, 4 blocks, yield vs stand
-440 pig gain vs initial weight, 4 treatments, 40 pigs
-454 protein vs yield for wheat, 91 plots, quadratic regression
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Robert G. D. Steel & James Hiram Torrie. Principles and Procedures of Statistics, 2nd ed. -

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154 Mint plant growth, 2-way + pot + plant
-244 Trivariate data
-319 Regression with three predictors
-384 Split-plot yield
-387 Split-plot row spacing
-400 Soybean 3 loc
-423 Pig weight gain
-429 Guinea pig weight gain
-434 Soybean lodging
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Oliver Schabenberger and Francis J. Pierce. Contemporary Statistical Models for the Plant and Soil Sciences. -

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Many datasets. Some added to agridat.

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S. J. Welham et al. (2015). Statistical Methods In Biology. -

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The online-supplements contain many small datasets for the examples and exercises.

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Pesticides in the Nation’s Streams and Ground Water, 1992-2001 -

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Extensive data for detection of pesticides in water samples. See Appendix 5 and Appendix 6 of the supporting info. https://water.usgs.gov/nawqa/pnsp/pubs/circ1291/supporting_info.php

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Data Repositories -

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Ag Data Commons -

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https://data.nal.usda.gov/about-ag-data-commons https://data.nal.usda.gov/search/type/dataset

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CyVerse Data Commons -

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https://datacommons.cyverse.org/ https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated

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DataDryad -

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Harvard Dataverse -

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https://dataverse.harvard.edu/

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IRRI Rice Research includes plot-level data for long term rice experiments. https://dataverse.harvard.edu/dataverse/RiceResearch

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Nature Scientific Data -

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https://www.nature.com/sdata/

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Open Data Journal for Agricultural Research -

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https://library.wur.nl/ojs/index.php/odjar/

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Plant Genomics and Phenomics Research Data Repository -

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Wolfram Data Repository -

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https://datarepository.wolframcloud.com/

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Journals - Bulletins -

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Iowa State Agricultural Research Bulletins -

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https://lib.dr.iastate.edu/ag_researchbulletins/

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Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice.
-Page 11: Lattice, 81 hybs, 4 reps 
-Page 24: Triple lattice, 81 hybs, 6 reps
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-Vol 29/347. Homeyer. Punched Card and Calculating Machine Methods for Analyzing 
-  Lattice Experiments Including Lattice Squares and the Cubic Lattice.
-Page 37: Triple lattice (9 blocks * 9 hybrids) with 6 reps.
-Page 60: Simple lattice, 8 blocks * 8 hybrids, 4 reps.
-Page 76: Balanced lattice, 25 hybrids 
-Page 87: Lattice square with (k+1)/2 reps, 121 hybrids, 6 rep 
-Page 109: Lattice square with k+1 reps, 7 blocks * 7 hyb, 8 reps 
-Page 126: Cubic lattice, 16 blocks * 4 plots = 64 varieties, 9 reps, cotton
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-Vol 32/396. Wassom. Bromegrass Uniformity Trial: agridat::wassom.bromegrass.uniformity
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-Vol 33/424. Heady. Crop Response Surfaces and Economic Optima in 
-  Fertilizer - agridat::heady.fertilizer
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-Vol 34/358. Schwab. Research on Irrigation of Corn and Soybeans At Conesville.
-Page 257. 2 year, 2 loc, 4 rep, 2 nitro. Stand & yield.
-  Nice graph of soil moisture deficit (fig 9)
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-Vol. 34/463. Doll. Fertilizer Production Functions for Corn and Oats.
-Table 1, 1954 Clarion Loam.  N,P,K.
-Table 14, 1955 McPaul Silt Loam.  N,P.
-Table 25, 1955 corn.  K,P,N.
-Table 31, 1956 oats, K,P,N.  Trends difficult to establish.
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-Vol 34/472. Pesek. Production Surfaces and Economic Optima For Corn Yields.
-  Same data published in SSA journal?
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-Vol 34/488. Walker. Application of Game Theory Models to Decisions.
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-Vol 35/494. North Central Regional Potassium Studies with Alfalfa.
-Page 176. Two years, several locs per state, multiple states, multiple fertilizer 
-  levels, multiple cuttings. Soil test attributes.
-Page 183. Yield and %K.
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-Vol 35/503. North Central Regional Potassium Studies with Corn.
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Papers -

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Xavier, Alencar et al.. Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population. https://doi.org/10.1534/g3.117.300300

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Data are in the SoyNAM and NAM packages.

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Cleveland, M.A. and John M. Hickey, Selma Forni (2012). A Common Dataset for Genomic Analysis of Livestock Populations. G3, 2, 429-435. https://doi.org/10.1534/g3.111.001453

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The supplemental information for this paper contains data for 3534 pigs with high-density genotypes (50000 SNPs), and a pedigree including parents and grandparents of the animals.

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Daillant-Spinnler (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere. Food Quality and Preference, 7(2), 113-126. https://doi.org/10.1016/0950-3293(95)00043-7

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The data are in ClustVarLV::apples_sh$pref and ClustVarLV::apples_sh$senso 12 apple varieties, 43 traits, 60 consumers

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Gregory, Crowther & Lambert (1932). The interrelation of factors controlling the production of cotton under irrigation in the Sudan. Jour Agric Sci, 22, p. 617.

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Hedrick (1920). Twenty years of fertilizers in an apple orchard. https://books.google.com/books?hl=en&lr=&id=SqlJAAAAMAAJ&oi=fnd&pg=PA446

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The authors found no significant differences between fertilizer treatments.

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Meehan & Gratton (2016). A Landscape View of Agricultural Insecticide Use across the Conterminous US from 1997 through 2012. PLOS ONE, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166724

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Supplemental material contains county-level data for each of 4 years. Complete R-INLA code for analysis.

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Monteverde et al Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas https://doi.org/10.1534/g3.119.400064 https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636

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Supplemental information contains phenotypic data and markers and environmental covariates for PLS analysis.

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Roger W. Hexem, Earl O.Heady, Metin Caglar (1974) A compendium of experimental data for corn, wheat, cotton and sugar beets grown at selected sites in the western United States and alternative production functions fitted to these data. Technical report: Center for Agricultural and Rural Development, Iowa State University. https://babel.hathitrust.org/cgi/pt?id=wu.89031116783;view=1up;seq=3

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The technical report provides data from experiments on corn, wheat, cotton & sugar beets, each crop tested at several locations over two years, with a factorial structure on irrigation and nitrogen treatments, with replications. Three polynomial functions were fit to the data for each location (quadratic, square root, three-halves).

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Kenward, Michael G. (1987). A Method for Comparing Profiles of Repeated Measurements. Applied Statistics, 36, 296-308.

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An ante-dependence model is fit to repeated measures of cattle weight.

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Klumper & Qaim (2015). A Meta-Analysis of the Impacts of Genetically Modified Crops. https://doi.org/10.1371/journal.pone.0111629

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Nice meta-analysis dataset. Published data only include differences, not standard-errors. See the comments on PLOS article for some peculiarities in the data.

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Lado, B. et al. (2013). Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data. G3, 3, 2105-2114. https://doi.org/10.1534/g3.113.007807

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Has a large haplotype dataset (83 MB) and two-year phenotype data with multiple traits.

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Payne, Roger (2015). The Design and Analysis of Long-Term Rotation Experiments. Agronomy Journal, 107, 772-784. https://doi.org/10.2134/agronj2012.0411

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The data and R code appeared in the paper. Free access, but closed copyright.

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Peixoto, Marco Antonio et al (2020) Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244021

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Repeated measurements over six years. Data in supplemental Word doc.

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Snedecor, George and E. S. Haber (1946). Statistical Methods For an Incomplete Experiment on a Perennial Crop. Biometrics Bulletin, 2, 61-67. https://www.jstor.org/stable/3001959

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Harvest of asparagus over 10 years, three cutting dates per year, 6 blocks.

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Tanaka, Takashi X. T. Assessment of design and analysis frameworks for on-farm experimentation through a simulation study of wheat yield in Japan. https://github.com/takashit754/geostat

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Yield-monitor data for 3 fields.

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Technow, Frank, et al. (2014). Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize. August 1, 2014 vol. 197 no. 4 1343-1355. https://doi.org/10.1534/genetics.114.165860

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Genotype and phenotype data appears in the sommer package.

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Tian, Ting (2015). Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. https://doi.org/10.1371/journal.pone.0144370

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Uses agridat::australia.soybean data and one other real dataset with 4 traits that are not identified. All data and code available.

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Randall J. Wisser et al. (2011). Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS. https://doi.org/10.1073/pnas.1011739108

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The supplement contains genotype data, but no phenotype data.

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Rife et al. (2018) Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery. https://doi.org/10.5061/dryad.q968v83

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Large phenotypic dataset with 691 wheat lines, 33 years, 670 environments, 3-4 reps, 120000 datapoints. No genotypic data is included.

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van der Voet et al. (2017). Equivalence testing using existing reference data: An example with genetically modified and conventional crops in animal feeding studies. https://doi.org/10.1016/j.fct.2017.09.044

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The full datasets for the GRACE studies A-E are available here: https://www.cadima.info/index.php/area/publicAnimalFeedingTrials CC license.

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Yan, Weikei (2002). Singular value partitioning in biplots. Agron Journal.

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Winter wheat, 31 gen in 8 loc. This data is different from Yan’s earlier papers. Unfortunately, the data given in the paper are missing two rows.

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R packages on CRAN, Github, etc. -

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agriCensData -

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https://github.com/OnofriAndreaPG/agriCensData

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Three datasets with censored observations for the paper “Analyzing interval-censored data in agricultural research: A review with examples and software tips”.

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agriTutorial -

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https://myaseen208.github.io/agriTutorial/

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Five datasets used to illustrate analyses.

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agricolae -

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Has assorted data and functions for analysis of agricultural data.

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agroBioData -

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https://github.com/OnofriAndreaPG/agroBioData

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Datasets for agriculture and applied biology. Referenced by this blog: https://www.statforbiology.com/

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aml - Adaptive Mixed LASSO -

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Data aml::wheat genetic and phenotypic data for wheat. Modest size.

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BGLR - Bayesian Generalized Linear Regression. -

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Has an A matrix (but no pedigree) for 499 genotypes at 4 locations.

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BLR - Bayesian Linear Regression. -

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Has an A matrix (but no pedigree) for 499 genotypes at 4 locations.

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BSagri -

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Safety assessment in agriculture trials

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ClustVarLV -

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Data apples_sh sensory attributes and preference scores for 12 apple varieties.

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cropcc - Climate change on crops -

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https://r-forge.r-project.org/projects/cropcc/

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drc - Dose response curves -

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Has nice herbicide dose response curves and germination data for mungbean, rice, wheat.

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epiphy -

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https://github.com/chgigot/epiphy

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Contains 10 historical datasets for plant disease epidemics.

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FW - Finlay-Wilkinson regression -

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https://github.com/lian0090/FW/

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Has phenotype data and marker data for 599 wheat lines in 4 environments.

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ggenealogy -

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https://doi.org/10.18637/jss.v089.i13

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Data sbGeneal contains a soybean pedigree with 230 varieties.

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gRbase -

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Data gRbase::carcass: thickness of meat and fat on slaughter pigs

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lmDiallel -

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https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data

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lmtest -

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Data lmtest::ChickEgg time series of annual chicken and egg production in the United States 1930-1983.

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NADA -

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Data Atra and Recon contain measurements of Atrazine in water samples.

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nlraa -

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Miguez. Non-linear models in agriculture. nlraa::sm = agridat::miguez.biomass Vignettes and functions for working with (non)linear mixed models

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nlme -

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nlme::Orange: Growth of orange trees

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nlme::Soybean: Growth of soybean plants. From the book “Nonlinear Models for Repeated Measurement Data”.

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pbkrtest -

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pbkrtest::beets Yield and percent sugar in split-plot experiment.

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plantbreeding -

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https://r-forge.r-project.org/projects/plantbreeding/

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Data: fulldial
-Data: linetester
-Data: peanut same as agridat::kang.peanut
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SDaA - Survey Data and Analysis -

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This package has county-level data from the United States Census of Agriculture, along with a vignette to illustrate survey sampling analyses.

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SemiPar -

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Data: SemiPar::onions is same as agridat::ratkowski.onions

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soilDB -

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https://ncss-tech.github.io/AQP/soilDB/soilDB-Intro.html Soil database interface.

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sommer - Solving mixed model equations in R -

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Data: h2. Modest-sized GxE experiment in potato Data: cornHybrid. Yield/PLTHT for 100 hybrids from 20 inbred * 20 inbred, 4 locs. Phenotype and relationship matrix.

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Data:

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data(DT_wheat) #  CIMMYT wheat data
-DT_wheat # 599 varieties, yield in 4 envts
-GT_wheat # 599 varieties, 1279 markers coded -1,1
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Data: RICE

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Data: FDdata taken from agridat::bond.diallel

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Data:

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data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental
-DT <- DT_technow  # 1254 hybs, parents, GY=yield, GM=moisture
-Md <- Md_technow  # 123 dent parents, 35478 markers
-Mf <- Mf_technow  # 86 flint parents, 37478 markers
-Ad <- Ad_technow  # 123 x 123 A matrix 
-Af <- Af_technow  # 86 x 85 A matrix
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SoyNAM - Soybean nested association mapping -

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Dataset with phenotype data 3 yr, 9 locations, 18 environments, 60 thousand observations for height, maturity, lodging, moisture, protein, oil, fiber, seed size. There are 5000+ strains, 40 families.

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Data formatted for the analysis of the NAM package is available with the following command: SoyNAM::ENV().

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spdep -

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Has a vignette ‘The Problem of Spatial Autocorrelation: forty years on’ that examines agriculture in Irish counties. See also the data ade4::irishdata.

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spuRs -

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Data: spuRs::trees has data for 107 trees that were cut into cross sections with the volume calculated at roughly 10-year increments. This is a subset of the much-larger original data from Guttenberg: https://archive.org/stream/wachstumundertra00gutt

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StatForBiology -

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https://www.statforbiology.com/

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Blog posts with example analyses.

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statgenSTA -

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https://CRAN.R-project.org/package=statgenSTA See vignette: Modeling field trials using statgenSTA

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statgenGWAS -

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https://CRAN.R-project.org/package=statgenGWAS

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Includes a worked example with data from: https://data.inra.fr/dataset.xhtml?persistentId=doi:10.15454/IASSTN

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st4gi - Stat for genetic improvement -

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https://github.com/reyzaguirre/st4gi

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Web sites -

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ARS oat trials -

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https://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4

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CIMMYT Research Data -

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https://data.cimmyt.org/dataverse/cimmytdatadvn

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Genomes To Fields (G2F) -

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https://www.genomes2fields.org/publications/

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https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3

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Very large GxE data here for 2014 and 2015. Hybrid & inbred phenotype data, weather data, genomic data. Very nice.

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GLTEN Global Long Term Metadal Portal -

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https://glten.org/

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Links to long-term experiments.

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https://datasetsearch.research.google.com/

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Grain genes -

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  1. https://wheat.pw.usda.gov/ggpages/HxT/ The Harrington x TR306 Barley Mapping Population. The genotype and phenotype data comes from Mapmaker, but seems to be in a slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep.

    2. -

  2. https://wheat.pw.usda.gov/ggpages/SxM/ . This data is agridat::steptoe.morex.

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Ideals -

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https://www.ideals.illinois.edu/handle/2142/3528 Data File : Raw data from each ear analyzed each year of the Illinois long-term selection experiment for oil and protein in corn (1896-2004)

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ILRI International Livestock Research Institute -

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Case study 4 is a nice diallel example with sheep data. Available as agridat::ilri.sheep

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IRRI Biometrics and Breeding Informatics -

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http://bbi.irri.org/products

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STAR, PBTools, CropStat. The STAR user guide has well-documented data (even using 2 from agridat), but the PBTools user guide does not document the data.

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Rothamsted Electronic Archive -

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http://www.era.rothamsted.ac.uk/index.php Data from Broadbalk and other long-term experiments.

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Github draft data: https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts

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Rothamsted Documents Archive -

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http://www.era.rothamsted.ac.uk/eradoc/collections.php

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Annual reports from Rothamsted 1908-1987. Many have data, especially in the early years (before WWII) there are data given for the ‘Classical Experiments’.

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Year, page
-1908-1926 
-1926-1927 agridat::sawyer.multi.uniformity 
-1927-1928 agridat::sawyer.multi.uniformity
-1929-1930
-1931,143 agridat::yates.oats
-1932
-1933
-1934,215-222 Sugar beet multi-environment trial with 3^3 fertilizer 
-  treatments at each site Roots, SugarPercent, SugarWeight, PlantNumber, Tops, Purity.
-1935
-1936,241 Similar to the 1934 experiment, but only gives the main effects, 
-  not the actual data.
-1937-1939
-1946-1953
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Statistical Analysis of Agricultural Experiments with R -

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rstats4ag.org (no http included here because of firewall problems).

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Datasets for mixed models, ancova, dose response curves, competition.

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Syngenta Crop Challenge -

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https://www.ideaconnection.com/syngenta-crop-challenge/

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Annual Kaggle-style competition sponsored by Syngenta.

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Terra-Ref -

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https://terraref.org/

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Sensor observations, plant phenotypes, derived traits, genetic and genomic data. Beta version until Nov 2018.

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USDA National Agricultural Statistics Service -

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https://www.nass.usda.gov https://quickstats.nass.usda.gov/

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Group: Field Crops Commodity: Corn Category: Area Harvested, Yield Data Item: Corn grain Acres Harvested, Yield Bu/Ac Domain: Total Geography: State See agridat::nass.corn, nass.wheat, etc.

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Other -

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Rothamsted Library -

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https://www.rothamsted.ac.uk/library-and-information-services

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Archive catalog.

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Box of uniformity trial data

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STATS17  WG Cochran
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-Uniformity trial data. 
-Genstat data. Data received since publication of the catalogue.  1935-1943. 
-Uniformity trial data.  1930-1936. 
-Uniformity trials.  1936-1938. 
-Uniformity trials.  R data.  1936-1937. 
-O. V. S. Heath.  Cotton uniformity trial data.  1934-1935. 
-Data.  Yields of grain per foot length.  1934. 
-Catalogue of field uniformity trial data.  N. d. 
-Demandt.  1931. One box
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- - - - - - - - diff --git a/docs/articles/agridat_data_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/agridat_data_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/articles/agridat_data_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/agridat_data_files/header-attrs-2.4/header-attrs.js b/docs/articles/agridat_data_files/header-attrs-2.4/header-attrs.js deleted file mode 100644 index dd57d92e..00000000 --- a/docs/articles/agridat_data_files/header-attrs-2.4/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/agridat_examples.html b/docs/articles/agridat_examples.html deleted file mode 100644 index a8211dbf..00000000 --- a/docs/articles/agridat_examples.html +++ /dev/null @@ -1,304 +0,0 @@ - - - - - - - -Graphical Gems in the agridat Package • agridat - - - - - - - - - - - - - - - - - - - -
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- - - Source: ../../../Users/wrightkevi/OneDrive/kw_work/rpack/agridat/vignettes/agridat_examples.Rmd - - -
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title: “Graphical Gems in the agridat Package”
author: “Kevin Wright”
date: “2021-12-17”
output:
rmarkdown::html_vignette:
md_extensions: [
“-autolink_bare_uris”
]
bibliography: agridat.bib
vignette: >
%
%
%
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The agridat package is an extensive collection of data sets that have been previously published in books and journals, primarily from agricultural experiments.

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This vignette presents graphical views of a few of the datasets in this package.

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Setup -

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This exhibit of agricultural data uses the following packages: agridat, desplot, gge, HH, lattice, latticeExtra, mapproj, maps, reshape2.

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Potato blight incidence over space and time -

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## Loading required package: desplot
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(lee2009random?) analyzed a large dataset to evaluate the resistance of potato varieties to blight. This data contains evaluations of a changing set of varieties every two years, evaluated in 5 blocks, repeatedly throughout the growing season to track the progress of the disease. Each panel shows a field map on the given date, with a separate row of panels for each year.

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Would you include field spatial trends in a model for these data?

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## Loading required package: latticeExtra
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## Loading required package: lattice
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In 1983, 20 varieties were evaluated in 5 blocks (shown by colored numbers) throughout the growing season for disease resistance. Resistance scores start at 9 for all varieties (shown in panels). As the growing season progresses, the ‘I.HARDY’ variety succumbs quickly to blight, while ‘IWA’ succumbs steadily, and ‘064.1’ resists blight until near the end of the season.

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Does this view show differences between blocks?

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An informative prior -

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(harrison2012bayesian?) used a Bayesian approach to model daidzein levels in soybean samples. From 18 previous publications, they extracted the published minimum and maximum daidzein levels, and the number of samples tested. Each line in the dotplot shows large, dark dots for one published minimum and maximum. The small dots are imputed using a lognormal distribution.

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All observed/imputed data were then used to fit a common lognormal distribution that can be used as an informative prior. The common prior is shown by the density at the top of the dotplot.

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Do you think it is better to use a non-informative prior, or this informative prior?

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Data densities for a binomial GLM -

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(mead2002statistical?) present data for germination of seeds under four temperatures (T1-T4) and four chemical concentrations. For each of the 4*4=16 treatments, 50 seeds were tested in each of four reps. In the graphic, each point is one rep. The blue line is a fitted curve from a GLM with Temperature as a factor and log concentration as a covariate. The gray lines show the central 95 percent of the binomial density at that position.

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Does this display help you understand the logit link and changing shape of the binomial density?

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Verification of experiment layout -

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(gomez1984statistical?) provide data for an experiment with 3 reps, 6 genotypes, 3 levels of nitrogen and 2 planting dates. The experiment layout is putatively a ‘’split strip-plot’’. To verify the design, the desplot package is used for plotting the design of field experiments.

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How is the design different from a ‘’split-split-plot’’ design?

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Visualizing main effects, two-way interactions -

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## Loading required package: HH
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## Loading required package: grid
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## Loading required package: multcomp
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## Loading required package: mvtnorm
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## Loading required package: survival
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## Loading required package: TH.data
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## Loading required package: MASS
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## 
-## Attaching package: 'TH.data'
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## The following object is masked from 'package:MASS':
-## 
-##     geyser
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## Loading required package: gridExtra
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(heiberger2004statistical?) provide an interesting way to use lattice graphics to visualize the main effects (using boxplots) and interactions (using interaction plots) in data. Rice yield is plotted versus replication, nitrogen, management type, and genotype variety. Box plots show minor differences between reps, increaing yield due to nitrogen, high yield from intensive management, and large differences between varieties.

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Do you think interaction plots show interaction (lack of parallelism)?

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3D yield response to fertilizers -

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Note: This image was created by manual manipulation of an rgl device. The manual manipulation makes this non-reproducible in an Rmd file. See the example for the sinclair.clover data for the code.

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(sinclair1994sulphur?) examined clover yields as a function of sulfur and phosphorous fertilizer in a factorial-treatment experiment. (dodds1996bivariate?) modeled the yield response using a Mitzerlisch-like equation that allows interacting curvature in two dimensions \(x\) and \(y\): \[ -yield = \alpha * \left( 1 + \beta *\left(\frac{\sigma + \tau*x}{x+1}\right)^y\right) * -\left( 1+\delta*\left(\frac{\theta + \rho *y}{y+1}\right)^x \right) -\]

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The blue dots are observed data, and the tan surface is the fitted surface drawn by the rgl package).

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How would you decide the optimal fertilizer levels?

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Mosaic plot of potato damage from harvesting -

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(keen1997analysis?) looked at damage to potatoes caused by lifting rods during harvest. In this experiment, eight types of lifting rods were compared. Two energy levels, six genotypes and three weight classes were used. For each combinations of treatments, about 20 potato tubers were rated as undamaged (D1, yellow) to severely damaged (D4, red). Counts per treatment are shown in a mosaic plot.

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Which style of lifting rods cause the least/most damage to potatoes?

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Yield vs covariate for lattice::barley -

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(wright2013revisiting?) investigated the lattice::barley data. The original two years of data were extended to 10 years (from original source documents), and supplemented with weather covariates for the 6 locations and 10 years. Each panel shows a scatterplot and regression for average location yield verses the weather covariate. Horizontal strips are for locations, vertical strips are for covariates: cdd = Cooling Degree Days, hdd = Heating Degree Days, precip = Precipitation). Higher values of heating imply cooler weather. Each plotting symbol is the last digit of the year (1927-1936) for that location.

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Does barley yield better in cooler or warmer weather?

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GGE biplot -

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(laffont2013genotype?) developed a variation of the GGE (genotype plus genotype-by-environment) biplot to include auxiliary information about a block/group of environments. Each location is classified into one of two mega-environments (colored). The mosaic plots partition variation simultaneously by principal component axis and source (genotype, genotype-by-block, residual).

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Which genotypes are best to each mega-environment?

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Nebraska farming income choropleth -

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## Loading required package: maps
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## Loading required package: mapproj
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The Red-Blue palette in the RColorBrewer package is a divergent palette with light colors near the middle of the scale. This can cause problems when there are missing values, which appear as white (technically, the background). In order to increase the visibility of missing values, the agridat package uses a Red-Gray-Blue palette, with a gray color that is dark enough to clearly distinguish missing values.

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How does the outlier county (Butler) in northeast Nebraska limit interpration of spatial patterns in the data?

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Because counties are different sizes, the second graphic uses an income rate per square mile. Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier. Instead, the break points are calculated using a method called Fisher-Jenks. These break points show both the outlier and the spatial patterns. It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops. Counties with missing data are white, which is easily distinguished from gray.

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Where are farm incomes highest? Why?

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Las Rosas yield monitor -

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(anselin2004spatial?) and (lambert2004comparison?) looked at yield monitor data collected from a corn field in Argentina in 1999 and 2001, to see how yield was affected by field topography and nitrogen fertilizer. The figures here show heatmaps for the yield each year, and also the experiment design (colors are reps, shades of color are nitrogen level, plotting character is topography).

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Which year showed greater spatial variation in yield?

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Time series of corn yields by state -

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The National Agricultural Statistics Service tracks the total number of acres planted to corn (and other crops) for each state in the U.S. There are large changes over the past century in corn acreage for selected states.

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Which states were in the corn belt in 1925?

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Which states were in the corn belt in 2000?

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References -

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We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/agridat_intro.html b/docs/articles/agridat_intro.html deleted file mode 100644 index 90f1551b..00000000 --- a/docs/articles/agridat_intro.html +++ /dev/null @@ -1,169 +0,0 @@ - - - - - - - -Introduction to `agridat` • agridat - - - - - - - - - - - - - - - - - - - -
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Introduction to agridat -

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Kevin Wright

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2021-12-17

- - Source: ../../../Users/wrightkevi/OneDrive/kw_work/rpack/agridat/vignettes/agridat_intro.Rmd - - -
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For a quick overview of the datasets available in the package, type ?agridat at the R prompt.

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Comments on the package purpose -

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When this project was first begun in the early 2000s, electronic versions of agricultural datasets were hard to find. Since then, there has been a revolution in the availability of datasets related to agriculture. See the vignette which describes some data sources.

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Box (1957) said, “I had hoped that we had seen the end of the obscene tribal habit practiced by statisticians of continually exhuming and massaging dead data sets after their purpose in life has long since been forgotten and there was no possibility of doing anything useful as a result of this treatment.”

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Massaging these dead data sets will not lead to any of the genetics being released for commercial use. The value of this package is: 1. Validating published analyses. 2. Providing data for testing new analysis methods. 3. Illustrating (and validating) the use of R packages.

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White and van Evert (2008) present some guidelines for publication of data.

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Some of the examples use the asreml package since it is the only R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures. Commercial use of asreml requires a license: https://www.vsni.co.uk/downloads/asreml.

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Comments on the package structure -

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Many of these datasets appear in electronic form here for the first time.

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A tremendous amount of effort has been given to the curating process of identifying datasets, extracting the data from source materials, checking data values, and documenting the data. In effect, to make the data somewhat ‘computable’ (Wolfram 2017).

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The original sources for these data use different words to refer to genotypes including accession, breed, cultivar, genotype, hybrid, line, progeny, stock, type, and variety. For consistency, these datasets mostly use gen (genotype).

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Also for consistency, row and col are usually used for the field coordinates.

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In dataframes, block, rep, and similar terms are almost always coded like B1, B2, B3 instead of 1, 2, 3. This causes R to treat the data as a factor instead of a numeric covariate (which is a good thing).

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Almost all of the data are presented as ‘tidy’ dataframes with observations in rows and variables in columns.

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Although using data() is not necessary to access the data files, the example sections do include the use of data() because devtools::run_examples() needs it.

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References -

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G. E. P. Box (1957). Integration of Techniques in Process Development, Transactions of the American Society for Quality Control.

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J. White and Frits van Evert. (2008). Publishing Agronomic Data. Agronomy Journal, 100, 1396-1400. https://doi.org/10.2134/agronj2008.0080F

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Stephen Wolfram (2017). Launching the Wolfram Data Repository: Data Publishing that Really Works. https://writings.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/

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Using Papadakis covariates for nearest neighbor analysis

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Kevin Wright

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2019-09-13

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-R setup

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Papadakis (1937) believed that traditional blocking in field experiments did not adequately represent the patchiness of soil fertility patterns and he instead proposed adjusting the yield of each plot by the performance of the neighboring plots.

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If there is heterogeneity in the field that is of a scale smaller than the block (but larger than the individual plots) then adjacent plots will be positively correlated and this information about the neighboring plots can be used to reduce the effect of spatial heterogeneity and increase the accuracy of the treatment effects.

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The Papadakis method is a nearest neighbor method that uses a residual covariate in the analysis. In essence, the method follows the following steps.

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  1. Fit a treatment model and calculate the residuals from the model.

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  2. Calculate covariates that are the average of the neighboring residuals.

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  3. Fit a model with additional covariate terms for the residuals.

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The left-right (LR) covariate for the (i,j)th plot is the average of the residuals for the plots immediately to the left and right of the (i,j)th plot. If one of these neighbors is missing, then the covariate is constructed from the single remaining neighboring residual. Border plots use only one neighboring residual. The up-down (UD) covariate is similarly constructed from residuals for plots immediately up or down from the (i,j)th plot.

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-Reproduce Hinz 1987 case 2

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Hinz (1987) used the Papadakis technique to analzye a field experiment of tobacco.

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library("agridat")
-data(federer.tobacco)
-dat <- federer.tobacco
-dat <- transform(dat, height=height-600) # For simplicity
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-# Model 1 - RCB
-m1 <- aov(height ~ factor(block) + factor(dose), dat)
-anova(m1)
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## Analysis of Variance Table
-## 
-## Response: height
-##               Df  Sum Sq Mean Sq F value Pr(>F)
-## factor(block)  7  388315   55474  1.8352 0.1056
-## factor(dose)   6  273875   45646  1.5100 0.1985
-## Residuals     42 1269586   30228
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# Model 2 - Row/Col as class variables
-m2 <- aov(height ~ factor(block) + factor(dose) + factor(row), dat)
-anova(m2)
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## Analysis of Variance Table
-## 
-## Response: height
-##               Df  Sum Sq Mean Sq F value    Pr(>F)    
-## factor(block)  7  388315   55474  7.5455 1.355e-05 ***
-## factor(dose)   6  273875   45646  6.2088 0.0001521 ***
-## factor(row)    6 1004920  167487 22.7816 6.767e-11 ***
-## Residuals     36  264666    7352                      
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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# Model 3 - Two-step Papadakis
-m3 <- aov(height ~ factor(dose), dat)
-dat <- cbind(dat, papcov(m3$resid, dat$block, dat$row))
-m4 <- aov(height ~ factor(dose) + LR + UD, data=dat)
-anova(m4)
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## Analysis of Variance Table
-## 
-## Response: height
-##              Df  Sum Sq Mean Sq F value    Pr(>F)    
-## factor(dose)  6  273875   45646  3.6857  0.004407 ** 
-## LR            1 1061352 1061352 85.6998 3.636e-12 ***
-## UD            1   14477   14477  1.1689  0.285136    
-## Residuals    47  582073   12385                      
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## [1] 12653.76
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-Iterated example as given in Stroup et al, Table 2

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Stroup, Baenziger, and Mulitze (1994) used the Papadakis tecnique in an iterative manner.

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library("agridat")
-data(stroup.nin)
-dat2 <- stroup.nin
-dat2 <- subset(dat2,!is.na(yield))
-n.gen <- nlevels(dat2$gen)
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-# RCB model, ranks match Stroup Table 2, RCB Alliance
-m5 <-  lm(yield ~ gen -1 + rep, data=dat2)
-pred.rcb <- coef(m5)[1:n.gen] # RCB adj means
-rev(57-sort(rank(pred.rcb)))
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##    genNE86503    genNE87619    genNE86501    genRedland  genCenturk78    genNE83498 
-##             1             2             3             4             5             6 
-##  genSiouxland    genNE86606   genArapahoe    genNE87613    genNE86607     genLancer 
-##             7             8             9            10            11            12 
-##     genTAM107   genCheyenne    genNE87446  genHomestead    genScout66    genNE83404 
-##            13            14            15            16            17            18 
-##       genColt    genNE86509    genNE87513    genLancota    genNE85556    genNE87408 
-##            19            20            21            22            23            24 
-##      genBrule    genNE87463    genNE87615   genBuckskin    genNE87403    genNE87522 
-##            25            26            27            28            29            30 
-##    genNE87451    genNE86582       genGage     genNorkan    genNE86482    genNE83406 
-##            31            32            33            34            35            36 
-##   genKS831374    genNE87457    genNE86507       genVona    genNE87512    genNE87627 
-##            37            38            39            40            41            42 
-##    genNE83407    genNE86527    genNE87612    genNE85623    genCentura    genNE83T12 
-##            43            44            45            46            47            48 
-##   genNE86T666    genNE87409     genTAM200       genCody genRoughrider    genNE84557 
-##            49            50            51            52            53            54 
-##    genNE87499    genNE83432 
-##            55            56
- -
## Iteration:  1  tol:  51.60518 
-## Iteration:  2  tol:  7.823055 
-## Iteration:  3  tol:  1.445812 
-## Iteration:  4  tol:  0.3619413 
-## Iteration:  5  tol:  0.09555693 
-## Iteration:  6  tol:  0.02840853 
-## Iteration:  7  tol:  0.008783593 
-## Iteration:  8  tol:  0.003297561 
-## Iteration:  9  tol:  0.001650757 
-## Iteration:  10  tol:  0.001157637 
-## Iteration:  11  tol:  0.001000046 
-## Iteration:  12  tol:  0.0009401906
- -
##               rcb nna
-## genNE86503      1  14
-## genNE87619      2   4
-## genNE86501      3  24
-## genRedland      4   6
-## genCenturk78    5  15
-## genNE83498      6   2
-## genSiouxland    7  28
-## genNE86606      8  11
-## genArapahoe     9  18
-## genNE87613     10   9
-## genNE86607     11  16
-## genLancer      12  35
-## genTAM107      13  37
-## genCheyenne    14  21
-## genNE87446     15  46
-## genHomestead   16  44
-## genScout66     17  13
-## genNE83404     18  23
-## genColt        19  17
-## genNE86509     20  42
-## genNE87513     21  45
-## genLancota     22  49
-## genNE85556     23   3
-## genNE87408     24  36
-## genBrule       25   7
-## genNE87463     26  39
-## genNE87615     27  32
-## genBuckskin    28   1
-## genNE87403     29  53
-## genNE87522     30  52
-## genNE87451     31  33
-## genNE86582     32  48
-## genGage        33  29
-## genNorkan      34  51
-## genNE86482     35  25
-## genNE83406     36  19
-## genKS831374    37   5
-## genNE87457     38  34
-## genNE86507     39  10
-## genVona        40  30
-## genNE87512     41  47
-## genNE87627     42  55
-## genNE83407     43  27
-## genNE86527     44   8
-## genNE87612     45  20
-## genNE85623     46  31
-## genCentura     47  26
-## genNE83T12     48  40
-## genNE86T666    49  56
-## genNE87409     50  12
-## genTAM200      51  54
-## genCody        52  43
-## genRoughrider  53  22
-## genNE84557     54  38
-## genNE87499     55  41
-## genNE83432     56  50
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# Visually compare the coefficients from the two methods
-lims=range(c(pred.rcb,pred.pap))
-plot(pred.rcb, pred.pap, xlim=lims, ylim=lims,
-     xlab="RCB",ylab="Papadakis",type='n')
-text(pred.rcb,pred.pap, substring(names(pred.rcb),4),cex=0.5)
-title("Iterated Papadakis vs. RCB")
-abline(0,1)
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The variety ‘Buckskin’ has a large adjustment when using the Papadakis method. This makes sense, because Buckskin ended up in the high-yielding part of each of the four reps.

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Hinz, Paul N. 1987. “Nearest-Neighbor Analysis in Practice.” Iowa State Journal of Research 62: 199–217.

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Papadakis, J S. 1937. “Méthode Statistique Pour Les Expériences En Champ.” Bulletin Institute de L’Ameloration Des Plantes à Salonique 23.

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Stroup, Walter, P Stephen Baenziger, and Dieter K Mulitze. 1994. “Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods.” Crop Science 86: 62–66.

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title: “Notes on uniformity data”
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Todo -

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To do: Figure out appendix II. Mathew, Lucyamma. Standardization of field plot technique for cashew. Diss. Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy. https://krishikosh.egranth.ac.in/handle/1/5810142095

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Contact Rothamsted to scan uniformity trial papers.

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Added to agridat -

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Ansari, M. A. A., and G. K. Sant (1943). A Study of Soil Heterogeneity in Relation to Size and Shape of Plots in a Wheat Field at Raya (Muhra District). Ind. J. Agr. Sci, 13, 652-658. https://archive.org/details/in.ernet.dli.2015.271748

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Baker, GA and Huberty, MR and Veihmeyer, FJ. (1952) A uniformity trial on unirrigated barley of ten years’ duration. Agronomy Journal, 44, 267-270. DOI: 10.2134/agronj1952.00021962004400050011x

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Baker, G. A.; and R. E. Baker (1953). Strawberry Uniformity Yield Trials. Biometrics, 9, 412-421. DOI: 10.2307/3001713

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G. A. Baker, E. B. Roessler (1957). Implications of a uniformity trial with small plots of wheat. Hilgardia, 27, 183-188.

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https://hilgardia.ucanr.edu/Abstract/?a=hilg.v27n05p183

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Bancroft, T. A. et a1., (1948). Size and Shape of Plots and Distribution of Plot Yield for Field Experiments with Peanuts. Alabama Agricultural Experiment Station Progress Report, sec. 39. Table 4, page 6. http://hdl.handle.net/11200/1345

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Batchelor, L. D.; and H. S. Reed. (1918). Relation of the variability of yields of fruit trees to the accuracy of field trials. J. Agric. Res, 12, 245–283. https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245

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Bose, R. D. (1935). Some soil heterogeneity trials at Pusa and the size and shape of experimental plots. Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). https://archive.org/details/in.ernet.dli.2015.271739

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Bradley, P. L. (1941). A study of the variation in productivity over a number of fixed plots in field 2. Dissertation: The University of the West Indies. Appendix 1a, 1b, 1c, 1d. https://uwispace.sta.uwi.edu/dspace/handle/2139/41259

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The data are repeated in: C. E. Wilson. Study of the plots laid out on field II with a view to obtaining plot-fertility data for use in future experiments on these plots, season 1940-41. Dissertation: The University of the West Indies. Page 36-39. https://uwispace.sta.uwi.edu/dspace/handle/2139/43658

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Christidis, B. G. (1939). Variability of Plots of Various Shapes as Affected by Plot Orientation. Empire Journal of Experimental Agriculture 7: 330-342. Table 1.

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Christidis, Basil G (1931). The importance of the shape of plots in field experimentation. The Journal of Agricultural Science, 21, 14-37. Table VI, p. 28. DOI: 10.1017/S0021859600007942

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da Silva, Enedino Correa. (1974). Estudo do tamanho e forma de parcelas para experimentos de soja (Plot size and shape for soybean yield trials). Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, page 52-53.

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Davies, J. Griffiths (1931). The Experimental Error of the Yield from Small Plots of Natural Pasture. Council for Scientific and Industrial Research (Aust.) Bulletin 48. Table 1.

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Day, James Westbay (1916). The relation of size, shape, and number of replications of plats to probable error in field experimentation. Dissertation, University of Missouri. Table 1, page 22. https://hdl.handle.net/10355/56391

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Draper, Arlen D. (1959). Optimum plot size and shape for safflower yield tests. Dissertation. University of Arizona. https://hdl.handle.net/10150/319371

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Edan, T. (1931). Studies in the yield of tea. 1. The experimental errors of field experiments with tea. Agricultural Science, 21, 547-573. DOI: 10.1017/S0021859600088511

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Garber, RJ and Mcllvaine, TC and Hoover, MM. 1926. A study of soil heterogeneity in experiment plots. Jour Agr Res, 33, 255-268. Tables 3, 5. https://naldc.nal.usda.gov/download/IND43967148/PDF

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Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research. Wiley-Interscience. Page 481.

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Goulden, C. H. (1939). Methods of statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp

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Harris, J Arthur and Scofield, CS. (1920). Permanence of differences in the plats of an experimental field. Jour. Agr. Res., 20, 335-356. https://naldc.nal.usda.gov/catalog/IND43966236

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Harris, J. Arthur and Scofield, CS. (1928). Further studies on the permanence of differences in the plots of an experimental field. Jour. Agr. Res. 36, 15–40. https://naldc.nal.usda.gov/catalog/IND43967538

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Harris, J.A. 1920. Practical universality of field heterogeneity as a factor influencing plot yields. Journal of Agricultural Research, 19, 279–314. Page 296-297. https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279

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Hartman, J. D.; and E. C. Stair (1942). Proceedings Of The American Society For Horticultural Science, 41, 315-320. https://archive.org/details/in.ernet.dli.2015.240678

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Heath, O.V.S. (1970). Investigation by Experiment. Table 1. https://archive.org/details/investigationbye0000heat

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Holtsmark, G and Larsen, BR (1905). Om Muligheder for at indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. Tidsskrift for Landbrugets Planteavl. 12, 330-351. (In Danish) https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 https://dca.au.dk/publikationer/historiske/planteavl/

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Igue, Toshio; Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). Tamanho e forma de parcela experimental para cana-de-acucar (Plot size and shape for sugar cane experiments). Bragantia, 50, 163-180. Appendix, page 169-170. DOI: 10.1590/S0006-87051991000100016

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Immer, F. R. (1932). Size and shape of plot in relation to field experiments with sugar beets. Jour. Agr. Research, 44, 649–668. https://naldc.nal.usda.gov/download/IND43968078/PDF

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Jones, Marcus; Marin Harbur, and Ken J. Moore (2021). Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials. Agronomy 2021, 11, 1254. DOI: 10.3390/agronomy11061254 Data from 12x12 corn expt.

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Iyer, P. V. Krishna (1942). Studies with wheat uniformity trial data. I. Size and shape of experimental plots and the relative efficiency of different layouts. The Indian Journal of Agricultural Science, 12, 240-262. Page 259-262. https://archive.org/stream/in.ernet.dli.2015.7638/2015.7638.The-Indian-Journal-Of-Agricultural-Science-Vol-xii-1942#page/n267/mode/2up

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Kadam, B. S. Kadam; and S. M. Patel. (1937). Studies in Field-Plot Technique With P. Typhoideum Rich. The Empire Journal Of Experimental Agriculture, 5, 219-230. https://archive.org/details/in.ernet.dli.2015.25282

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Kalamkar, R.J. (1932). Experimental Error and the Field-Plot Technique with Potatoes. The Journal of Agricultural Science, 22, 373-385. DOI: 10.1017/S0021859600053697

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Kalamkar, R. J (1932). A Study in Sampling Technique with Wheat. The Journal of Agricultural Science, Vol.22(4), pp.783-796. DOI: 10.1017/S0021859600054599

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Kempton, R. A. and C. W. Howes (1981). The use of neighbouring plot values in the analysis of variety trials. Applied Statistics, 30, 59–70. DOI: 10.2307/2346657

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Kerr, H. W. (1939). Notes on plot technique. Proc. Internat. Soc. Sugarcane Technol. 6, 764-778.

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Khin, San. 1950. Investigation into the relative costs of rice experiments based on the efficiency of designs. Dissertation: Imperial College of Tropical Agriculture (ICTA). Appendix XV. https://uwispace.sta.uwi.edu/dspace/handle/2139/42396

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Kiesselbach, Theodore A. (1917). Studies Concerning the Elimination of Experimental Error in Comparative Crop Tests. University of Nebraska Agricultural Experiment Station Research Bulletin No. 13. Pages 51-72. https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop https://digitalcommons.unl.edu/extensionhist/430/

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Kristensen, R. K. (1925). Anlaeg og Opgoerelse af Markforsoeg. Tidsskrift for landbrugets planteavl, Vol 31, 464-494. Fig 1, pg. 467. https://dca.au.dk/publikationer/historiske/planteavl/

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Kulkarni, R. K., Bose, S. S., and Mahalanobis, P. C. (1936). The influence of shape and size of plots on the effective precision of field experiments with sorghum. Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. https://archive.org/details/in.ernet.dli.2015.271737

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Lander, P. E. et al. (1938). Soil Uniformity Trials in the Punjab I. Ind. J. Agr. Sci. 8:271-307.

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Laycock, D. H. (1955). The effect of plot shape in reducing the errors of tea experiments. Tropical Agriculture, 32, 107-114.

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Department of Agriculture, Mysore State. [2nd-9th] Annual Report of the Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ

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Lessman, Koert James (1962). Comparisons of methods for testing grain yield of sorghum. Iowa State University. Retrospective Theses and Dissertations. Paper 2063. Appendix Table 17. https://dr.lib.iastate.edu/handle/20.500.12876/73575

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Li, HW and Meng, CJ and Liu, TN. 1936. Field Results in a Millet Breeding Experiment. Agronomy Journal, 28, 1-15. Table 1. DOI: 10.2134/agronj1936.00021962002800010001x

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Loesell, Clarence (1936). Size of plot & number of replications necessary for varietal trials with white pea beans. Thesis, Michigan State. https://d.lib.msu.edu/etd/5271

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Lord, L. (1931). A Uniformity Trial with Irrigated Broadcast Rice. The Journal of Agricultural Science, 21(1), 178-188. DOI: 10.1017/S0021859600008029

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Love, Harry (1937). Application of Statistical Methods to Agricultural Research. The Commercial Press, Shanghai. Page 411. https://archive.org/details/in.ernet.dli.2015.233346/page/n421

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Lucas, HL. 1956. Switchback trials for more than two treatments. Journal of Dairy Science, 39, 146-154. DOI: 10.3168/jds.S0022-0302(56)94721-X

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Lyon, T.L. (1911). Some experiments to estimate errors in field plat tests. Proc. Amer. Soc. Agron, 3, 89-114. Table III. DOI: 10.2134/agronj1911.00021962000300010016x

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Magistad, O. C.; & C. A. Farden (1934). Experimental Error In Field Experiments With Pineapples. Journal of the American Society of Agronomy, 26, 631–643.

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Masood, M Asif and Raza, Irum. 2012. Estimation of optimum field plot size and shape in paddy yield trial. Pakistan J. Agric. Res., Vol. 25 No. 4, 2012

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McClelland, Chalmer Kirk (1926). Some determinations of plat variability. Agronomy Journal, 18, 819-823. DOI: 10.2134/agronj1926.00021962001800090009x

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Mercer, WB and Hall, AD, 1911. The experimental error of field trials The Journal of Agricultural Science, 4, 107-132. Table 1. DOI: 10.1017/S002185960000160X

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Montgomery, E. G. (1912). Variation in Yield and Methods of Arranging Plats To Secure Comparative Results. Twenty-Fifth Annual Report of the Agricultural Experiment Station of Nebraska, 164-180. https://books.google.com/books?id=M-5BAQAAMAAJ&pg=RA4-PA164

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E. G. Montgomery (1913). Experiments in Wheat Breeding: Experimental Error In The Nursery and Variation in Nitrogen and Yield. U.S. Dept of Agriculture, Bureau of Plant Industry, Bulletin 269. Figure 10, page 37. DOI: 10.5962/bhl.title.43602

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Moore, John F and Darroch, JG. (1956). Field plot technique with Blue Lake pole beans, bush beans, carrots, sweet corn, spring and fall cauliflower. Washington Agricultural Experiment Stations, Institute of Agricultural Sciences, State College of Washington.

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Nagai, Violeta (1978). Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size and number of repetitions in experiments with strawberry). Bragantia, 37, 71-81. Table 2, page 75. DOI: 10.1590/S0006-87051978000100009

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Nair, B. Gopakumaran Nair, (1984). Optimum plot size for field experiments on turmeric (Curcuma longa L) Thesis, Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy. https://krishikosh.egranth.ac.in/handle/1/5810147397 Appendix has data.

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Narain, R.; and A. Singh, (1940). A Note on the Shape of Blocks in Field Experiments. Ind. J. Agr. Sci., 10, 844-853. Page 845. https://archive.org/stream/in.ernet.dli.2015.271745

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Nonnecke, Ib Libner. (1958). Yield variability of sweet corn and canning peas as affected by plot size and shape. Thesis at Oregon State College. https://hdl.handle.net/1957/23367

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Obsi, Dechassa (2008). Application of Spatial Modeling to the Study of Soil Fertility Pattern. MS Thesis, Addis Ababa University. Page 122-125. http://etd.aau.edu.et/handle/123456789/3221

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Odland, TE and Garber, RJ. (1928). Size of Plat and Number of Replications in Field Experiments with Soybeans. Agronomy Journal, 20, 93–108. DOI: 10.2134/agronj1928.00021962002000020002x

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Paez Bogarin, Gilberto (1962). Estudios sobre tamano y forma de parcela para ensayos en cafe. Instituto Interamericano de Ciencias Agricolas de la O.E.A. Centro Tropical de Investigacion y Ensenanza para Graduados. Costa Rica. https://www.google.com/books/edition/Estudio_Sobre_Tamano_Y_Forma_de_Parcela/7ScOAQAAIAAJ

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Panse, V. G. (1941). Studies in the technique of field experiments. V. Size and shape of blocks and arrangements of plots in cotton trials. The Indian Journal Of Agricultural Science, 11, 850-867 https://archive.org/details/in.ernet.dli.2015.271747/page/n955

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Parker, E. R.; & L. D. Batchelor. (1932). Variation in the Yields of Fruit Trees in Relation to the Planning of Future Experiments. Hilgardia, 7(2), 81-161. Tables 3-9. DOI: 10.3733/hilg.v07n02p081

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Polson, David (1964). Estimation of Optimum Size, Shape, and Replicate Number of Safflower Plots for Yield Trials. Utah State University, All Graduate Theses and Dissertations, 2979. Table 6. https://digitalcommons.usu.edu/etd/2979/

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Robinson, H.F.; and J.A.Rigney and P.H.Harvey (1948). Investigations In Peanut Plot Technique With Peanuts. Univ California Tech. Bul. No 86.

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Rothamsted Experimental Station, Report 1925-26. Lawes Agricultural Trust, p. 154-155. http://www.era.rothamsted.ac.uk/eradoc/book/84

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Sayer, Wynne; M. Vaidyanathan and S. Subrammonia Iyer (1936). Ideal size and shape of sugar-cane experimental plots based upon tonnage experiments with Co 205 and Co 213 conducted in Pusa. Indian J. Agric. Sci., 1936, 6, 684-714. Appendex, page 712. https://archive.org/details/in.ernet.dli.2015.271737

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Sayer, Wynne; and Krishna Iyer. (1936). On some of the factors that influence the error of field experiments with special reference to sugar cane. Indian J. Agric. Sci., 1936, 6, 917-929. Appendix, page 927. https://archive.org/details/in.ernet.dli.2015.271737

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Shafi, Sameera (2007). On Some Aspects of Plot Techniques in Field Experiments on Tomato (Lycopersicon esculentum mill.) in Soils of Kashmir. Thesis. Univ. of Ag. Sciences & Technology of Kashmir. Table 2.2.1. https://krishikosh.egranth.ac.in/handle/1/5810083035

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Shafi, Sameera; S.A.Mir, Nageena Nazir, and Anjum Rashid. (2010). Optimum plot size for tomato by using S-PLUS and R-software’s in the soils of Kashmir. Asian J. Soil Sci., 4, 311-314. https://www.researchjournal.co.in/upload/assignments/4_311-314.pdf

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Smith, Francis L. (1958). Effects of plot size, plot shape, and number of replications on the efficacy of bean yield trials. Hilgardia, 28, 43-63. DOI: 10.3733/hilg.v28n02p043

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Smith, Louis H. 1910. Plot arrangement for variety experiments with corn. Agronomy Journal, 1, 84–89. Table 1. https://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84

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Stephens, Joseph C. 1928. Experimental methods and the probable error in field experiments with sorghum. Journal of Agricultural Research, 37, 629–646. https://naldc.nal.usda.gov/catalog/IND43967516

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Stickler, F. C. (1960). Estimates of Optimum Plot Size from Grain Sorghum Uniformity Trial Data. Technical bulletin, Kansas Agricultural Experiment Station.

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Strickland, A. G. (1935). Error in horticultural experiments. Journal of Agriculture, Victoria, 33, 408-416.

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A. G. Strickland (1932). A vine uniformity trial. Journal of Agriculture, Victoria, 30, 584-593.

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A. G. Strickland (1935). Error in horticultural experiments. Journal of Agriculture, Victoria, 33, 408-416.

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Wassom and R.R. Kalton. (1953). Estimations of Optimum Plot Size Using Data from Bromegrass Uniformity Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396. https://lib.dr.iastate.edu/ag_researchbulletins/32/

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Wiebe, G.A. 1935. Variation and Correlation in Grain Yield among 1,500 Wheat Nursery Plots. Journal of Agricultural Research, 50, 331-357. https://naldc.nal.usda.gov/download/IND43968632/PDF

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Wiedemann, Alfred Max. 1962. Estimation of Optimum Plot Size and Shape for Use in Safflower Yield Trails. Table 5. All Graduate Theses and Dissertations. Paper 3600. Table 5. https://digitalcommons.usu.edu/etd/3600/

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Williams, ER and Luckett, DJ. 1988. The use of uniformity data in the design and analysis of cotton and barley variety trials. Australian Journal of Agricultural Research, 39, 339-350. DOI: 10.1071/AR9880339

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F. A. Wyatt (1927). Variation in plot yields due to soil heterogeneity. Scientific Agriculture, 7, 248-256. Table 1. DOI: 10.4141/sa-1927-0020

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Reviewed. Not used. -

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Damor, Bhavika. (2019). Comparison of uniformity trial data with experimental data for plot technique. DEPARTMENT OF AGRICULTURAL STATISTICS B. A. COLLEGE OF AGRICULTURE ANAND AGRICULTURAL UNIVERSITY ANAND. https://krishikosh.egranth.ac.in/handle/1/5810169594 Has data for 3 uniformity trials, but the data look strange. For example, in Appendix III, there are some plots along the left edge that are measured to 3 decimals (0.993, 0.631), but most of the field is measured to 2 decimals. In Appendix I, there are 500 numbers but only about 9 of them have “4” in the hundredths place. We would expect to see 50. It could be that the data were collected in non-kg weights and then converted to kg. It could be that the html-viewer is not rendering the document correctly, but there is no way to download the original pdf.

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Hansen, Niels Anton (1914). Provedyrkning paa Forsogsstationen ved Aarslev. Tidsskrift for landbrugets planteavl. https://dca.au.dk/publikationer/historiske/planteavl/ Bind 21, page 553. In Danish. Page 557 might be the table referred to by Eden, except there are 30 plots, not 35. Yields appear to be given as percents. See comments by Eden & Maskell found here: https://archive.org/stream/in.ernet.dli.2015.25895/2015.25895.Journal-Of-Agricultural-Science-Vol-xviii-1928#page/n175 Page 165: “One of the trials, comprising 35 plots, extended over the years 1907-11 and included oats, rye, barley, roots, barley.” Oats and rye 1907 and 1908 show marked positive correlation. rye and barley 1908 and 1909 no correlation of significance. barley and roots 1909 and 1910 no correlation. roots and barley 1910 and 1911 a significant correlation. rye and roots 1908 and 1910 satisfactory.

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Hatton, R. G.; N. H. Grubb & R. C. Knight (1928) Black Currant Variety Trials: Reliability of Results., Journal of Pomology and Horticultural Science, 4:4, 200-220. DOI: 10.1080/03683621.1925.11513282 Not used. Has data for 21 bushes * 4 years and 52 bushes * 4 years.

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Lambert, Edmund B. (1934). Size and arrangement of plots for yield tests with cultivated mushrooms. Journal of Agricultural Research, Vol 48, 971-980. Note: Uniformity trial at three locations for mushrooms in growth houses. https://naldc.nal.usda.gov/naldc/download.xhtml?id=IND43968493

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Reviewed. No data found. -

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Bakke, Olaf Andreas. (1988). Tamanho e forma Ăłtimos de parcelas em delineamentos experimentais. Dissertation. Escola Superior de Agricultura Luiz de Queiroz. https://teses.usp.br/teses/disponiveis/11/11134/tde-20181127-160559/pt-br.php Used Gomez rice uniformity.

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Bayoumi, Tarek Youssef; S El-Demardash. Effect of water deficit on soil variability, plot size, shape and number of replications for chickpea. No data.

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Beard, J.S. (1954). Investigations into experimental plot technique with black wattle. Empire Forestry Review, 33, 158-171. https://www.jstor.org/stable/42602653 No data.

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Bhatt, Hitesh M. (1998). “Plot technique in Potato”. AAU, Anand. https://krishikosh.egranth.ac.in/handle/1/5810043200 No data.

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Bose, S S; Khanna, K L; Mahalanobis, P C Note on the optimum shape and size of plots for sugarcane experiments in Bihar. India Journal of Agricultural Science 9, 807-816 https://hdl.handle.net/10263/1896 http://library.isical.ac.in:8080/jspui/handle/10263/1896 No data.

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Boyhan, George E.; David B. Langston; Albert C. Purvis; C. Randell Hill. (2003). Research to determine suitable plot size and the number of replications in the field trials of sugarbeet. J. Amer. Soc. Hort. Sci., 128, 409-424. No data. Compared Hatheway’s method with Cochran and Cox’s method, the latter gave smaller number of plots.

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Boyhan, George E. (2013). Optimum Plot Size and Number of Replications for Determining Watermelon Yield, Fruit Size, Fruit Firmness, and Soluble Solids. HortScience, 48, 1200-1208. DOI: 10.21273/HORTSCI.48.9.1200 No data.

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Brim, C. A. and D. D. Mason. (1959). Estimates of Optimum Plot Size for soybean Yield Trials. Agron. Jour. 51: pp. 331-335¡ DOI: 10.2134/agronj1959.00021962005100060008x No data.

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Chaves, L. J.; J. B. Miranda Filho (1992). Plot size for progeny selection in maize (Zea maysL.). Theoretical and Applied Genetics, 84, 963–970. DOI: 10.1007/bf00227411 No data.

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Cheesman, E. E., and Pound, F. J. “Uniformity trials on Cacao”. Trop. Agric., 9, 277-88. No data.

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Conners, Helen Elizabeth (1951). Field plot techniques for sweet potatoes obtained from uniformity trial data. Master’s Thesis, Iowa State University. No digital copy available. No data.

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Cordeiro, Célia Maria Torres, João Eustáquio Cabral de Miranda, and Jarbas Campos. “Tamanho de parcelas e número de repetições em experimento de batatas.” Pesquisa Agropecuária Brasileira 17.9 (1982): 1341-1348. No data.

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Crews, Julian W., Guy L. Jones, and D. D. Mason. “Field Plot Technique Studies with Flue‐Cured Tobacco. I. Optimum Plot Size and Shape 1.” Agronomy Journal 55.2 (1963): 197-199. No data.

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da Silva, Willerson CustĂłdio; MĂĄrio Puiatti; Paulo Roberto Cecon; Leandro Roberto de Macedo; Tocio Sediyama. (2019) Estimation of optimum experimental plot size for taro culture. CiĂŞncia Rural, Santa Maria, v.49 DOI: 10.1590/0103-8478cr20180440 No data.

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da Silva, Luiz Fernando de Oliveira; Katia Alves Campos; Augusto Ramalho de Morais; Franciane Diniz Cogo; Carolina Ruiz Zambon. Optimal plot size for experiments with radish. Revista Ceres 59(5):624-629 DOI: 10.1590/S0034-737X2012000500007 No data.

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de Sousa, Roberto Pequeno; Paulo SĂŠrgio Lima e Silva; Janilson Pinheiro de Assis; Paulo Igor Barbosa e Silva; JĂşlio CĂŠsar DoVale. 2015. Optimum plot size for experiments with the sunflower. Revista Ciencia Agronomica, 46, 170-175. DOI: 10.1590/S1806-66902015000100020 No data. Precision increased up to plot sizes about 5 m^2.

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Dorph-Petersen, K. 1949. Parcelfordeling i markforsog. Tidsskrift for Planteavl. 52, 111-175 https://dca.au.dk/publikationer/historiske/planteavl/ No data. Did not see anything that looked like a field of 12x43 plots. Mentioned in Kristensen (2003).

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Elliot, F. C., Darroch, J.G. and Wang, H.L. (1952). Uniformity trials with spring wheat. Agronomy Journal, 44, 524-528. DOI: 10.2134/agronj1952.00021962004400100005x

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Facco, Giovani; Cargnelutti Filho Alberto; Mendonça Alves Bruna; et. al. February 2017. Basic experimental unit and plot sizes with the method of maximum curvature of the coefficient of variation in sunn hemp. African journal of agricultural research 12(6):415-423 DOI: 10.5897/AJAR2016.11814 No data.

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Filho, Alberto; Cargnelutti; Marcos Toebe; Cláudia Burin; André Luis Fick; Gabriele Casarotto. (2011). “Plot sizes and uniformity assays in turnip.” Ciência Rural 41.9 (2011): 1517-1525. DOI: 10.1590/S0103-84782011005000119 No data.

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Filho, Alberto Cargnelutti; Marcos VinĂ­cius Loregian, Gabriel Elias Dumke, Felipe Manfio Somavilla, Samanta Luiza da Costa, Lucas Fillipin Osmari, Bruno Fillipin Osmari (2021). Optimal plot size in buckwheat. DOI: 10.5433/1679-0359.2021v42n2p501 https://www.uel.br/revistas/uel/index.php/semagrarias/article/view/39448/29330 No data

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Fleming, A.A., Roger, T.H. and Bancroft, T.A. (1957). Field plot technique with hybrid corn under Alabama conditions. Agronomy Journal, 49, 1-4. DOI: 10.2134/agronj1957.00021962004900010001x

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George, M. V.; M. Sannamarappa (1984). Uniformity trial: Size, Shape, and Direction of Experimental Plots for Turmeric. Proceedings of the Sixth Symposium on Plantation Crops, p. 429. https://archive.org/details/in.ernet.dli.2015.502074/page/n453 No data.

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Gopalakrishna, S. (1992). Optimum plot size and shape, block size and shape and relative efficiency of designs for field experiments in navane (Setaria italica). Thesis. https://krishikosh.egranth.ac.in/handle/1/5810120871 No data.

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Goyal, Manoj Kumar. 1998. A Study of uniformity trial in wheat (Triticum aestivum L.). College of Agriculture Chaudhary Charan Singh Haryana Agricultural University Hisar. https://krishikosh.egranth.ac.in/handle/1/5810078304 . No data.

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Guimarães, Bruno Vinícius Castro, et al. (2020). “Optimal plot size for experimental trials with Opuntia cactus pear.” Acta Scientiarum. Technology 42 (2020): e42579-e42579. DOI: 10.5539/jas.v11n18p206 No data.

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Gupton, C.L. (1972). Estimates of Optimum Plot Size and Shape from Uniformity Data in Burley Tobacco (Nicotiana tabacum L.). Agron J, 64, 678-682. DOI: 10.2134/agronj1972.00021962006400050038x No data.

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Handa, D. P.; Sreenath, P. R., & Rajpali, S. K. (1995). Uniformity trial with lucerne grown for fodder. Grass and Forage Science, 50(3), 209–216. DOI: 10.1111/j.1365-2494.1995.tb02316.x No data.

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Hariharan, V., Jacob Thomas, M. George, K.C. (1986). Optimum size and shape of plots for field experiments in brinjal. Agricultural Research Journal of Kerala, 24(2), 189-194. https://krishikosh.egranth.ac.in/handle/1/5810100010 No data.

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Hatheway, W. H., and E. J. Williams. (1958). “Efficient estimation of the relationship between plot size and the variability of crop yields.” Biometrics, 14, 207-222. DOI: 10.2307/2527785 No data.

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Heidmann, T. 1988. Startkarakterisering af arealer til systemforskning I. Forsøgsarealer, müleprogram og metoder. Tidsskrift for Planteavls Specialserie. S 1958, 89 pp. https://dca.au.dk/publikationer/historiske/planteavlspecial/ No data. No obvious data found for a field 26x20 plots. Mentioned in Kristensen (2003).

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Heidmann, T. 1989. Startkarakterisering af arealer til systemforskning: IV Resultater fra arealet ved Jyndevad. Tidsskrift for Planteavls Specialserie. S 2021, 163 pp. https://dca.au.dk/publikationer/historiske/planteavlspecial/ No data. No obvious data found for a field 26x20 plots. Mentioned in Kristensen (2003).

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Hidalgo, Eduardo Calero Calero (1965). Estudio Del Tamano Y Forma De La Parcela Experimental Para Ensayos De Campo En Frijol (Phaseolus vulgaris L.). Instituto Interamericano de Ciencias Agricolas de la OEA. https://www.google.com/books/edition/Estudio_Del_Tamano_Y_Forma_De_La_Parcela/rNIOAQAAIAAJ No data. In Spanish.

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Hodnett, G.E. A Uniformity Trial on Groundnuts. Journal of Agricultural Science. https://www.cambridge.org/core/journals/journal-of-agricultural-science/article/uniformity-trial-on-groundnuts/0BE0B311FACEA593803F68180AF08452 No data.

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Holle, Michael. 1960. Plot technique for field evaluation of three characters in the lima bean. Master’s Thesis, Iowa State University. No digital copy available. No data.

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Idrees, Nadia; and Muhammad Inayat Khan (2009). Design improvement using uniformity trials experimental data. Pak. J. Agri. Sci., Vol. 46(4), 2009. https://pakjas.com.pk No data.

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Johnson, I.J. & Murphy, H.C. 1943. Lattice and lattice square designs with oat uniformity data and in varietal trials. J. Agric. Sci., 22, 366-372. DOI: 10.2134/agronj1943.00021962003500040004x No data. Has a contour map of fertility.

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Jones, W.W.; T.W. Embleton; C.B. Cree. (1957). Number of Replications and Plot Sizes Required for Reliable Evaluation of Nutritional Studies and Yield Relationships with Citrus and Avocado. American Society for Horticultural Science, 69:208-216. No data. Fruit yields are much more variable than quality factors.

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Jua, Junagadh. Optimum plot size for field experiments on tomato – A statistical investigation. Thesis, Junagadh Agricultural University. https://krishikosh.egranth.ac.in/handle/1/5810006490. No data.

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Kaushik, L. S., R. P. Singh, and T. P. Yadava. “A uniformity trial with mustard [India].” Indian Journal of Agricultural Sciences (1977). No data.

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Kavitha, B. (2010). Study on optimum plot size and optimum plot shape of soybean crop. https://krishikosh.egranth.ac.in/handle/1/84302 No data.

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Keller, Kenneth R. (1949). Uniformity Trial on Hops, Humulus lupulus L., for Increasing the Precision of Field Experiments. DOI: 10.2134/agronj1949.00021962004100080011x No data.

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Khurana, Alka. 1991. A Study of Uniformity Trial in Soyabean. Thesis, M.Sc. College of Basic Sciences and Humanities Chaudhary Charan Singh Haryana Agricultural University Hisar. https://krishikosh.egranth.ac.in/handle/1/5810076525 No data. Smith’s Law.

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Kristensen, Dr. K. (2003). Incomplete split-plots in variety trials - based on a-designs. In: Biuletyn Oceny Odmian, 31, pp. 7-17. No data. Refers to other papers with uniformity trials.

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Krysczun, Dionatan Ketzer; Lúcio, Alessandro Dal’Col; Sari, Bruno Giacomini; Diel, Maria Inês; Olivoto, Tiago; Santana, Cinthya Souza; Ubessi, Cassiane; Schabarum, Denison Esequiel (2018). Sample size, plot size and number of replications for trials with Solanum melongena L.. Scientia Horticulturae, 233(), 220–224. DOI: 10.1016/j.scienta.2018.01.044 No data.

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Kumar, Ajay. 1999. A study on uniformity trial in sesame (Til) Sesamum indicum. https://krishikosh.egranth.ac.in/handle/1/5810078711. No data.

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Kumar, Ajay; Kiran Kapoor; Gupta, S. C.; Hasija, R. C. Uniformity trial on sesame Sesamum indicum. https://eurekamag.com/research/017/592/017592269.php Not found, but Kumar’s thesis was found, but had no data.

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Lakhera, M.L.; M.A. Ali (1996). Optimum plot size and shape estimates for sunflower yield trials. Journal of Maharashtra Agricultural Universities 21(3): 350-353 No data. Optimum plot size was 20 units, 10 rows each 2 m long.

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Lavezo, AndrÊ; Alberto Cargnelutti Filho, Clåudia Marques de Bem, Clåudia Burin, JÊssica Andiara Kleinpaul, Rafael Vieira Pezzini. Plot size and number of replications to evaluate the grain yield in oat cultivars. Bragantia, Campinas, v. 76, n. 4, p.512-520, 2017. DOI: 10.1590/1678-4499.2016.410 No data.

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Lizy, M.J. (1986). Uniformity trials on colocasia. https://krishikosh.egranth.ac.in/handle/1/5810113181 No data.

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Lizy, M.J.; George, K.C.; Thomas, M.J. (1988). Optimum size and shape of plots under colocasia (Colocasia esculenta L.) Agricultural Research Journal of Kerala 25(2): 241-248 https://eurekamag.com/research/001/901/001901914.php Paper not found, but Lizy thesis was found, but had no data.

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Lohmor, Nishu (2015). Estimation of optimum plot size, shape and number of replications in sunflower (Helianthus annuus). Thesis. https://krishikosh.egranth.ac.in/handle/1/81261 No data.

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Lohmor, Nishu; Mujahid Khan; Kiran Kapoor; and Nitin Tanwar (2017). Study on Optimum Block Size and Shape in Uniformity Trial of Sunflower (Helianthus annuus). Advances in Research, 9, 1-8. 2017. No data.

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Lorentz, Leandro Homrich; Alexandra Augusti Boligon; Lindolfo Storck; Alessandro Dal’Col Lúcio. Plot size and experimental precision for sunflower production. Sci. Agric. (Piracicaba, Braz.), v.67, n.4, p.408-413, July/August 2010 No data.

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Meier, V. D., and K. J. Lessman. “Estimation of Optimum Field Plot Shape and Size for Testing Yield in Crambe abyssinica Hochst”. Crop Science 11, 648-650. DOI: 10.2135/cropsci1971.0011183X001100050013x No data.

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Miller, John D., and E. James Koch. (1962) “A Plot Technique Study with Birdsfoot Trefoil 1.” Agronomy Journal, 54, 95-97. https://acsess.onlinelibrary.wiley.com/doi/abs/10.2134/agronj1962.00021962005400020001x No data.

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Miyaska, Susan C.; Charles E. McCulloch, Graham E. Fogg, James R. Hollyer. Optimum Plot Size for Field Trials of Taro (Colocasia esculenta). HortScience 48, 435-443. DOI: 10.21273/HORTSCI.48.4.435 No data. Optimum plot size was 18-21 plants (4.9 - 5.7 m^2).

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Modjeska, J. S., & Rawlings, J. O. (1983). Spatial Correlation Analysis of Uniformity Data. Biometrics, 39(2), 373. DOI: 10.2307/2531010 Used data from Batchelor & Reid.

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Muniya, Sejal ben. Plot size study through mathematical approach. https://krishikosh.egranth.ac.in/handle/1/5810115216 No data.

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Nagardas, Motaka Ganapatlal (2006). PLOT SIZE STUDY FROM UNIFORMITY TRIAL DATA IN DURUM WHEAT FOR BHAL REGION. https://krishikosh.egranth.ac.in/handle/1/5810115906 No data.

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Narayanan, R (1974). Estimation of optimum plot size for clone trials in hevea. Journal of the Rubber Research Institute of Malaysia 24(1): 54-67. https://rios.lgm.gov.my/cms/fedDigiJournalDetail.jsp?searchText=&selTab=digiCon&id=&type=JRR&issueYear=1974 No data.

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Narayanbhai, P.J. (2005). Plot size study from uniformity trial data of French bean. https://krishikosh.egranth.ac.in/handle/1/5810115923 No data.

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Oliveira, SĂŠrgio JosĂŠ Ribeiro de. DependĂŞncia entre o tamanho da unidade bĂĄsica e as estimativas das dimensĂľes da parcela experimental de batata. Diss. Universidade Federal de Santa Maria, 2005. No data.

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Omanakuttan, Chithra (2015). Plot size study from uniformity trial data of mustard (Brassica juncea L.). https://krishikosh.egranth.ac.in/handle/1/5810116905 No data.

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Onate, Burton. 1965. Size and Shape of Plot in Rice Replicated Field Experiments. Philippine Statistician. No data. Results indicate that plot of dimensions 4 rows by 12 hills (3-5 sq m) will be relatively as efficient as bigger plots. The CV observed was about 10 percent, which implies 5 replicates are needed to detect a mean difference of 10 percent.

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Paardekooper, E.G. (1966). Results of four uniformity trials to determine the influence of plot size and shape on the efficiency of field experiments in rubber. Res. At-chs Rubb. Res. Inst. Malaya, Docum. 56. No data.

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Pal, Satyabrata; Goutam Mandal; Kajal Dihidar. 2015. Determination of robust optimum plot size and shape – a model-based approach. Biometrical Letters, 52, 13-22. DOI: 10.1515/bile-2015-0002 No data.

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Patel, J.B. (2004). Estimation of Optimum Plot Size for Field Experiment on Green Gram (Phaseolus rediatus L.) https://krishikosh.egranth.ac.in/handle/1/5810072537 No data.

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Patel, Natverlal M. (1967). Plot technique in Bajri-Pennisetum typhoides. https://krishikosh.egranth.ac.in/handle/1/5810045227 No data.

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Patel, N.M. (1981). Plot size study in bidi tobacco (Nicotiana tabacum L.). Thesis. https://krishikosh.egranth.ac.in/handle/1/5810045267 No data.

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Patil, N.M. (1995). Optimum plot size and shape, block size and shape and relative efficiency of designs for field experiments in cowpea (Vigna unguiculata). https://krishikosh.egranth.ac.in/handle/1/5810119241 No data.

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Peiris, T. S. G.; and R. O. Thattil (1997). Alternative methods to determine plot sizes for three crops: A case study from coconut data. COCOS (1997), 12, 44-53 No data. References CRI (1985).

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Pimentel, Gomes; Couto, H.T.Z. (1985). O Tamanho Ótimo de Parcela Experimental Para Ensaios com Eucaliptos (The Optimum Experimental Plot Size for Essays with Eucalipt). Revista do IPEF, Pirarcicaba, 31, 75. No data.

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Plaisted, Robert LeRoy (1954). Field plot techniques for estimating onion yields. Master’s Thesis, Iowa State University. DOI: 10.31274/rtd-20200617-42 No data.

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Prabhakaran, P.V. (1978). Optimum plot size for field trials with banana. Agricultural Research Journal of Kerala, 16(1), 33-38. https://krishikosh.egranth.ac.in/handle/1/5810115813 No data.

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Prajapati Rameshbhai, I. (2005). Optimum size and shape of plot for field experiments on potato. Thesis. https://krishikosh.egranth.ac.in/handle/1/5810115905 No data.

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Ramani, C.V. (1995). Plot technique in Lucern. https://krishikosh.egranth.ac.in/handle/1/5810046435 No data.

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Reddy, M. N., and C. K. Ramanatha Chetty Effect of Plot Shape on Variability in Smith’s Variance Law. Experimental Agriculture, 18, 333-338. DOI: 10.1017/S0014479700000752 No data.

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Reddy, S.B.S. (1992). Uniformity trial on groundnut. https://krishikosh.egranth.ac.in/handle/1/5810000021 No data for the uniformity trial? Small dataset on page 96, 20 plots with 5 plants/plot. Branches/plant, leaves/plant, height of the plant.

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Ross C. Thompson (1934). Size, Shape, and Orientation of Plots and Number of Replications Required in Sweetpotato Field-Plot Experiments. J. Agric. Rsch., 5, https://books.google.com/books?hl=en&lr=&id=9-DUAAAAMAAJ&oi=fnd&pg=PA379 No data. Interesting paper because the experiment was conducted on 4 plots of year/loc combinations.

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Rossettii, A.G.; Pimentel Gomes, F. (1983). Determinação do Tamanho Otimo de Parcelas em Ensaios Agricolas (Determination of the Optimum Plot Size in Agricultural Essays). Pesq. agropec. bras., Brasilia, 18(5), 477. No data. In Spanish.

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Rossetti, A.G.; Gomes, F.P. (1987). A method for the determination of optimum plot size in experiments with rubber trees (Hevea). J Nat Rubb Res (1987) 2(3):135-141 http://agris.upm.edu.my:8080/dspace/handle/0/15652 No data.

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Sagisi, Fancisca D.; Mariano B. de Ramos. Field Plot and Sampling Techniques on Virginia Tobacco. No data for the uniformity trial part of this report.

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Sands, Daniel E. (1954). A Determination of Optimum Plot Size for Estimating Mean Number of Bolls. USDA. https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Yield_Reports/A%20Determination%20of%20Optimum%20Plot%20Size%20for%20Estimating%20Mean%20Number%20of%20Bolls.pdf No data.

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Sari, Bruno Giacomini; Alessandro Dal’Col Lucio. (2018). Uniformity trials size for estimating cherry tomato plot size. Revista Ciência Agronômica, v. 49, n. 4, p. 653-662, out-dez, 2018 http://ccarevista.ufc.br/seer/index.php/ccarevista/article/view/4462 No data.

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Schmildt, Edilson Romais; Adriel Lima Nascimento; Alan de Lima Nascimento; et al. Optimum plot size for experiments with papaya genotypes in field. African Journal of Agricultural Research, 13, 360-365. DOI: 10.5897/AJAR2017.12951 No data. Optimum plot size if four plants by plot, using four replications.

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Sharma, Anju (1992). Determination of optimum plot size estimation of of tomato (Lycopersicon esculentum Mill.) yield in Saproon Valley of Solan District (H.P.). https://krishikosh.egranth.ac.in/handle/1/5810067799 No data.

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Sharpe, R. H., and G. H. Blackmon, 1950 “A Study of Plot Size and Experimental Design with Pecan Yield Data.” Am. Soc. Hort. Sci. Pro. 56:236-241. No data.

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Sheela, M A (1987). Optimum size of plots in cocoa (Theobroma cacoa L.) a multivariate case. Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy https://krishikosh.egranth.ac.in/handle/1/5810142527 No data.

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Shitap, Shri M.S. (2011). Plot technique in Brinjal (Solanum melongena L.) https://krishikosh.egranth.ac.in/handle/1/5810010600 No data. The table of contents suggests that the data appears in the appendix, but the pages are blank.

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Siao, F.U. 1935. Uniformity trials with cotton. J. Amer. Soc. Agron., 27, 974-979. DOI: 10.2134/agronj1935.00021962002700120004x No data.

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Sills, G. R., & Nienhuis, J. (1993). Field Plot Technique Affects Snap Bean Yield Evaluation. Journal of the American Society for Horticultural Science, 118(5), 672-674. DOI: 10.21273/JASHS.118.5.672 No data.

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Silva, Mauricio dos Santos da, et al. (2019) “Optimal size of experimental plots of papaya trees using a modified maximum curvature method.” Ciência Rural 49 (2019). https://www.scielo.br/j/cr/a/CYZsbJzSD6SWpYB9X3WscqF/?lang=en No data.

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Sokhal, Urmil (1982). College on The Efficiency of Designs for Bajra in Relation to Shape and size of Plot And Block. https://krishikosh.egranth.ac.in/handle/1/5810055172 No data.

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Soplin, H., H. D. Gross, and J. O. Rawlings. “Optimum Size of Sampling Unit to Estimate Coastal Bermudagrass Yield 1.” Agronomy Journal 67.4 (1975): 533-537. No data.

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Stadler, Lewis John. (1921). Experiments in field plot technic for the preliminary determination of comparative yields in the small grains. University Of Missouri. https://hdl.handle.net/2027/umn.319510008017444 No data. Uniformity trial discussed on page 43.

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Storck, Lindolfo; Sidinei Lopes; Alessandro Dal’Col Lúcio; Alberto ​Cargnelutti Filho 2011. Optimum plot size and number of replications related to selective precision. Ciência Rural 41(3):390-396. DOI: 10.1590/S0103-84782011000300005 https://www.researchgate.net/publication/270761907_Optimum_plot_size_and_number_of_replications_related_to_selective_precision No data

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Swallow, William H.; Todd C. Wehner (1986). Optimum Plot Size Determination and Its Application to Cucumber Yield Trials. Euphytica, 35, 421-432. DOI: 10.1007/BF00021850 No data. For multiple-harvest yield trials, optimum plot sizes were estimated to be 6.4 to 10.3 m^2.

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Tartaglia, FdL, Lúcio, A, Diel, MI, et al.  Experimental Plan For Tests With Pea. Agronomy Journal. 2021; 113: 1394–1406. DOI: 10.1002/agj2.20575 No data.

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Taye, Girma; Amsal Tarekegne; and D. G. Tanner. (2000). “Estimation of optimum plot dimensions and replication number for wheat experimentation in Ethiopia.” African crop science journal 8.1 (2000): 11-23. https://repository.cimmyt.org/handle/10883/2321 No data.

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Taylor, F.W. (1907-1909). The size of the experimental plot for field crops. Proceedings annual Society of agronomy 1, 56-58. https://acsess.onlinelibrary.wiley.com/doi/10.2134/agronj1907-1909.00021962000100010014x No data.

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Taylor, Howard Lewis (1951). The effect of plot shape on experimental error. Master’s Thesis, Iowa State University. No digital copy available. No data given. Used data from a corn uniformity trial, oats uniformity, and the data of Fairfield Smith. Smaller experimental errors were found for long narrow plots.

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Tedin, Olof (1931). The Influence of Systematic Plot Arrangement upon the Estimate of Error in field Experiments. J. Agric. Science, 21, 191-208. DOI: 10.1017/S0021859600008613 No data. Uses uniformity trials from other papers.

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Toebe, Marcos, et al. (2020). “Plot size and number of replications for ryegrass experiments.” Ciência Rural 50.1. DOI: 10.1590/0103-8478cr20190195 No data.

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Westover, Kyle C. (1924). The influence of plot size and replication on experimental error in field trials with potatoes. Agricultural Experiment Station, West Virginia University, No. 189. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019921359&view=1up&seq=1 No data.

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Wilson, Wendell W. (1970). Texas Vegetable Remote Sensing Study, 1969 Determination of Optimum Plot Size and Shape for Estimation of Carrot Yield. Research and Development Branch Standards and Research Division Statistical Reporting Service. https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Yield_Reports/Texas%20Vegetable%20Remote%20Sensing%20Study%201969.pdf No data found.

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Yadav, Raj Pal. 1991. Spatial Correlation Analysis of Uniformity Trial Data. College of Basic Sciences and Humanities Chaudhary Charan Singh Haryana Agricultural University Hisar. https://krishikosh.egranth.ac.in/handle/1/5810076755 No data.

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Vallejo, Roger L.; VHumberto A. Mendoza. (1992). Plot Technique Studies on Sweetpotato Yield Trials. J. AMER. Soc. HORT. SCI. 117(3):508-511. No data.

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Vargas-Rojas, Jorge Claudio. (2020). Size and shape of the experimental unit in yield trials of Brachiaria , hybrid CIAT 3608. https://www.semanticscholar.org/paper/Size-and-shape-of-the-experimental-unit-in-yield-of-Vargas-Rojas/e9964eca6ac42471aa44bc45e84bc376b02daf09

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Warren, J.A. and Mendez, I. (1981). Block Size and Orientation, and Allowance for Positional Effects, in Field Experiments. Experimental Agriculture, 17, 17 - 24. DOI: 10.1017/S0014479700011182 No data. Used a dozen existing datasets.

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Paper not found -

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Abraham, T.P. & Vachhani, M.V. 1964. Investigations on field experimental techniques with rice crop Indian J. Agric. Sci. 34, 152-165.

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Agarwal, K.N. (1973). Uniformity trial of Apple. Indian Journal of horticulture. 30:525-528. https://www.indianjournals.com/ijor.aspx?target=ijor:ijh&volume=30&issue=3and4&article=013 Paywall. Not available.

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Agarwal, K.N. & Deshpande, M.R. 1967. Size and shape of plots and blocks in field experiments with dibbled paddy. Indian J. Agric. Sci., 37, 445-455.

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Agarwal, K.N., Bavappa, K.V.A. and Khosla, R.K. (1968). A study of size and shape of plots and blocks an optimum number of experimental periods on arecanut. Indian Journal of Agricultural Sciences, 38, 444-460. Article not found. Journal: http://epubs.icar.org.in/ejournal/index.php/IJAgS

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Agnihotri, V. and Asrawal, M. C. 1996. Size and shape of plots and blocks for field experiments with Eucalyptus in Shivalik Hills. Indian Journal of Forestry, 19, 74-78. Not found.

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Ali, A.A.; Ammar, S.E.M.M. Optimum plot size and shape for trials of maize. Annals of Agricultural Science, Moshtohor 36(3): 1361-1372. https://eurekamag.com/research/012/808/012808132.php Not found.

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Beckett, W.H.; Fletcher, S.R.B. A uniformity trial with maize. Gold Coast Dept Agric Bull 16: 222-226. https://eurekamag.com/research/029/823/029823954.php Not found.

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Bhatnagar, S.; Srivastava, O.P. and Lather, B. P. S. Optimum size and shape of plot for mustard. Annals of Agricultural Research. Not found.

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Bischoff, K.P.; Gravois, K.A.; Schexnayder, H.P.J.; Hawkins, G.L. (2001). The effect of harvest method and plot size on the estimation of sugarcane yield. Journal American Society of Sugar Cane Technologists 21: 51-60.

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Bonazzi, A. (1933). Errors in field experimentation with ratoon cane. Asoc. de Tec. Azucareros Cuba, Proc 7: 32-40. Looked hard, but could not find. Original probably in Spanish. Full publisher: AsociaciĂłn de TĂŠcnicos Azucareros de Cuba.

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Calzada Benza, J. 1957. El error experimental y la precision en los experimentos. Lima Peru , Estac. Expt. Agr. de La Molina , Bol. 67 , 33 pp . Not found.

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Chapas, L.C. 1961. Plot size and reduction of variability in oil palm experiments. Emp Jour. Exp. Agric., 29, 212-224.

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Chaudhary, G.K.; Prajapati, B.H.; Patel, J.K.; Prajapti, R.I.; Loria, J.M. (2011) Optimum size and shape of plot for field experiments on wheat under North Gujarat Condition. J.Ind.Soci.Agril.S

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CRI (1985). Report of the Coconut Research Institute for 1985. (Ed. R Mahindapala), Lunuwila, Sri Lanka.

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Dutta, S. K., and HEATH, E. D. Size, shape, and number of plots for field experiments with tea. Not found.

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Faqir Mohammad, Tariq Mahmood Bajwa; Sohail Ahmad. (2001). Size and Shape of Plots for Wheat Yield Trials in Field Experiments. International Journal Of Agriculture & Biology, 4, 397-402.

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Fisher, Alan Charles (1973). A Technique for Designing Field Experiments Using Uniformity Trial Data. North Carolina State University at Raleigh. https://catalog.lib.ncsu.edu/catalog/NCSU508428 No electronic version.

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French, M. H. (1956). Minimum size of experimental plots for the assessment of pasture yield. East African Agr. and Forestry Res. Organ. Rpt. 1956.

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French, M. H. Errores asociados con el uso de pequeĂąas parcelas de prueba en las evaluaciones de rendimiento de pastos. Agron. Trop. 10, 71-76.

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Galal, H.E.; E.G.ttouh Abou. (1971). Estimation of optimum plot size and shape for Egyptian cotton yield trials. Alexandria Journal of Agricultural Research 19(2): 233-238

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Gopini, D.D.; Kabaria, M.M. and Vaishnani, N.L. (1970). Size and shape of plots in field experiments on groundnut. Indian Journal of agricultural science, 40, 1004-1010. Not found.

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Gosh, G.L.M. & Sanyal, A.T. 1945. Techniques of field experiments in jute. Agri. Res. Memoire 4, Indian Central Jute Comm.

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Gupta, J.P. and Raghavarao, D. (1969). A uniformity trial on onions. Journal of Indian Horticulture, 7, 184-187.

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Gupta, S. C.; Kauchik, L. S.; Yadava T. P. (1991). A study of size and shape of experimental plots with groundnut. Indian Journal of Agricultural Research, 25, 161-166. https://krishikosh.egranth.ac.in/handle/1/2040054 Restricted access.

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Hamdi, A.; Shokr, E. M. H.; Sedhom, S. A.; Hassanein, S. A. A. (2003). Estimation of optimum plot size and shape for lentil yield trials. Egyptian Journal of Agricultural Research, 81, 1723-1734.

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Handa, D.P., Sreenath, P.R., Sastry, J.A., Rajpal, S.K. and Shukla, N.P. (1982). Size and shape of plots and blocks for experiments with oats grown for fodder. Indian Journal of agricultural sciences, 52, 466-471. Not found.

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Hoshyar Chand Sharma (1991). Some observations on estimation of plot size and shape, Bartlett’s Rate Index and yield loss adjustments. Journal of Indian potato Association, 18, 9-18.

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Jayaram, K.; Venkataramu, M.N.; Sunderrajan, N.; Seetharam, A. (1981) Optimum size and shape of plots and blocks for field experiments in sunflower (Helianthus annuus Linn.) Mysore Journal of Agricultural Sciences 15(4): 512-518 https://eurekamag.com/research/001/422/001422535.php

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Joachim, A.W.R. 1935. A uniformity trial with coconuts. Trop. Agric. Mag. Ceylon. Agric. Soc. 85:198-207. https://eurekamag.com/research/013/298/013298524.php Not found. Has data! Todo.

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Jolly, A. L. Uniformity trials on estate cacao. Trop. Agriculture, 19, 167-174. https://journals.sta.uwi.edu/ta/index.asp?action=viewIssue&issueId=532 No access.

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Kaushik, L.S.; Kapoor, K.; Hasija, R.C.; Tyagi, C.S. (1989). A uniformity trial on moong (Vigna radiata (L) Wilczek) Indian Journal of Agricultural Research 23(3): 138-142 https://krishikosh.egranth.ac.in/handle/1/2040100 Restricted access.

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Khan, Mujahid. 2015. Uniformity trial in raya (Brassica juncea). M.Sc. Dissertation. https://krishikosh.egranth.ac.in/handle/1/80362 Seems like paper should be here, but download fails.

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Khurana, A., Rai, L. and Gupta, S.N. (1992). A uniformity trial on soybean (Glycine max. L.). Haryana agricultural University Journal of research, 22, 225-228.

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Kristiensen, K. and Ersboll, A.K. 1995. The use of Geostatistical methods in variety trials, where some varieties are unreplicated. Biuletyn Oceny Odmain (Cultivar Testing Bulletin), 26-27, 113-122. Not found.

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Kumar, Manoj and Hasija (2002). A study of size and shape of plots with wheat. Annals of Agriculture Bio-research 7 (1), 89-93

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Lakhera, M.L.; Singh, V.P.; Sundar, R. M. and Patel, N.M. (1995). Optimum size and shape of plot for field experiments on gram. Gujarat Agricultural University Research Journal, 21, 142-148.

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Lakshmi, K. R. and Gadewar, A.U. (1989). Optimum plot size for field trials with yams. J. of Root Crops, 15, 25-27.

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Lopes, S. J. et al. (2005). Experimental plot size in grain sorghum in different plant densities. June 2005; Pesquisa Agropecuåria Brasileira 40(6):525-530.

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Lu, ChunTang et al (1995). Estimation of optimum plot size and replicates number using data from rice uniformity trials. J. of Agricultural Research of China, 44, 229-241.

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Mahalanobis, P C; Chakravorti, S C; Banerjee, E A R. 1934. Influence of shape and size of plots on the accuracy of field experiments with rice, Chinsurah, Bengal Procedings of Indian Science Congress (Bombay) Sec Agricultural Statistics 21, 71. http://library.isical.ac.in:8080/xmlui/handle/10263/1793 Not found; pdf only contains abstract.

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Mangat, N. S. (1984). “Optimum size and shape of plot and block in cotton field experiments.” Journal of Research-Punjab Agricultural University (India).

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McHatton, T.H. 1947. The comparison of plot size in a peach experiment Proceedings American Society Hort Science 49: 18-20 https://eurekamag.com/research/013/864/013864510.php No access. Not found.

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Menon, T.C.M. and Tyagi, B.N. (1971). Optimum size and shape of plots in experiments with mandarin orange. Indian Journal of agricultural science, 41, 857-861. https://eurekamag.com/research/000/153/000153659.php Not available.

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Murray, D.B. 1950. A uniformity trial with swamp rice. Trop. Agriculture https://journals.sta.uwi.edu/ta/index.asp?action=viewIssue&issueId=406 No access.

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Pandya, H.R.; Upadhyay, S.M>; Parmar, B.A. (2006). Analysis of uniformity trial on cotton. J. Ind. Soc. Agric. Statist, 60, 175-236.

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Peiris, W. V. D. and Salgado, M. L. M. (1937). Experimental error in field experiments with coconut. Trop. Agriculture, Trinidad, 89:75-85

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Pimentel Gomes, F. (1987). Novos Aspectos do Tamanho Otimo das Parcelas em Experimentos Com Plantas Arbdreas (A New Contribution on the Problem of Plot Size in Experiments with Trees). Pesq. agropec. bras., Brasilia (in press).

-

Prabhakaran, P.V. (1974). Optimum plot size for field experiments with tapioca Agricultural Research Journal of Kerala, 12(1), 19-23. https://krishikosh.egranth.ac.in/handle/1/5810114240

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Rai, Lajpat Gupta, S.N. and Rai, L. (1994). Optimum size and shape of plots in field experiments of soyabean (Glycine max. L.). Annals of biology Ludhiana, 10, 261-263.

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Rambabu; Aarwal, M.C.; Somaraj, P. and Chinnamani, S. (1980). Size and shape of plots and blocks for field trials on natural grasslands in Nilgiris hills. Indian Journal of agricultural science, 50, 598-602. https://eurekamag.com/research/006/424/006424390.php Not found

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Schuster, W; Bretschneider-Hermann, N; Zschoche, K.-H. (1984). Untersuchungen zur Feldversuchstechnik von Winterraps. Versuche zur TeilstĂźckgrĂśsse, TeilstĂźckform und zur Zahl der Wiederholungen [English: Investigations into experimental designs with winterrape experiments on plot size, plot-shape and numbers of replications]. Zeitschrift fĂźr Acker- und Pflanzenbau. [Eng: Journal of agronomy and crop science]. 1984, Vol 153, Num 6, pp 446-459.

-

Sen, A.R. 1963. Some techniques of experimentation with cloval tea based on a uniformity trial. Emp. J. Exp. Agri., 31, 296-310.

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Sethi, A.S. (1985). A modified approach to determine optimum size and shape of plots in field experiments on maize grown on terraced land. Indian Journal of Agricultural Sciences, 36, 95-99.

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Shamasundaran, K.S., Singh, K.P. and Mandhar, S.C. (2002). Standardization of optimum size and shape of plots for gerbera under naturally ventilated greenhouse condition. Indian Journal of Horticulture, 59, 431-434. http://www.indianjournals.com/ijor.aspx?target=ijor:ijh&volume=59&issue=4&article=019 Paywall.

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Shu, S.C.; Park, S.H.; Yu, I.S.; Joo, Y.J.; Hong, Y.K. (1970). Studies on the optimum plot size, plot shape and number of replications in field experiments for wheat. Research Reports of the Office of Rural Development, S. Korea, Crops, 13: 95-99 https://eurekamag.com/research/014/696/014696398.php

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Singh, R.P. (1989). Optimum plot size for yield trials on chillies Indian Journal of Horticulture 46(3): 404-406 https://eurekamag.com/research/001/901/001901901.php

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Sreenath, P.R. (1973). Size and shape of plots and blocks in field trials with sorghum. Indian Journal of Agricultural Sciences, 43, 110-112. Not found.

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Srinivasan, C.S.; George, M.V.; Subbalakshmi, V.; Amarnath, C.H. (1994). Optimum plot size for field trials with Coffea arabica cv. Cauvery (Catimor) Journal of Coffee Research 24(2): 87-96.

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Upadhyay, S. M., Prajapati, B.H., Raja, K.R.V. and Darji, V.B. (1994). Optimum plot size for summer paddy in the Navsari zone of Gujarat. Gujarat Agricultural University Research Journal, 19, 92-97. Not found.

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Vaidyanathan, M. The method of covariance applicable to the utilization of the previous crop records for judging the improved precision of experiments. Ind. J. Agric. Sci., 4, 327-42. Not found. Has data. Todo.

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Weber, Charles Robert. 1941. Statistical analysis of a uniformity trial of soybeans with reference to plot size and shape and a comparison of efficiency of various experimental designs. Univ of Illinois. Thesis.

-

Webster, C. 1939. A note on a uniformity trial with oil palms. Tropical Agriculture, 16, 15-19. Behind paywall.

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Yun, K.H.; Park, S.H.; Lee, Y.M. (1968). An estimation of optimum plot size, shape and number of replications for yield performance trials of rice. Res. Rep. Office rur. Dev, Suwon, 11: 1, 53-7. Bibl. 21 https://eurekamag.com/research/014/341/014341511.php

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Zuhlke, Thomas Albert (1968). Uniformity Trial in Pisum Sativum for Estimating Optimum Plot Size and Shape, Replication Number, and Relative Efficiency of Lattice Designs. https://www.google.com/books/edition/A_Uniformity_Trial_in_Pisum_Sativum_for/Ya9nAAAAMAAJ?hl=en&gbpv=0

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All vignettes

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Graphical Gems in the agridat Package
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Introduction to `agridat`
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Notes on uniformity data
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Citation

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Wright K (2021). -agridat: Agricultural Datasets. -R package version 1.19, https://kwstat.github.io/agridat/. -

- 
@Manual{,
-  title = {agridat: Agricultural Datasets},
-  author = {Kevin Wright},
-  year = {2021},
-  note = {R package version 1.19},
-  url = {https://kwstat.github.io/agridat/},
-}
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    agridat -

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    Homepage: https://kwstat.github.io/agridat

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    Repository: https://github.com/kwstat/agridat

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    The agridat package provides an extensive collection of datasets from agricultural experiments.

    -

    The datasets come from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

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    This package tries to make data FAIR: Findable, Accessible, Interoperable, Reusable.

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    Key features -

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    • Thorough documentation.

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    • Examples for (almost) every dataset.

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    Installation -

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    -# Install the released version from CRAN:
-install.packages("agridat")
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-# Install the development version from GitHub:
-install.packages("devtools")
-devtools::install_github("kwstat/agridat")
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    Usage -

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    -library(agridat)
-?agridat # list all datasets with keywords
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    Developed by Kevin Wright.

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    Site built with pkgdown 2.0.1.

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    - - - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad8276..00000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/logo.png b/docs/logo.png deleted file mode 100644 index 10c94c2a..00000000 Binary files a/docs/logo.png and /dev/null differ diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 984986c0..00000000 --- a/docs/news/index.html +++ /dev/null @@ -1,285 +0,0 @@ - -Changelog • agridat - - -
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    agridat 1.19 - Dec 2021

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    New data

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    arankacami.groundnut.uniformity, barrero.maize, buntaran.wheat, gomez.heterogeneity, grover.diallel, grover.rcb.subsample, hadasch.lettuce, hadasch.lettuce.markers, jones.corn.uniformity, petersen.sorghum.cowpea, woodman.pig

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    Other notes

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    • Re-named mead.cowpeamaize to mead.cowpea.maize.

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    • Re-named correa.soybean.uniformity to dasilva.soybean.uniformity.

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    • There are more than 200 URLs in the vignettes and these were slow to check and generated too many false warnings. Stopped the Rmd to html conversion from creating URL links.

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    agridat 1.18 - Jan 20212021-01-12

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    New data

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    damesa.maize, jayaraman.bamboo, nair.turmeric.uniformity, shafi.tomato.uniformity

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    agridat 1.17 - Jul 20202020-08-03

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    New data

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    alwan.lamb, baker.strawberry.uniformity, besag.checks, bryan.corn.uniformity, davidian.soybean, devries.pine, edwards.oats, george.wheat, hartman.tomato.uniformity, heath.raddish.uniformity, johnson.douglasfir, kayad.alfalfa, kerr.sugarcane.uniformity, laycock.tea.uniformity, lehmann.millet.uniformity, linder.wheat, loesell.bean.uniformity, miguez.biomass, obsi.potato.uniformity, paez.coffee.uniformity, pederson.lettuce.repeated, piepho.barley.uniformity, rothamsted.oats, shaw.oats, wyatt.multi.uniformity

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    Other notes

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    • Examples using asreml() now asreml version 4.

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    • Re-named hutchinson.cotton.uniformity to panse.cotton.uniformity.

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    • Added INLA example to crowder.seeds.

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    • Wrapped most example sections with dontrun.

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    • New function libs(), which is basically the same as pacman::p_load(), but without the dependency on pacman.

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    • Example sections now use library(agridat) and then libs() to load all other packages on the fly if needed.

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    • Website built with pkgdown.

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    agridat 1.16 - Jul 20182018-07-06

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    • Minor release to make a small change for the next release of the broom package.
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    agridat 1.15 - Jun 20182018-06-27

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    New data

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    ansari.wheat.uniformity, baker.wheat.uniformity, bancroft.peanut.uniformity, bose.multi.uniformity, christidis.cotton.uniformity, dasilva.soybean.uniformity, davies.pasture.uniformity, eden.tea.uniformity, hutchinson.cotton.uniformity, igue.sugarcane.uniformity, kulkarni.sorghum.uniformity, lander.multi.uniformity, lord.rice.uniformity, magistad.pineapple.uniformity, nagai.strawberry.uniformity, narain.sorghum.uniformity, robinson.peanut.uniformity, sayer.sugarcane.uniformity, strickland.apple.uniformity, strickland.grape.uniformity, strickland.peach.uniformity, strickland.tomato.uniformity

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    dasilva.maize, mead.turnip

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    Other notes

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    • nonnecke.corn.uniformity is renamed nonnecke.sweetcorn.uniformity

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    • moore.carrots.uniformity is renamed moore.carrot.uniformity

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    • Switch from lsmeans to emmeans in examples.

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    -

    agridat 1.13 - Nov 20172017-11-29

    -
    -

    New data for uniformity trials

    -

    bradley.multi.uniformity, christidis.wheat.uniformity, day.wheat.uniformity, draper.safflower.uniformity, holtsmark.timothy.uniformity, iyer.wheat.uniformity, kadam.millet.uniformity, kalamkar.wheat.uniformity, khin.rice.uniformity, kiesselbach.oats.uniformity, kristensen.barley.uniformity, lessman.sorghum.uniformity, love.cotton.uniformity, masood.rice.uniformity, mcclelland.corn.uniformity, montgomery.wheat.uniformity, moore.polebean.uniformity, moore.bushbean.uniformity, moore.sweetcorn.uniformity, moore.carrot.uniformity, moore.springcauliflower.uniformity, moore.fallcauliflower.uniformity, nonnecke.corn.uniformity, nonnecke.peas.uniformity, parker.orange.uniformity, polson.safflower.uniformity, sawyer.multi.uniformity, smith.beans.uniformity, stickler.sorghum.uniformity, wiedemann.safflower.uniformity

    -
    -
    -

    New data for stability

    -

    fisher.barley, lu.stability, tai.potato

    -
    -
    -

    New data

    -

    acorsi.grayleafspot, becker.chicken, chinloy.fractionalfactorial, cramer.cucumber, crampton.pig, battese.survey, christidis.competition, depalluel.sheep. eden.nonnormal, gartner.corn, giles.wheat, gomez.heteroskedastic, gomez.nonnormal1, gomez.nonnormal2, gomez.nonnormal3, gomez.wetdry, goulden.eggs, goulden.splitsplit, gregory.cotton, hanover.whitepine, harvey.lsmeans, harville.lamb, huehn.wheat, kenward.cattle, kreusler.maize, lehner.soybeanmold, lillemo.wheat, lin.superiority, lin.unbalanced, little.splitblock, mead.lambs, omer.sorghum, onofri.winterwheat, reid.grasses, silva.cotton, urquhart.feedlot, usgs.herbicides, vaneeuwijk.fusarium, vaneeuwijk.nematodes, vaneeuwijk.drymatter, wheatley.carrot

    -
    -
    -

    Other notes

    -

    • The following data objects have been changed from lists of 2 matrices into tidy dataframes: aastveit.barley, box.cork, ortiz.tomato, talbot.potato, vargas.txe, vargas.wheat1, vargas.wheat2

      • -

    • Moved desplot() function to package desplot.

      • -

    • Moved gge() function to package gge.

      • -

    • Changed license to CC BY-SA 4.0, file LICENSE.note (similar to igraphdata).

      • -

    • Titles in Rd pages are more consistent.

      • -

    • Checked aspect ratio of field plot heatmaps where possible.

      • -

    • devtools::run_examples() now works even without suggested packages installed.

      • -

    • Changed extensions in inst/files from .bug to .jag .

      • -

    • Added DOIs for most source references.

      • -

    • Changed all hyperlinks to plain text (for faster checking, avoids re-direct errors).

      • -

    • The nass.* datasets now have state names with correct spaces. e.g. NewYork to New York.

      • -

    • Replaced use of gam package in examples, in favor of mgcv package.

      • -

    • The mead.lambs data is renamed to mead.lamb for consistency.

      • -

    • Added package logo on github.

      • -
    -
    -
    -

    agridat 1.12 - Jun 20152015-06-29

    -
    -

    New data

    -

    cochran.beets, cochran.lattice, jansen.apple, jansen.carrot

    -
    -
    -
    -

    agridat 1.11 - Mar 20152015-03-03

    -
    -

    New data

    -

    besag.beans, besag.triticale, burgeuno.alpha, burgueno.rowcol, burgueno.unreplicated, steptoe.morex.pheno

    -
    -
    -

    Other

    -
    • Removed asreml package from Suggests (due to CRAN check problems).
      • -
    -
    -
    -

    agridat 1.10 - Nov 20142014-11-26

    -
    -

    New data

    -

    beaven.barley, perry.springwheat, ridout.appleshoots

    -
    -
    -

    Other

    -
    • Move packages from Depends to Imports.
      • -
    -
    -
    -

    agridat 1.9 - Jul 20142014-07-02

    -
    -

    New data

    -

    beall.webworms, besag.endive, brandt.switchback, butron.maize, carlson.germination, cochran.factorial, connolly.potato, cornelius.maize, cullis.earlygen, fisher.latin, foulley.calving, fox.wheat, gomez.splitplot.subsample, goulden.latin, gumpertz.pepper, harrison.priors, hazell.vegetables, heady.fertilizer, holland.arthropods, hunter.corn, jansen.strawberry, kalamkar.potato.uniformity, kang.maize, kang.peanut, karcher.turfgrass, keen.potatodamage, lasrosas.corn, lee.potatoblight, lonnquist.maize, lucas.switchback, maindonald.barley, mead.cauliflower, mercer.mangold.uniformity, patterson.switchback, piepho.cocksfoot, sinclair.clover, snijders.fusarium, stirret.borers, theobald.barley, turner.herbicide, vargas.txe, vold.longterm, wallace.iowaland, walsh.cottonprice, wassom.brome.uniformity, welch.bermudagrass, weiss.incblock, weiss.lattice, yang.barley

    -
    -
    -

    Other

    -

    • Use if(require(lme4)) in examples. B.Ripley request.

      • -

    • All data (almost) now have an example graphic.

      • -
    -
    -
    -

    agridat 1.8 - Sep 20132013-09-23

    -
    -

    New data

    -

    brandle.rape, salmon.bunt

    -
    -
    -
    -

    agridat 1.7 - Sep 20132013-09-06

    -
    -

    New data

    -

    baker.barley.uniformity, bliss.borers, bond.diallel, harris.wateruse, hayman.tobacco, holshouser.splitstrip, pearce.apple, waynick.soil

    -
    -
    -
    -

    agridat 1.6 - June 20132013-06-04

    -
    -

    New data

    -

    crossa.wheat, garber.multi.uniformity, gomez.nitrogen, harris.multi.uniformity, hughes.grapes, li.millet.uniformity, odland.soybean.uniformity, odland.soyhay.uniformity, ratkowsky.onions, stephens.sorghum.uniformity

    -
    -
    -
    -

    agridat 1.5 - Apr 20132013-04-26

    -
    -

    New data

    -

    adugna.sorghum, ars.earlywhitecorn96, besag.bayesian, box.cork, broadbalk.wheat, byers.apple, caribbean.maize, carmer.density, cate.potassium, cleveland.soil, cochran.eelworms, cochran.wireworms, fan.stability, gomez.seedrate, gotway.hessianfly, goulden.barley.uniformity, henderson.milkfat, hernandez.nitrogen, hessling.argentina, immer.sugarbeet.uniformity, ivins.herbs, jenkyn.mildew, johnson.blight, lambert.soiltemp, lavoranti.eucalyptus, lyon.potato.uniformity, lyons.wheat, mead.cowpeamaize, mead.germination, minnesota.barley.weather, minnesota.barley.yield, nebraska.farmincome, nass.barley, nass.corn, nass.cotton, nass.hay, nass.rice, nass.sorghum, nass.soybean, nass.wheat, ortiz.tomato, pacheco.soybean, senshu.rice, snedecor.asparagus, streibig.competition, zuidhof.broiler

    -
    -
    -
    -

    agridat 1.4 - Mar 20122012-03-14

    -
    -

    New data

    -

    archbold.apple, blackman.wheat, cochran.crd, cochran.latin, darwin.maize, denis.ryegrass, digby.jointregression, engelstad.nitro, federer.diagcheck, gilmour.slatehall, john.alpha, ilra.sheep, kempton.slatehall, ryder.groundnut, vsn.lupin3

    -
    -
    -
    -

    agridat 1.3 - Sep 20112011-10-20

    -
    -

    New data

    -

    bridges.cucumber, cox.stripsplit, diggle.cow, eden.potato, gauch.soy, graybill.heteroskedastic, hildebrand.systems, hanks.sprinkler, mcconway.turnip, pearl.kernels, williams.barley.uniformity, williams.cotton.uniformity

    -
    -
    -
    -

    agridat 1.2 - Jun 20112011-06-30

    -
    -

    New data

    -

    aastveit.barley, gathmann.bt, kempton.competition, wedderburn.barley

    -
    -
    -
    -

    agridat 1.0 - Apr 2011

    -
    -

    First release to CRAN

    -
    -
    -

    New data

    -

    allcroft.lodging, australia.soybean, batchelor.apple.uniformity, batchelor.lemon.uniformity, batchelor.navel1.uniformity, batchelor.navel2.uniformity, batchelor.valencia.uniformity, batchelor.walnut.uniformity, besag.elbatan, besag.met, cochran.bib, corsten.interaction, crowder.germination, denis.missing, durban.competition, durban.rowcol, durban.splitplot, federer.tobacco, gilmour.serpentine, gomez.fractionalfactorial, gomez.groupsplit, gomez.multilocsplitplot, gomez.splitsplit, gomez.stripplot, gomez.stripsplitplot, gomez.rice.uniformity, hughes.grapes, kempton.rowcol, kempton.barley.uniformity, mead.strawberry, mercer.wheat.uniformity, rothamsted.brussels, shafii.rapeseed, smith.uniformity3, stroup.nin, stroup.splitplot, student.barley, talbot.potato, theobald.covariate, thompson.cornsoy, vargas.wheat1, vargas.wheat2, verbyla.lupin, williams.trees, wiebe.wheat.uniformity, yan.winterwheat, yates.missing, yates.oats

    -
    -
    -
    -

    agridat 0.0 - Oct 2010

    -
    • Development begins
      • -
    -
    - - - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/notes_agridat.html b/docs/notes_agridat.html deleted file mode 100644 index c8e462b5..00000000 --- a/docs/notes_agridat.html +++ /dev/null @@ -1,293 +0,0 @@ - -wanted • agridat - - -
    -
    - - - -
    -
    -
    -

    wanted

    -
    - - -

    % agridat notes

    -
    -

    To do

    -
    • Figure out best way to use jags
      • -
    • JAGS code for edwards.oats
      • -
    • JAGS code for lee.potatoblight
      • -
    • JAGS code for theobald.barley
      • -
    • JAGS code for besag.elbatan
      • -
    • Note: R_MAX_NUM_DLLS=150
      • -
    • Rcmd check –run-dontrun before release devtools::run_examples(run=FALSE, start=“butron.maize”) build_site(lazy=TRUE, run=TRUE)
      • -

    Do NOT use Roxygen to document data, because it will complain about data/*.txt files with error messages like: Error: ‘uscrime’ is not an exported object from ‘namespace:agridat’

    -

    This document lists notes about data sources searched and additional sources of agricultural data. Although this is an .md file, the formatting is best viewed in plain text mode.

    -

    Henry Wallace archive at Univ Iowa Library http://www.lib.uiowa.edu/sc/location-and-hours/ Henry Wallace research papers http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400608 500 Ear experiment. Might be the data for “What is in the corn judge’s mind” paper http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400615

    -

    rpathanalysis http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/

    -
    -
    - -
    • Plant-level data for distinctness, uniformity and stability testing. Most published data is for plot-means. This page mentions an R version for DUS: http://www.bioss.ac.uk/knowledge/software.html -
      • -
    • Yield-monitor data for a split-planter field
      • -
    • Yield-monitor data for a strip trial.
      • -
    -
    -

    stability

    -
    -
    -

    done

    -

    adugna.sorghum 28g,13l,5y brandle.rape 5g,9l,3y denis.missing 5g,26e digby.jointregression 10g,17l fan.stability 13g,10l,2y hildebrand.systems huehn.wheat 20g,10e lu.stability 5g,6e pieopho.cocksfoot 25g,7y williams.trees 37g,6l

    -
    -
    -

    todo ?

    -

    kamidi 11g,7l lin.balanced 33g,12l lin.unbalanced 33g,18l

    -

    Jose Crossa papers http://repository.cimmyt.org/xmlui/handle/10883/1/browse?value=Crossa,%20J.&type=author Meta-r http://repository.cimmyt.org/xmlui/handle/10883/4130 Data http://repository.cimmyt.org/xmlui/handle/10883/4036 http://repository.cimmyt.org/xmlui/handle/10883/2976 http://repository.cimmyt.org/xmlui/handle/10883/1380 http://repository.cimmyt.org/xmlui/handle/10883/4128 http://repository.cimmyt.org/xmlui/handle/10883/4290

    -

    Review of meta-analyses in agronomy http://www6.versailles-grignon.inra.fr/agronomie/Meta-analysis-in-agronomy/References

    -

    Richard Plant http://www.plantsciences.ucdavis.edu/plant/qgislabs.htm CC-SA license

    -
    -
    -

    Papers

    -

    Biplot/StdErr/ VanEeuwijk 1993 - Incorporating env info Biplot Bartkowiak GxE table GE folder: Crossa 1997

    -

    Stability folder Fan 2007 - Yield stability of maize GxE Flores 1998 Flores 1998 - bean & pea yields, Flores 2012, Flores 2013

    -
    -

    Malosetti 2013

    -

    F2 data. Folder: GE http://www.frontiersin.org/Plant_Physiology/10.3389/fphys.2013.00044/abstract CC license:

    -
    -
    -

    Oakey, Cullis, Thompson

    -

    https://www.g3journal.org/content/6/5/1313 http://www.g3journal.org/content/6/5/1313/suppl/DC1

    -
    -
    -

    Perez

    -

    Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat https://www.scienceopen.com/document/vid/4017fb51-381c-4374-93aa-608423df4004;jsessionid=0TLjjSbaooSUk1y3JKd4nUeb.master:so-app1-prd Data: http://www.g3journal.org/content/suppl/2012/12/05/2.12.1595.DC1 All content has CC license

    -
    -
    -

    Carley et al (2018).

    -

    Genetic Covariance of Environments in the Potato National Chip Processing Trial https://dl.sciencesocieties.org/publications/cs/articles/59/1/107

    -
    -
    -

    Endelman et al (2018)

    -

    Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato https://www.genetics.org/content/209/1/77

    -
    -
    -
    -

    Other

    -
    -
    -

    Extension

    -

    http://articles.extension.org/plant_breeding_genomics Many interesting pages. License is not clear, but developed with help of USDA.

    -
    1. http://articles.extension.org/pages/68660/plant-breeding-and-genomics-learning-lessons
      2. -
    2. http://articles.extension.org/pages/60430/introduction-to-the-augmented-experimental-design-webinar
      3. -
    3. http://articles.extension.org/pages/61006/estimating-heritability-and-blups-for-traits-using-tomato-phenotypic-data
      4. -
    4. -http://articles.extension.org/pages/68019/genomic-relationships-and-gblup <— review this
      5. -

    Introduction to the rrBLUP Package in R for Genomewide Selection Webinar http://pbgworks.org/node/1440

    -
    -
    -

    Items below have been reviewed for data sources

    -
    -
    -

    Journals

    -
    -

    Agronomy Journal

    -

    Skimmed Vol 1

    -
    -
    -

    The American Statistician

    -

    Vol 1-13

    -
    -
    -

    Biometrics. Skimmed 1947-2006

    -

    http://www.jstor.org/action/showPublication?journalCode=biometrics

    -
    -
    -

    The Empire Journal of Experimental Agriculture

    -

    http://archive.org Vol 3-5, 23-24 26

    -
    -
    -

    Field Crops Research.

    -

    http://www.sciencedirect.com/science/journal/03784290/157 Vol 1-40

    -
    -
    -

    IASRI newsletters

    -

    http://www.iasri.res.in/NewsLetters/nl.HTM

    -
    -
    -

    Indian Journal of Agricultural Science

    -
    Vol 1. 309
-Vol 2. 53 694
-Vol 3. 544 5 varieties, 2 blocks, 4 reps/block
-Vol 4. 
-Vol 5. 579. Multi-year uniformity trial. agridat::bose.multi.uniformity
-Vol 6. Pg 85 has 4-way factorial with whole-plot date, but non-contiguous sub-plots.
-Vol 9.
-Vol 10.
-Vol 11. 
-Vol 12. 240. Wheat uniformity trial. agridat::iyer.wheat.uniformity
-Vol 14. 315 
-Vol 16.
-Vol 17.
-Vol 19.
    -
    -
    -

    JABES

    -

    Vol 6.

    -
    -
    -

    Journal of the American Society of Agronomy

    -

    Vol 23.

    -
    -
    -

    Journal of the Indian Society of Agricultural Statistics

    -

    http://www.isas.org.in/jsp/onlinejournal.jsp Skimmed: Vol 50-56

    -
    -
    -

    JRSSA

    -
    -
    -

    JRSSB 1940-1997

    -

    http://www.jstor.org/action/showPublication?journalCode=jroyastatsocise4 Datasets 1998-2015 http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868/homepage/seriesb_datasets.htm http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm

    -
    -
    -

    JRSSC Applied Statistics datasets

    -

    http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm 1998-2015

    -
    -
    -

    Tidsskrift for Planteavl 1895-1992

    -

    http://dca.au.dk/publikationer/historiske/planteavl/

    -
    -
    -
    -

    Books

    -
    -

    Nebraska Agricultural Experiment Station Annual Report

    -

    Vol 19-24, 1906-1911 https://books.google.com/books?id=HBlJAAAAMAAJ Vol 25, 1912 https://books.google.com/books?id=M-5BAQAAMAAJ

    -
    -
    -

    Iowa State University Library Special Collections

    -

    Helen Elizabeth Conners (1951). Field plot techniques for sweet potatoes obtained from uniformity trial data. Master’s Thesis. No data given.

    -

    Robert LeRoy Plaisted (1954). Field plot techniques for estimating onion yields. Master’s Thesis. No data given.

    -

    Michael Holle. 1960. Plot technique for field evaluation of three characters in the lima bean. Master’s Thesis. No data given.

    -

    Howard Lewis Taylor (1951). The effect of plot shape on experimental error. Master’s Thesis. No data given. Used data from a corn uniformity trial, oats uniformity, and the data of Fairfield Smith. Smaller experimental errors were found for long narrow plots.

    -
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    -
    -

    Classes

    -
    -

    Hernandez

    -

    http://www.soils.umn.edu/academics/classes/soil4111/hw/ Available on Wayback. Yield monitor data for two fields with soils layer.

    -
    -
    -

    Jack Weiss

    -

    Ecol 563 Stat Meth in Ecology http://www.unc.edu/courses/2010fall/ecol/563/001/ Env Studies 562 Stat for Envt Science http://www.unc.edu/courses/2010spring/ecol/562/001/ Ecol 145 http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures.htm

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    Journals / Proceedings

    -
    -

    Applied Statistics in Agriculture

    -

    http://newprairiepress.org/agstatconference/ 1989-2014

    -
    -
    -

    Computers and Electronics in Agriculture.

    -

    http://www.sciencedirect.com/science/journal/01681699/103 Vol 1-110 180-191

    -
    -
    -

    Journal of Agricultural Science

    -

    https://www.cambridge.org/core/journals/journal-of-agricultural-science/all-issues 1900-2016

    -
    -
    -

    Experimental Agriculture

    -

    https://www.cambridge.org/core/journals/experimental-agriculture 1965-2016

    -
    -
    -

    SAS Global Forum

    -

    http://support.sas.com/events/sasglobalforum/previous/online.html 22-31, 2007-2013

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    -
    - - -
    - - - -
    - - - -
    -

    Developed by Kevin Wright.

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    Site built with pkgdown 2.0.1.

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    - - - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 80ea5b83..00000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,384 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; 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- - - - - - - -Barley heights and environmental covariates in Norway — aastveit.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Barley heights and environmental covariates in Norway

    - - -
    - -
    -

    Average height for 15 genotypes of barley in each of 9 years. - Also 19 covariates in each of the 9 years.

    -
    - - 
    data("aastveit.barley.covs")
-data("aastveit.barley.height")
    - - -

    Format

    - - - -

    The 'aastveit.barley.covs' dataframe has 9 observations on the following 20 variables.

    -
    year

    year

    -
    R1

    avg rainfall (mm/day) in period 1

    -
    R2

    avg rainfall (mm/day) in period 2

    -
    R3

    avg rainfall (mm/day) in period 3

    -
    R4

    avg rainfall (mm/day) in period 4

    -
    R5

    avg rainfall (mm/day) in period 5

    -
    R6

    avg rainfall (mm/day) in period 6

    -
    S1

    daily solar radiation (ca/cm^2) in period 1

    -
    S2

    daily solar radiation (ca/cm^2) in period 2

    -
    S3

    daily solar radiation (ca/cm^2) in period 3

    -
    S4

    daily solar radiation (ca/cm^2) in period 4

    -
    S5

    daily solar radiation (ca/cm^2) in period 5

    -
    S6

    daily solar radiation (ca/cm^2) in period 6

    -
    ST

    sowing date

    -
    T1

    avg temp (deg Celsius) in period 1

    -
    T2

    avg temp (deg Celsius) in period 2

    -
    T3

    avg temp (deg Celsius) in period 3

    -
    T4

    avg temp (deg Celsius) in period 4

    -
    T5

    avg temp (deg Celsius) in period 5

    -
    T6

    avg temp (deg Celsius) in period 6

    -
    value

    value of the covariate

    - -
    - -

    The 'aastveit.barley.height' dataframe has 135 observations on the - following 3 variables.

    -
    year

    year, 9

    -
    gen

    genotype, 15 levels

    -
    height

    height (cm)

    - -
    - - -

    Details

    - - - -

    Experiments were conducted at As, Norway.

    -

    The height dataframe contains average plant height (cm) of 15 varieties - of barley in each of 9 years.

    -

    The growth season of each year was divided into eight periods from - sowing to harvest. Because the plant stop growing about 20 days after - ear emergence, only the first 6 periods are included here.

    -

    Used with permission of Harald Martens.

    -

    Source

    - -

    Aastveit, A. H. and Martens, H. (1986). - ANOVA interactions interpreted by partial least squares regression. - Biometrics, 42, 829--844. - https://doi.org/10.2307/2530697

    -

    References

    - -

    J. Chadoeuf and J. B. Denis (1991). - Asymptotic variances for the multiplicative interaction model. - J. App. Stat., 18, 331-353. - https://doi.org/10.1080/02664769100000032

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data("aastveit.barley.covs")
-data("aastveit.barley.height")
-
-libs(reshape2, pls)
-
    #> 
-#> Attaching package: 'pls'
    #> The following object is masked from 'package:stats':
-#> 
-#>     loadings
      
-  # First, PCA of each matrix separately
-
-  Z <- acast(aastveit.barley.height, year ~ gen, value.var="height")
-  Z <- sweep(Z, 1, rowMeans(Z))
-  Z <- sweep(Z, 2, colMeans(Z)) # Double-centered
-  sum(Z^2)*4 # Total SS = 10165
-
    #> [1] 10165.55
      sv <- svd(Z)$d
-  round(100 * sv^2/sum(sv^2),1) # Prop of variance each axis
-
    #> [1] 74.4  7.9  5.0  4.8  3.6  2.1  1.4  0.7  0.0
      # Aastveit Figure 1.  PCA of height
-  biplot(prcomp(Z),
-         main="aastveit.barley - height", cex=0.5)
-
      
-  U <- aastveit.barley.covs
-  rownames(U) <- U$year
-  U$year <- NULL
-  U <- scale(U) # Standardized covariates
-  sv <- svd(U)$d
-  # Proportion of variance on each axis
-  round(100 * sv^2/sum(sv^2),1)
-
    #> [1] 33.5 18.4 16.5 13.9  8.8  4.4  2.4  2.1  0.0
    
-  # Now, PLS relating the two matrices
-  m1 <- plsr(Z~U)
-  loadings(m1)
-
    #> 
-#> Loadings:
-#>    Comp 1 Comp 2 Comp 3 Comp 4 Comp 5 Comp 6 Comp 7 Comp 8
-#> R1 -0.131  0.232 -0.345  0.411         0.140 -0.157  0.130
-#> R2  0.141        -0.139  0.549 -0.343         0.148       
-#> R3                0.505  0.231 -0.125        -0.163       
-#> R4  0.358               -0.174  0.123  0.103 -0.182  0.420
-#> R5  0.152  0.425  0.150         0.141  0.141  0.495 -0.300
-#> R6  0.230 -0.362 -0.211  0.173  0.148 -0.129              
-#> S1  0.172 -0.427  0.191                0.331  0.124       
-#> S2        -0.122        -0.509  0.281 -0.554  0.106       
-#> S3        -0.135 -0.388 -0.226  0.230  0.297        -0.448
-#> S4 -0.284 -0.290 -0.238        -0.157                     
-#> S5 -0.345 -0.206                       0.250 -0.320 -0.138
-#> S6 -0.349  0.249                       0.138  0.132 -0.120
-#> ST  0.299 -0.117               -0.417         0.110 -0.239
-#> T1  0.243 -0.169  0.316  0.175 -0.209  0.196  0.106 -0.271
-#> T2  0.156  0.197        -0.353 -0.440 -0.261 -0.217       
-#> T3  0.318 -0.144 -0.244                       0.239  0.410
-#> T4 -0.110 -0.315        -0.201 -0.482  0.151  0.266  0.143
-#> T5 -0.239 -0.105  0.331 -0.105        -0.388  0.274  0.325
-#> T6 -0.267  0.170 -0.131  0.331         0.318  0.467  0.184
-#> 
-#>                Comp 1 Comp 2 Comp 3 Comp 4 Comp 5 Comp 6 Comp 7 Comp 8
-#> SS loadings     1.025  1.024  1.020  1.230  1.013  1.055  1.000  1.000
-#> Proportion Var  0.054  0.054  0.054  0.065  0.053  0.056  0.053  0.053
-#> Cumulative Var  0.054  0.108  0.162  0.226  0.280  0.335  0.388  0.440
      # Aastveit Fig 2a (genotypes), but rotated differently
-  biplot(m1, which="y", var.axes=TRUE)
-
      # Fig 2b, 2c (not rotated)
-  biplot(m1, which="x", var.axes=TRUE)
-
    
-  # Adapted from section 7.4 of Turner & Firth,
-  # "Generalized nonlinear models in R: An overview of the gnm package"
-  # who in turn reproduce the analysis of Chadoeuf & Denis (1991),
-  # "Asymptotic variances for the multiplicative interaction model"
-
-  libs(gnm)
-
      dath <- aastveit.barley.height
-  dath$year = factor(dath$year)
-
-  set.seed(42)
-  m2 <- gnm(height ~ year + gen + Mult(year, gen), data = dath)
-
    #> Initialising
-#> Running start-up iterations..
-#> Running main iterations........
-#> Done
      # Turner: "To obtain parameterization of equation 1, in which sig_k is the
-  # singular value for component k, the row and column scores must be constrained
-  # so that the scores sum to zero and the squared scores sum to one.
-  # These contrasts can be obtained using getContrasts"
-  gamma <- getContrasts(m2, pickCoef(m2, "[.]y"),
-                        ref = "mean", scaleWeights = "unit")
-  delta <- getContrasts(m2, pickCoef(m2, "[.]g"),
-                        ref = "mean", scaleWeights = "unit")
-  # estimate & std err
-  gamma <- gamma$qvframe
-  delta <- delta$qvframe
-  # change sign of estimate
-  gamma[,1] <- -1 * gamma[,1]
-  delta[,1] <- -1 * delta[,1]
-  # conf limits based on asymptotic normality, Chadoeuf table 8, p. 350, 
-  round(cbind(gamma[,1], gamma[, 1] +
-                           outer(gamma[, 2], c(-1.96, 1.96))) ,3)  
-
    #>         [,1]   [,2]   [,3]
-#>  [1,] -0.227 -0.337 -0.116
-#>  [2,] -0.514 -0.609 -0.418
-#>  [3,] -0.439 -0.539 -0.339
-#>  [4,]  0.200  0.090  0.311
-#>  [5,]  0.533  0.440  0.627
-#>  [6,] -0.142 -0.254 -0.030
-#>  [7,]  0.332  0.226  0.439
-#>  [8,]  0.176  0.064  0.287
-#>  [9,]  0.079 -0.034  0.192
      round(cbind(delta[,1], delta[, 1] +
-                           outer(delta[, 2], c(-1.96, 1.96))) ,3)
-
    #>         [,1]   [,2]   [,3]
-#>  [1,] -0.079 -0.195  0.037
-#>  [2,] -0.008 -0.124  0.109
-#>  [3,] -0.170 -0.284 -0.055
-#>  [4,]  0.047 -0.069  0.163
-#>  [5,] -0.181 -0.296 -0.067
-#>  [6,] -0.129 -0.245 -0.014
-#>  [7,] -0.014 -0.130  0.103
-#>  [8,]  0.422  0.318  0.527
-#>  [9,]  0.271  0.159  0.382
-#> [10,]  0.335  0.226  0.444
-#> [11,]  0.376  0.269  0.483
-#> [12,] -0.285 -0.396 -0.174
-#> [13,]  0.168  0.054  0.283
-#> [14,] -0.293 -0.404 -0.182
-#> [15,] -0.460 -0.563 -0.358
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/acorsi.grayleafspot-1.png b/docs/reference/acorsi.grayleafspot-1.png deleted file mode 100644 index 27ff6c3e..00000000 Binary files a/docs/reference/acorsi.grayleafspot-1.png and /dev/null differ diff --git a/docs/reference/acorsi.grayleafspot-2.png b/docs/reference/acorsi.grayleafspot-2.png deleted file mode 100644 index bcceed92..00000000 Binary files a/docs/reference/acorsi.grayleafspot-2.png and /dev/null differ diff --git a/docs/reference/acorsi.grayleafspot.html b/docs/reference/acorsi.grayleafspot.html deleted file mode 100644 index 58408f0b..00000000 --- a/docs/reference/acorsi.grayleafspot.html +++ /dev/null @@ -1,250 +0,0 @@ - -Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot • agridat - - -
    -
    - - - -
    -
    -
    -

    Multi-environment trial evaluating 36 maize genotypes in 9 locations

    - - -
    - -
    -

    Multi-environment trial evaluating 36 maize genotypes in 9 locations

    -
    - -
    - 
    data("acorsi.grayleafspot")
    -
    - -
    -

    Format

    -

    A data frame with 324 observations on the following 3 variables.

    gen
    -

    genotype, 36 levels

    - -
    env
    -

    environment, 9 levels

    - -
    rep
    -

    replicate, 2 levels

    - -
    y
    -

    grey leaf spot severity

    - - -
    -
    -

    Details

    -

    Experiments conducted in 9 environments in Brazil in 2010-11. Each - location had an RCB with 2 reps.

    -

    The response variable is the percentage of leaf area affected by gray - leaf spot within each experimental unit (plot).

    -

    Acorsi et al. use this data to illustrate the fitting of a generalized - AMMI model with non-normal data.

    -
    -
    -

    Source

    -

    C. R. L. Acorsi, T. A. Guedes, M. M. D. Coan, R. J. B. Pinto, - C. A. Scapim, C. A. P. Pacheco, P. E. O. Guimaraes, C. R. Casela. (2016). - Applying the generalized additive main effects and multiplicative - interaction model to analysis of maize genotypes resistant to grey leaf spot. - Journal of Agricultural Science. - https://doi.org/10.1017/S0021859616001015

    -

    Electronic data and R code kindly provided by Marlon Coan.

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(acorsi.grayleafspot)
-  dat <- acorsi.grayleafspot
-  
-  # Acorsi figure 2. Note: Acorsi used cell means
-  op <- par(mfrow=c(2,1), mar=c(5,4,3,2))
-  libs(lattice)
-  boxplot(y ~ env, dat, las=2,
-          xlab="environment", ylab="GLS severity")
-  title("acorsi.grayleafspot")
-  boxplot(y ~ gen, dat, las=2,
-          xlab="genotype", ylab="GLS severity")
-
-  par(op)
-  
-  # GLM models
-  
-  # glm main-effects model with logit u(1-u) and wedderburn u^2(1-u)^2
-  # variance functions
-  # glm1 <- glm(y~ env/rep + gen + env, data=dat, family=quasibinomial)
-  # glm2 <- glm(y~ env/rep + gen + env, data=dat, family=wedderburn)
-  # plot(glm2, which=1); plot(glm2, which=2)
-  
-  # GAMMI models of Acorsi. See also section 7.4 of Turner
-  # "Generalized nonlinear models in R: An overview of the gnm package"
-  
-  # full gnm model with wedderburn, seems to work
-  libs(gnm)
-#> 
-#> Attaching package: 'gnm'
-#> The following object is masked from 'package:lattice':
-#> 
-#>     barley
-  set.seed(1)
-  gnm1 <- gnm(y ~  env/rep + env + gen + instances(Mult(env,gen),2),
-              data=dat,
-              family=wedderburn, iterMax =800)
-#> Initialising
-#> Running start-up iterations..
-#> Running main iterations.........................................................
-#> ................................................................................
-#> ................................................................................
-#> ................
-#> Done
-  deviance(gnm1) # 433.8548
-#> [1] 433.8548
-  # summary(gnm1)
-  # anova(gnm1, test ="F")  # anodev, Acorsi table 4
-  ##                          Df Deviance Resid. Df Resid. Dev       F    Pr(>F)    
-  ## NULL                                       647     3355.5                      
-  ## env                       8  1045.09       639     2310.4 68.4696 < 2.2e-16 ***
-  ## env:rep                   9    12.33       630     2298.1  0.7183    0.6923    
-  ## gen                      35  1176.23       595     1121.9 17.6142 < 2.2e-16 ***
-  ## Mult(env, gen, inst = 1) 42   375.94       553      745.9  4.6915 < 2.2e-16 ***
-  ## Mult(env, gen, inst = 2) 40   312.06       513      433.9  4.0889 3.712e-14 ***
-
-
-  # maybe better, start simple and build up the model
-  gnm2a <- gnm(y ~  env/rep + env + gen,
-               data=dat,
-               family=wedderburn, iterMax =800)
-
-  # add first interaction term
-  res2a <- residSVD(gnm2a, env, gen, 2)
-  gnm2b <- update(gnm2a, . ~ . + Mult(env,gen,inst=1),
-                  start = c(coef(gnm2a), res2a[, 1]))
-#> Running main iterations.........................................................
-#> ................................................................................
-#> .................................................................
-#> Done
-  deviance(gnm2b) # 692.19
-#> [1] 692.1921
-
-  # add second interaction term
-  res2b <- residSVD(gnm2b, env, gen, 2)
-  gnm2c <- update(gnm2b, . ~ . + Mult(env,gen,inst=1) + Mult(env,gen,inst=2),
-                  start = c(coef(gnm2a), res2a[, 1], res2b[,1]))
-#> Running main iterations.........................................................
-#> ................................................................................
-#> ................................................................................
-#> ................................................................................
-#> ................................................................................
-#> ....................
-#> Done
-  deviance(gnm2c) # 433.8548
-#> [1] 433.8548
-  # anova(gnm2c) # weird error message
-
-  # note, to build the ammi biplot, use the first column of res2a to get
-  # axis 1, and the FIRST column of res2b to get axis 2. Slightly confusing
-  emat <- cbind(res2a[1:9, 1], res2b[1:9, 1])
-  rownames(emat) <- gsub("fac1.", "", rownames(emat))
-  
-  gmat <- cbind(res2a[10:45, 1], res2b[10:45, 1])
-  rownames(gmat) <- gsub("fac2.", "", rownames(gmat))
-
-  # match Acorsi figure 4
-  biplot(gmat, emat, xlim=c(-2.2, 2.2), ylim=c(-2.2, 2.2), expand=2, cex=0.5,
-         xlab="Axis 1", ylab="Axis 2",
-         main="acorsi.grayleafspot - GAMMI biplot")
-
-# }
-
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/adugna.sorghum-1.png b/docs/reference/adugna.sorghum-1.png deleted file mode 100644 index c49fd768..00000000 Binary files a/docs/reference/adugna.sorghum-1.png and /dev/null differ diff --git a/docs/reference/adugna.sorghum.html b/docs/reference/adugna.sorghum.html deleted file mode 100644 index ad43aa56..00000000 --- a/docs/reference/adugna.sorghum.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of sorghum at 3 locations across 5 years

    - - -
    - -
    -

    Multi-environment trial of sorghum at 3 locations across 5 years

    -
    - - - -

    Format

    - -

    A data frame with 289 observations on the following 6 variables.

    -
    gen

    genotype, 28 levels

    -
    trial

    trial, 2 levels

    -
    env

    environment, 13 levels

    -
    yield

    yield kg/ha

    -
    year

    year, 2001-2005

    -
    loc

    location, 3 levels

    - -
    - -

    Details

    - -

    Sorghum yields at 3 locations across 5 years. - The trials were carried out at three locations in dry, hot lowlands of - Ethiopia:

    -

    Melkassa (39 deg 21 min E, 8 deg 24 min N)

    -

    Mieso (39 deg 22 min E, 8 deg 41 min N)

    -

    Kobo (39 deg 37 min E, 12 deg 09 min N)

    -

    Trial 1 was 14 hybrids and one open-pollinated variety.

    -

    Trial 2 was 12 experimental lines.

    -

    Used with permission of Asfaw Adugna.

    -

    Source

    - -

    Asfaw Adugna (2008). - Assessment of yield stability in sorghum using - univariate and multivariate statistical approaches. - Hereditas, 145, 28--37. - https://doi.org/10.1111/j.0018-0661.2008.2023.x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(adugna.sorghum)
-dat <- adugna.sorghum
-
-libs(lattice)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(yield ~ env*gen, data=dat, main="adugna.sorghum gxe heatmap",
-          col.regions=redblue)
-
    
-# Genotype means match Adugna
-tapply(dat$yield, dat$gen, mean)
-
    #>      G01      G02      G03      G04      G05      G06      G07      G08 
-#> 5080.125 4672.875 5043.500 4842.000 4704.250 4769.500 4523.625 4142.625 
-#>      G09      G10      G11      G12      G13      G14      G15      G16 
-#> 4443.500 4377.250 4950.125 4306.625 4390.625 4635.625 4285.000 3396.692 
-#>      G17      G18      G19      G20      G21      G22      G23      G24 
-#> 3392.154 3279.231 3833.154 3569.769 3968.538 3556.308 3758.538 3421.077 
-#>      G25      G26      G27      G28 
-#> 3383.308 3323.077 3171.000 3312.154 
    
-# CV for each genotype.  G1..G15 match, except for G2.
-# The table in Adugna scrambles the means for G16..G28
-libs(reshape2)
-mat <- acast(dat, gen~env,  value.var='yield')
-round(sqrt(apply(mat, 1, var, na.rm=TRUE)) / apply(mat, 1, mean, na.rm=TRUE) * 100,2)
-
    #>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
-#> 26.45 24.89 18.68 36.45 22.18 30.47 43.32 21.42 26.02 21.38 13.36 15.44 23.54 
-#>   G14   G15   G16   G17   G18   G19   G20   G21   G22   G23   G24   G25   G26 
-#> 18.05 26.87 43.77 40.23 42.02 51.50 43.33 40.99 42.73 36.48 54.29 39.62 39.96 
-#>   G27   G28 
-#> 39.51 42.60 
    
-# Shukla stability.  G1..G15 match Adugna.  Can't match G16..G28.
-dat1 <- droplevels(subset(dat, trial=="T1"))
-mat1 <- acast(dat1, gen~env,  value.var='yield')
-w <- mat1; k=15; n=8  # k=p gen, n=q env
-w <- sweep(w, 1, rowMeans(mat1, na.rm=TRUE))
-w <- sweep(w, 2, colMeans(mat1, na.rm=TRUE))
-w <- w + mean(mat1, na.rm=TRUE)
-w <- rowSums(w^2, na.rm=TRUE)
-sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1))
-round(sig2/10000,1) # Genotypes in T1 are divided by 10000
-
    #>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
-#>  15.6   7.0   9.2  74.2   3.6  28.3 133.5   9.6  14.2   9.7  44.9  67.9  23.0 
-#>   G14   G15 
-#>  28.3  20.2 
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/agridat.html b/docs/reference/agridat.html deleted file mode 100644 index 46860fdd..00000000 --- a/docs/reference/agridat.html +++ /dev/null @@ -1,140 +0,0 @@ - -Datasets from agricultural experiments — agridat • agridat - - -
    -
    - - - -
    -
    -
    -

    Datasets from agricultural experiments

    - - -
    - -
    -

    This package contains datasets from publications relating to agriculture, - including field crops, tree crops, animal studies, and a few others.

    -
    - - -
    -

    Details

    - - -

    If you use these data, please cite both the agridat package and the - original source of the data.

    -

    Abbreviations in the 'other' column include: - xy = coordinates, - pls = partial least squares, - rsm = response surface methodology, - row-col = row-column design, - ts = time series,

    -

    Uniformity trials with a single genotype

    -
    namedimensionsothermodel
    ansari.wheat.uniformity96 x 8xy
    baker.barley.uniformity3 x 19xy, 10 years
    baker.strawberry.uniformity10 x 50xy
    baker.wheat.uniformity12 x 12xy
    bancroft.peanut.uniformity6 x 18xy, 2 blocks
    batchelor.apple.uniformity8 x 28xy
    batchelor.lemon.uniformity14 x 16xy
    batchelor.navel1.uniformity20 x 50xy
    batchelor.navel2.uniformity15 x 33xy
    batchelor.valencia.uniformity12 x 20xy
    batchelor.walnut.uniformity10 x 28xy
    bose.multi.uniformity15 x 26xy, 3 years
    bradley.multi.uniformity10 x 11xy
    bryan.corn.uniformity6 x 48xy
    christidis.cotton.uniformity16 x 16xy, 4 blocks
    christidis.wheat.uniformity12 x 24xy
    dasilva.soybean.uniformity24 x 48xy
    davies.pasture.uniformity19 x 40xy
    day.wheat.uniformity31 x 100xy
    draper.safflower.uniformity18 x 16xy, 2 exptsmith
    eden.tea.uniformity12 x 12xy
    garber.multi.uniformity45 x 6xy, 2 years/crops
    gomez.rice.uniformity18 x 36xyaov,smith
    goulden.barley.uniformity20 x 20xy
    harris.multi.uniformity2 x 23xy, 23 cropscorrgram
    hartman.tomato.uniformity12 x 32xy
    heath.raddish.uniformity10 x 10xy
    holtsmark.timothy.uniformity6 x 40xy
    igue.sugarcane.uniformity36 x 42xy
    immer.sugarbeet.uniformity10 x 60xy, 3 traits
    iyer.wheat.uniformity25 x 80xy
    kadam.millet.uniformity8 x 20xy, 2 expts
    kalamkar.potato.uniformity6 x 96xy
    kalamkar.wheat.uniformity16 x 80xy, 2 traits
    kempton.barley.uniformity7 x 28xy
    kerr.sugarcane.uniformity12 x 12xy, 4 fields
    khin.rice.uniformity30 x 36xy
    kiesselbach.oats.uniformity3 x 69xy
    kristensen.barley.uniformity22 x 11xy
    kulkarni.sorghum.uniformity4 x 40xy, 3 years
    lander.multi.uniformity5 x 39xy, 4 years
    laycock.tea.uniformity6 x 9xy, 2 locations
    lehmann.millet.uniformity6 x 22xy, 3 years
    lessman.sorghum.uniformity46 x 60xy
    li.millet.uniformity6 x 100xy
    loesell.bean.uniformity21 x 90xy
    lord.rice.uniformity5 x 14xy, 8 fields
    love.cotton.uniformity16 x 10xy
    lyon.potato.uniformity34 x 6xy
    magistad.pineapple.uniformity5 x 5xy
    masood.rice.uniformity12 x 24xy
    mcclelland.corn.uniformity2 x 219xy
    mercer.mangold.uniformity10 x 20xy
    mercer.wheat.uniformity25 x 20xy, 2 traitsspplot
    montgomery.wheat.uniformity14 x 16xy, 2 yearslm
    moore.polebean.uniformity12 x 12xy
    moore.bushbean.uniformity24 x 24xy
    moore.sweetcorn.uniformity24 x 12xy
    moore.carrot.uniformity24 x 12xy
    moore.springcauliflower.uniformity12 x 20xy
    moore.fallcauliflower.uniformity12 x 20xy
    nagai.strawberry.uniformity18 x 24xy
    nagai.strawberry.uniformity18 x 24xy
    nair.turmeric.uniformity72 x 12xy
    narain.sorghum.uniformity10 x 16xy
    nonnecke.peas.uniformity15 x 18xy, 2 traits
    nonnecke.sweetcorn.uniformity32 x 18xy, 3 loc
    obsi.potato.uniformity63 x 26, 49 x 19xy
    odland.soybean.uniformity25 x 42xy
    odland.soyhay.uniformity28 x 55xy
    panse.cotton.uniformity32 x 40xy
    parker.orange.uniformity10 x 27xy, 6 yr
    paez.coffee.uniformity32 x 36xy, 5 yr
    piepho.barley.uniformity30 x 36xy
    polson.safflower.uniformity52 x 33xysmith
    robinson.peanut.uniformity16 x 36xy
    sawyer.multi.uniformity8 x 6xy, 3 year
    sayer.sugarcane.uniformity8 x 136, 8 x 121xy, 2 year
    shafi.tomato.uniformity10 x 20xy
    smith.beans.uniformity18 x 12, 16 x 15xy, 2 yr, 2 crops
    smith.corn.uniformity6 x 20xy, 3 yearsrgl
    stephens.sorghum.uniformity100 x 20xy
    stickler.sorghum.uniformity20 x 20xy, 4 expts, 2 years
    strickland.apple.uniformity11 x 18xy
    strickland.grape.uniformity5 x 31xy
    strickland.peach.uniformity8 x 18xy
    strickland.tomato.uniformity30 x 6xy
    wassom.brome.uniformity36 x 36xy, 3 expts
    wiebe.wheat.uniformity12 x 125xymedianpolish, loess
    wiedemann.safflower.uniformity54 x 33xysmith
    williams.barley.uniformity48 x 15xyloess
    williams.cotton.uniformity24 x 12xyloess
    wyatt.multi.uniformity43 x 3xy

    -

    Yield monitor

    namerepsyearstrtothermodel
    gartner.cornxy,ym
    lasrosas.corn326xy,ymlm
    kayad.alfalfa4xy,ym

    -

    Animals

    -
    namegenyearstrtothermodel
    alwan.lamb342ordinalclmm
    becker.chicken5,12heritabilitylmer
    crampton.pig52 covlm
    brandt.switchback102aov
    depalluel.sheep44latin
    diggle.cow4ts
    foulley.calvingordinalpolr
    goulden.eggscontrolchart
    harvey.lsmeans3,3lm
    harville.lamb5lmer
    henderson.milkfatnls,lm,glm,gam
    holland.arthropods5
    ilri.sheep46diallellmer, asreml
    kenward.cattle2asreml
    lucas.switchback123aov
    mead.lamb33glm
    patterson.switchback124aov
    urquhart.feedlot113lm
    woodman.pig3covlm
    zuidhof.broilerts

    -

    Trees

    -
    namegenlocrepsyearstrtothermodel
    box.corkrepeatedradial, asreml
    devries.pine43,3xy,graecoaov
    harris.wateruse22repeatedasreml,lme
    hanover.whitepine7*44heritabilitylmer
    johnson.douglasfirxy
    lavoranti.eucalyptus707svd
    pearce.apple46covlm,lmer
    williams.trees3762

    -

    Field and horticulture crops

    -
    namegenlocrepsyearstrtothermodel
    acorsi.grayleafspot36925nonnormalgnm,ammi
    adugna.sorghum28135
    aastveit.barley159yr*gen~yr*trtpls
    allcroft.lodging327percenttobit
    archbold.apple2524split-splitlmer
    ars.earlywhitecorn966096 traitsdotplot
    australia.soybean58424-way, 6 traitsbiplot
    barrero.maize847164116asreml
    battese.survey121-52lmer
    beall.webworms152,2xy, split-blockglm poisson,nb
    beaven.barley820xy
    besag.bayesian753xyasreml
    besag.beans64*6xylm,competition
    besag.checks2xy
    besag.elbatan503xylm, gam
    besag.endivexy,binaryautologistic
    besag.met6463xy, incblockasreml, lme
    besag.triticale32,2,3xylm, asreml
    bliss.borers4glm
    blackman.wheat1272biplot
    bond.diallel6*69diallel
    bridges.cucumber424xy, latin, heteroasreml
    brandle.rape593lmer
    buntaran.wheat30182alphaasreml
    burgueno.alpha153xy, alphaasreml,lmer
    burgueno.rowcol642xy, row-colasreml,lmer
    burgueno.unreplicated280xyasreml
    butron.maize4932diallel,pedigreebiplot,asreml
    caribbean.maize1743
    carmer.density84nls,nlme
    carlson.germination158glm
    chinloy.fractionalfactorial91/3 3^5xyaov
    christidis.competition95xy
    cochran.beets67
    cochran.bib1313bibaov, lme
    cochran.crd7xy, crdaov
    cochran.factorial24^2factorialaov
    cochran.latin66xy, latinaov
    cochran.lattice516xy, latinlmer
    cochran.wireworms55xy, latinglm
    cochran.eelworms45xyaov
    connolly.potato204xy, competitionlm
    cornelius.maize920svd
    corsten.interaction207
    cramer.cucumber8pathcoef
    crossa.wheat1825ammi
    crowder.seeds2212glm,INLA,jags
    cox.stripsplit43,4,2split-blockaov
    cullis.earlygen532xyasreml
    damesa.maize2243xy,incblock,twostageasreml
    dasilva.maize5593
    darwin.maize122t.test
    davidian.soybean23nlme
    denis.missing526lme
    denis.ryegrass217aov
    digby.jointregression10174lm
    durban.competition363xy, competitionlm
    durban.rowcol2722xylm, gam, asreml
    durban.splitplot7042xylm, gam, asreml
    eden.potato434-12xy, rcb, latinaov
    eden.nonnormal44aov
    edwards.oats80537
    engelstad.nitro256rsm1nls quadratic plateau
    fan.stability131023-waystability
    federer.diagcheck122xylm, lmer, asreml
    federer.tobacco87xylm
    fisher.barley562
    fisher.latin55xy,latinlm
    fox.wheat2214lm
    gathmann.bt28tost
    gauch.soy77412ammi
    george.wheat2119415
    giles.wheat19132 traitsgnm
    gilmour.serpentine1083xy, serpentineasreml
    gilmour.slatehall256xyasreml
    gomez.fractionalfactorial21/2 2^6xylm
    gomez.groupsplit4532xy, 3 gen groupsaov
    gomez.heteroskedastic353hetero
    gomez.multilocsplitplot233rsm1,nitroaov, lmer
    gomez.nitrogen48aov, contrasts
    gomez.nonnormal149log10lm
    gomez.nonnormal2143sqrtlm
    gomez.nonnormal3123arcsinlm
    gomez.seedrate46ratelm
    gomez.splitplot.subsample38,4subsampleaov
    gomez.splitsplit33xy, nitro, mgmtaov, lmer
    gomez.stripplot63xy, nitroaov
    gomez.stripsplitplot63xy, nitroaov
    gomez.wetdry325nitrolmer
    gotway.hessianfly164xylmer
    goulden.latin55xy, latinlm
    goulden.splitsplit242*5xy, splitaov
    graybill.heteroskedastic413hetero
    gregory.cotton24*3*2*2polar
    grover.diallel46*6diallellmDiallel
    grover.rcb.subsample429subsampleaov
    gumpertz.pepperxyglm
    hadasch.lettuce8933markersasreml
    hanks.sprinkler33xyasreml
    hayman.tobacco822diallelasreml
    hazell.vegetables46linprog
    heady.fertilizer29*9rsm2lm,rgl
    hernandez.nitrogen54rsm1lm, nls
    hildebrand.systems144asreml
    holshouser.splitstrip442*4rsm1,poplmer
    huehn.wheat2010huehn
    hughes.grapes36binomiallmer, aod, glmm
    hunter.corn1231rsm1xyplot
    ivins.herbs1362 traitslm, friedman
    jansen.apple343binomialglmer
    jansen.carrot1632binomialglmer
    jansen.strawberry124ordinalmosaicplot
    jayaraman.bamboo623heritabilitylmer
    jenkyn.mildew94lm
    john.alpha243xy, alphalm, lmer
    johnson.blight2logistic
    kang.maize17432,4
    kang.peanut10154gge
    karcher.turfgrass42,4ordinalpolr
    keen.potatodamage642,3,8ordinalmosaicplot,clmm
    kempton.competition363xy, competitionlme AR1
    kempton.rowcol352xy, row-collmer
    kempton.slatehall256xyasreml, lmer
    lee.potatoblight337411xy, ordinal, repeated
    lehner.soybeanmold35411metafor, lmer
    lillemo.wheat24137qqmedpolish, huehn
    lin.superiority3312superiority
    lin.unbalanced3318superiority
    linder.wheat974gge
    little.splitblock44,5xy, split-blockaov
    lonnquist.maize11diallelasreml
    lyons.wheat124
    lu.stability56huehn
    mcconway.turnip242,4heteroaov, lme
    mcleod.barley86aggregate
    mead.cauliflower2poissonglm
    mead.cowpea.maize3,234intercrop
    mead.germination44,4binomialglm
    mead.strawberry84
    mead.turnip35,4aov
    miguez.biomass34
    minnesota.barley.weather610
    minnesota.barley.yield22610dotplot
    omer.sorghum1864jags
    onofri.winterwheat837ammi
    ortiz.tomato151816env*gen~env*covpls
    pacheco.soybean1811ammi
    pederson.lettuce.repeated183nlme
    perry.springwheat2854gainlm,lmer,asreml
    petersen.sorghum.cowpea2474intercrop
    piepho.cocksfoot257mumm
    ratkowsky.onionslm
    reid.grasses4321nlme SSfpl
    ridout.appleshoots302,4zipzeroinfl
    rothamsted.brussels46
    rothamsted.oats89rcb
    ryder.groundnut54xy, rcblm
    salmon.bunt10220betareg
    senshu.rice40lm,Fieller
    shafii.rapeseed61433biplot
    shaw.oats13253aov
    silva.cotton555 traitsglm,poisson
    sinclair.clover5,5rsm2,mitzerlichnls,rgl
    snedecor.asparagus444split-plot, antedependence
    snijders.fusarium1734percentglm/gnm,gammi
    steptoe.morex.pheno1521610 traits
    steptoe.morex.geno150223 markers, qtl
    streibig.competition23glm
    stroup.nin564xyasreml
    stroup.splitplot4asreml, MCMCglmm
    student.barley2516lmer
    tai.potato832tai
    talbot.potato912gen*env~gen*trtpls
    theobald.barley3525rsm1
    theobald.covariate1075covjags
    thompson.cornsoy533repeated measuresaov
    vaneeuwijk.fusarium20473-wayaov
    vaneeuwijk.drymatter6473-wayaov,lmer
    vaneeuwijk.nematodes11nonnormal,poissongnm, gammi
    vargas.wheat176gen*yr~gen*trt, yr*gen~yr*covpls
    vargas.wheat287env*gen~env*covpls
    vargas.txe1024yr*trt~yr*covpls
    verbyla.lupin98327rsm1, xy, densityasreml
    vold.longterm194rsm1nls,nlme
    vsn.lupin33363xyasreml
    wedderburn.barley109percentglm/gnm
    weiss.incblock316xy,incblockasreml
    weiss.lattice494xy,latticelm,asreml
    welch.bermudagrass4,4,4rsm3, factoriallm, jags
    wheatley.carrot311glm-binomial
    yan.winterwheat189gge,biplot
    yang.barley618biplot
    yates.missing103^2factoriallm, pca
    yates.oats36xy,split,nitrolmer

    Time series

    nameyearstrtothermodel
    byers.applelme
    broadbalk.wheat7417
    hessling.argentina30temp,precip
    kreusler.maize45plant growth
    lambert.soiltemp17
    nass.barley146
    nass.corn146
    nass.cotton146
    nass.hay104
    nass.sorghum93
    nass.wheat146
    nass.rice117
    nass.soybean88
    walsh.cottonprice34cor

    Other

    -
    namemodel
    cate.potassiumcate-nelson
    cleveland.soilloess 2D
    harrison.priorsnls, prior
    nebraska.farmincomechoropleth
    pearl.kernelschisq
    stirret.borerslm, 4 trt
    turner.herbicideglm, 4 trt
    usgs.herbicidesnon-detect
    wallace.iowalandlm, choropleth
    waynick.soilspatial, nitro/carbon

    Summaries:

    -

    Multi-environment trials with multi-genotype,loc,rep,year:

    namegenlocrepsyearstrtothermodel
    barrero.maize847164116asreml
    edwards.oats80537
    gauch.soy77412ammi
    george.wheat2119415
    shafii.rapeseed61433biplot
    shaw.oats13253aov
    verbyla.lupin98327rsm1, xy, densityasreml

    Data with markers: -hadasch.lettuce.markers, -steptoe.morex.geno

    -

    Data with pedigree: -butron.maize

    -
    -
    -

    Author

    -

    Kevin Wright, with support from many people who granted permission to include their data in this package.

    -
    -
    -

    References

    -

    J. White and Frits van Evert. (2008). - Publishing Agronomic Data. - Agron J. 100, 1396-1400. - https://doi.org/10.2134/agronj2008.0080F

    -
    - -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/allcroft.lodging-1.png b/docs/reference/allcroft.lodging-1.png deleted file mode 100644 index d7124a49..00000000 Binary files a/docs/reference/allcroft.lodging-1.png and /dev/null differ diff --git a/docs/reference/allcroft.lodging.html b/docs/reference/allcroft.lodging.html deleted file mode 100644 index b883f23e..00000000 --- a/docs/reference/allcroft.lodging.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Multi-environment trial of cereal with lodging data — allcroft.lodging • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of cereal with lodging data

    - - -
    - -
    -

    Percent lodging is given for 32 genotypes at 7 environments.

    -
    - - - -

    Format

    - -

    A data frame with 224 observations on the following 3 variables.

    -
    env

    environment, 1-7

    -
    gen

    genotype, 1-32

    -
    y

    percent lodged

    - -
    - -

    Details

    - -

    This data is for the first year of a three-year study.

    -

    Used with permission of Chris Glasbey.

    -

    Source

    - -

    D. J. Allcroft and C. A. Glasbey, 2003. - Analysis of crop lodging using a latent variable model. - Journal of Agricultural Science, 140, 383--393. - https://doi.org/10.1017/S0021859603003332

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(allcroft.lodging)
-dat <- allcroft.lodging
-
-# Transformation
-dat$sy <- sqrt(dat$y)
-# Variety 4 has no lodging anywhere, so add a small amount
-dat[dat$env=='E5' & dat$gen=='G04',]$sy <- .01
-
-libs(lattice)
-dotplot(env~y|gen, dat, as.table=TRUE,
-        xlab="Percent lodged (by genotype)", ylab="Variety",
-        main="allcroft.lodging")
-
    
-# Tobit model
-libs(AER)
-
    #> Loading required package: car
    #> Loading required package: carData
    #> Loading required package: lmtest
    #> Loading required package: zoo
    #> 
-#> Attaching package: 'zoo'
    #> The following objects are masked from 'package:base':
-#> 
-#>     as.Date, as.Date.numeric
    #> Loading required package: sandwich
    #> Loading required package: survival
    m3 <- tobit(sy ~ 1 + gen + env, left=0, right=100, data=dat)
-
-# Table 2 trial/variety means
-preds <- expand.grid(gen=levels(dat$gen), env=levels(dat$env))
-preds$pred <- predict(m3, newdata=preds)
-round(tapply(preds$pred, preds$gen, mean),2)
-
    #>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
-#> -0.52 -2.77  1.68 -3.63 -2.57 -0.32 -2.27  0.94 -0.82 -2.95 -2.35 -2.95 -0.48 
-#>   G14   G15   G16   G17   G18   G19   G20   G21   G22   G23   G24   G25   G26 
-#> -1.45  0.72  0.35 -0.48 -2.29  0.08 -2.70 -1.31 -1.47  1.23 -0.66 -0.32 -2.08 
-#>   G27   G28   G29   G30   G31   G32 
-#> -1.66  0.19 -0.92  0.86  0.16  0.87 
    round(tapply(preds$pred, preds$env, mean),2)
-
    #>    E1    E2    E3    E4    E5    E6    E7 
-#> -0.03 -2.48 -2.37 -0.60  2.47 -1.73 -1.79 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/alwan.lamb-1.png b/docs/reference/alwan.lamb-1.png deleted file mode 100644 index 7de6d57e..00000000 Binary files a/docs/reference/alwan.lamb-1.png and /dev/null differ diff --git a/docs/reference/alwan.lamb-2.png b/docs/reference/alwan.lamb-2.png deleted file mode 100644 index 19be6689..00000000 Binary files a/docs/reference/alwan.lamb-2.png and /dev/null differ diff --git a/docs/reference/alwan.lamb-3.png b/docs/reference/alwan.lamb-3.png deleted file mode 100644 index f04a243b..00000000 Binary files a/docs/reference/alwan.lamb-3.png and /dev/null differ diff --git a/docs/reference/alwan.lamb.html b/docs/reference/alwan.lamb.html deleted file mode 100644 index 14fe9481..00000000 --- a/docs/reference/alwan.lamb.html +++ /dev/null @@ -1,372 +0,0 @@ - - - - - - - - -For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    For the 34 sheep sires, the number of lambs in each of 5 foot shape classes.

    - - -
    - -
    -

    For the 34 sheep sires, the number of lambs in each of 5 foot shape classes.

    -
    - - 
    data("alwan.lamb")
    - - -

    Format

    - -

    A data frame with 340 observations on the following 11 variables.

    -
    year

    numeric 1980/1981

    -
    breed

    breed PP, BRP, BR

    -
    sex

    sex of lamb M/F

    -
    sire0

    sire ID according to Alwan

    -
    shape

    sire ID according to Gilmour

    -
    count

    number of lambs

    -
    sire

    shape of foot

    -
    yr

    numeric contrast for year

    -
    b1

    numeric contrast for breeds

    -
    b2

    numeric contrast for breeds

    -
    b3

    numeric contrast for breeds

    - -
    - -

    Details

    - -

    There were 2513 lambs classified on the presence of deformities in - their feet. The lambs represent the offspring of 34 sires, 5 strains, - 2 years.

    -

    The variables yr, b1, b2, b3 are numeric contrasts for the fixed - effects as defined in the paper by Gilmour (1987) and used in the SAS - example. Gilmour does not explain the reason for the particular - contrasts. The counts for classes LF1, LF2, LF3 were combined.

    -

    Source

    - -

    Mohammed Alwan (1983). - Studies of the flock mating performance of Booroola merino crossbred ram lambs, and the foot conditions in Booroola merino crossbreds and Perendale sheep grazed on hill country. - Thesis, Massey University. https://hdl.handle.net/10179/5900 - Appendix I, II.

    -

    References

    - -

    Gilmour, Anderson, and Rae (1987). - Variance components on an underlying scale for ordered multiple threshold categorical data using a generalized linear mixed model. - Journal of Animal Breeding and Genetics, 104, 149-155. - https://doi.org/10.1111/j.1439-0388.1987.tb00117.x

    -

    SAS/STAT(R) 9.2 Users Guide, Second Edition - Example 38.11 Maximum Likelihood in Proportional Odds Model with Random Effects - https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(alwan.lamb)
-  dat <- alwan.lamb
-
-  # merge LF1 LF2 LF3 class counts, and combine M/F
-  dat$shape <- as.character(dat$shape)
-  dat$shape <- ifelse(dat$shape=="LF2", "LF3", dat$shape)
-  dat$shape <- ifelse(dat$shape=="LF1", "LF3", dat$shape)
-  dat <- aggregate(count ~ year+breed+sire0+sire+shape+yr+b1+b2+b3,
-                   dat, FUN=sum)
-
-  dat <- transform(dat,
-                   year=factor(year), breed=factor(breed),
-                   sire0=factor(sire0), sire=factor(sire))
-  # LF5 or LF3 first is a bit arbitary...affects the sign of the coefficients
-  dat <- transform(dat, shape=ordered(shape, levels=c("LF5","LF4","LF3")))
-  
-  # View counts by year and breed
-  libs(latticeExtra)
-  dat2 <- aggregate(count ~ year+breed+shape, dat, FUN=sum)
-  useOuterStrips(barchart(count ~ shape|year*breed, data=dat2,
-                          main="alwan.lamb"))
-
    
-  # Model used by Gilmour and SAS
-  dat <- subset(dat, count > 0) 
-  libs(ordinal)
-  m1 <- clmm(shape ~ yr + b1 + b2 + b3 + (1|sire), data=dat,
-             weights=count, link="probit", Hess=TRUE)
-  summary(m1) # Very similar to Gilmour results
-
    #> Cumulative Link Mixed Model fitted with the Laplace approximation
-#> 
-#> formula: shape ~ yr + b1 + b2 + b3 + (1 | sire)
-#> data:    dat
-#> 
-#>  link   threshold nobs logLik   AIC     niter     max.grad cond.H 
-#>  probit flexible  2513 -1935.06 3884.12 477(1435) 3.26e-03 3.7e+01
-#> 
-#> Random effects:
-#>  Groups Name        Variance Std.Dev.
-#>  sire   (Intercept) 0.04849  0.2202  
-#> Number of groups:  sire 34 
-#> 
-#> Coefficients:
-#>    Estimate Std. Error z value Pr(>|z|)    
-#> yr -0.14222    0.04834  -2.942  0.00326 ** 
-#> b1 -0.37808    0.07154  -5.285 1.26e-07 ***
-#> b2 -0.31570    0.09709  -3.252  0.00115 ** 
-#> b3  0.09887    0.06508   1.519  0.12872    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Threshold coefficients:
-#>         Estimate Std. Error z value
-#> LF5|LF4  0.37814    0.04907   7.707
-#> LF4|LF3  1.64351    0.05930  27.717
      ordinal::ranef(m1) # sign is opposite of SAS
-
    #> $sire
-#>      (Intercept)
-#> S01  0.254544356
-#> S02  0.163964153
-#> S03  0.069022446
-#> S04 -0.035227884
-#> S05 -0.062963869
-#> S06 -0.142210089
-#> S07 -0.228308187
-#> S08  0.362315018
-#> S09  0.092873448
-#> S10  0.062107084
-#> S11  0.049561258
-#> S12 -0.265957083
-#> S13 -0.293272810
-#> S14  0.104669108
-#> S15  0.041998914
-#> S16 -0.145156128
-#> S17  0.441200144
-#> S18  0.061379717
-#> S19  0.009783405
-#> S20 -0.136898196
-#> S21 -0.145735910
-#> S22 -0.224834944
-#> S23  0.271311572
-#> S24  0.220700448
-#> S25  0.118450870
-#> S26  0.082118202
-#> S27  0.029036648
-#> S28  0.027969069
-#> S29  0.027125179
-#> S30 -0.009727541
-#> S31 -0.095941137
-#> S32 -0.143177140
-#> S33 -0.196628516
-#> S34 -0.315856884
-#> 
    
-  ## SAS var of sires .04849
-  ## Effect   Shape   Estimate   Standard Error   DF   t Value   Pr > |t|
-  ## Intercept   1   0.3781   0.04907   29   7.71   <.0001
-  ## Intercept   2   1.6435   0.05930   29   27.72   <.0001
-  ## yr       0.1422   0.04834   2478   2.94   0.0033
-  ## b1       0.3781   0.07154   2478   5.28   <.0001
-  ## b2       0.3157   0.09709   2478   3.25   0.0012
-  ## b3       -0.09887   0.06508   2478   -1.52   0.1289
-  
-  ## Gilmour results for probit analysis
-  ## Int1   .370 +/- .052
-  ## Int2  1.603 +/- .061
-  ## Year  -.139 +/- .052
-  ## B1    -.370 +/- .076
-  ## B2    -.304 +/- .103
-  ## B3     .098 +/- .070
-
-  # Plot random sire effects with intervals, similar to SAS example
-  plot.random <- function(model, random.effect, ylim=NULL, xlab="", main="") {
-    tab <- ordinal::ranef(model)[[random.effect]]
-    tab <- data.frame(lab=rownames(tab), est=tab$"(Intercept)")
-    tab <- transform(tab,
-                     lo = est - 1.96 * sqrt(model$condVar),
-                     hi = est + 1.96 * sqrt(model$condVar))
-    # sort by est, and return index
-    ix <- order(tab$est)
-    tab <- tab[ix,]
-    
-    if(is.null(ylim)) ylim <- range(c(tab$lo, tab$hi))
-    n <- nrow(tab)
-    plot(1:n, tab$est, axes=FALSE, ylim=ylim, xlab=xlab,
-         ylab="effect", main=main, type="n")
-    text(1:n, tab$est, labels=substring(tab$lab,2) , cex=.75)
-    axis(1)
-    axis(2)
-    segments(1:n, tab$lo, 1:n, tab$hi, col="gray30")
-    abline(h=c(-.5, -.25, 0, .25, .5), col="gray")
-    return(ix)  
-  }
-  ix <- plot.random(m1, "sire")
-
    
-  # foot-shape proportions for each sire, sorted by estimated sire effects
-  # positive sire effects tend to have lower proportion of lambs in LF4 and LF5
-  tab <- prop.table(xtabs(count ~ sire+shape, dat), margin=1)
-  tab <- tab[ix,]
-  tab <- tab[nrow(tab):1,] # reverse the order
-  lattice::barchart(tab,
-                    horizontal=FALSE, auto.key=TRUE,
-                    main="alwan.lamb", xlab="Sire", ylab="Proportion of lambs",
-                    scales=list(x=list(rot=70)),
-                    par.settings = simpleTheme(col=c("yellow","orange","red")) )
-
      
-  detach("package:ordinal") # to avoid VarCorr clash with lme4
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ansari.wheat.uniformity-1.png b/docs/reference/ansari.wheat.uniformity-1.png deleted file mode 100644 index 96f8e85d..00000000 Binary files a/docs/reference/ansari.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/ansari.wheat.uniformity.html b/docs/reference/ansari.wheat.uniformity.html deleted file mode 100644 index b9d12d09..00000000 --- a/docs/reference/ansari.wheat.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — ansari.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat in India in 1940.

    -
    - - 
    data("ansari.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 768 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield of grain per plot, in half-ounces

    - - - -

    Details

    - - - -

    An experiment was conducted at the Government Research Farm, Raya - (Muttra District), during the rainy season of 1939-40.

    -

    "Wheat was sown over an area of 180 ft. x 243 ft. with 324 rows on a - field of average fertility. It had wheat during 1938-39 rabi and was - fallow during 1939-40 kharif. The seed was sown behind desi plough in - rows 9 in. apart, the length of each row being 180 ft".

    -

    "At the time of harvest, 18 rows on both sides and 10 ft. at the end of - the field were discarded to eliminate border effects and an area of - 160 ft. x 216 ft. with 288 rows was harvested in small units, each - being 2 ft. 3 in. broad with three rows 20 ft. long. There were 96 - units across the rows and eight units along the rows. The total number - of unit plots thus obtained was 768. The yield of grain for each unit - plot was weighed and recorded separately and is given in the appendix."

    -

    Field width: 96 plots * 2.25 feet = 216 feet.

    -

    Field length: 8 plots * 20 feet = 160 feet.

    -

    Comment: There seems to be a strong cyclical patern to the fertility gradient. - "History of the field reveals no explanation for this phenomenon, as an average - field usually found on the farm was selected for the trial."

    -

    Source

    - -

    Ansari, M. A. A., and G. K. Sant (1943). - A Study of Soil Heterogeneity in Relation to Size and Shape of Plots in a Wheat Field at Raya (Muhra District). - Ind. J. Agr. Sci, 13, 652-658. - https://archive.org/details/in.ernet.dli.2015.271748

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(ansari.wheat.uniformity)
-  dat <- ansari.wheat.uniformity
-
-  # match Ansari figure 3
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=216/160, # true aspect
-          main="ansari.wheat.uniformity")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/arankacami.groundnut.uniformity-1.png b/docs/reference/arankacami.groundnut.uniformity-1.png deleted file mode 100644 index 3f1527ca..00000000 Binary files a/docs/reference/arankacami.groundnut.uniformity-1.png and /dev/null differ diff --git a/docs/reference/arankacami.groundnut.uniformity.html b/docs/reference/arankacami.groundnut.uniformity.html deleted file mode 100644 index dafbc8bb..00000000 --- a/docs/reference/arankacami.groundnut.uniformity.html +++ /dev/null @@ -1,147 +0,0 @@ - -Uniformity trial of groundnut — arankacami.groundnut.uniformity • agridat - - -
    -
    - - - -
    -
    -
    -

    Uniformity trial of groundnut

    - - -
    - -
    -

    Uniformity trial of groundnut

    -
    - -
    - 
    data("arankacami.groundnut.uniformity")
    -
    - -
    -

    Format

    -

    A data frame with 96 observations on the following 3 variables.

    row
    -

    row

    - -
    col
    -

    column

    - -
    yield
    -

    yield, kg/plot

    - - -
    -
    -

    Details

    -

    Basic plot size is 0.75 m (rows) x 4 m (columns).

    -
    -
    -

    Source

    -

    Ira Arankacami, R. Rangaswamy. (1995). - A Text Book of Agricultural Statistics. New Age International Publishers. - Table 19.1.

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-library(agridat)
-data(arankacami.groundnut.uniformity)
-dat <- arankacami.groundnut.uniformity
-
-require(desplot)
-#> Loading required package: desplot
-desplot(yield ~ col*row, dat,
-        flip=TRUE, aspect=(12*.75)/(8*1),
-        main="arankacami.groundnut.uniformity")
-#> Please use desplot(data,form) instead of desplot(form,data)
-
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/archbold.apple-1.png b/docs/reference/archbold.apple-1.png deleted file mode 100644 index e5dece59..00000000 Binary files a/docs/reference/archbold.apple-1.png and /dev/null differ diff --git a/docs/reference/archbold.apple.html b/docs/reference/archbold.apple.html deleted file mode 100644 index 9396d829..00000000 --- a/docs/reference/archbold.apple.html +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - -Split-split plot experiment of apple trees — archbold.apple • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Split-split plot experiment of apple trees

    - - -
    - -
    -

    Split-split plot experiment of apple trees with different spacing, - root stock, and cultivars.

    -
    - - - -

    Format

    - -

    A data frame with 120 observations on the following 10 variables.

    -
    rep

    block, 5 levels

    -
    row

    row

    -
    pos

    position within each row

    -
    spacing

    spacing between trees, 6,10,14 feet

    -
    stock

    rootstock, 4 levels

    -
    gen

    genotype, 2 levels

    -
    yield

    yield total, kg/tree from 1975-1979

    -
    trt

    treatment code

    - - - -

    Details

    - -

    In rep 1, the 10-foot-spacing main plot was split into two - non-contiguous pieces. This also happened in rep 4. In the analysis - of Cornelius and Archbold, they consider each row x within-row-spacing - to be a distinct main plot. (Also true for the 14-foot row-spacing, - even though the 14-foot spacing plots were contiguous.)

    -

    The treatment code is defined as 100 * spacing + 10 * stock + gen, - where stock=0,1,6,7 for Seedling,MM111,MM106,M0007 and gen=1,2 for - Redspur,Golden, respectively.

    -

    Source

    - -

    D Archbold and G. R. Brown and P. L. Cornelius. (1987). - Rootstock and in-row spacing effects on growth and yield of spur-type - delicious and Golden delicious apple. - Journal of the American Society for Horticultural Science, 112, 219-222.

    -

    References

    - -

    Cornelius, PL and Archbold, DD, 1989. - Analysis of a split-split plot experiment with missing data using - mixed model equations. - Applications of Mixed Models in Agriculture and Related - Disciplines. Pages 55-79.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(archbold.apple)
-dat <- archbold.apple
-
-# Define main plot and subplot
-dat <- transform(dat, rep=factor(rep), spacing=factor(spacing), trt=factor(trt),
-                 mp = factor(paste(row,spacing,sep="")),
-                 sp = factor(paste(row,spacing,stock,sep="")))
-
-# Due to 'spacing', the plots are different sizes, but the following layout
-# shows the relative position of the plots and treatments. Note that the
-# 'spacing' treatments are not contiguous in some reps.
-libs(desplot)
-desplot(dat, spacing~row*pos,
-        col=stock, cex=1, num=gen, # aspect unknown
-        main="archbold.apple")
    
-
-libs(lme4, lucid)
    #> Loading required package: Matrix
    m1 <- lmer(yield ~ -1 + trt + (1|rep/mp/sp), dat)
-
-vc(m1)  # Variances/means on Cornelius, page 59
    #>          grp        var1 var2   vcov sdcor
-#>  sp:(mp:rep) (Intercept) <NA>  193.3 13.9 
-#>       mp:rep (Intercept) <NA>  203.8 14.27
-#>          rep (Intercept) <NA>  197.3 14.05
-#>     Residual        <NA> <NA> 1015   31.86
    ##         grp        var1 var2   vcov sdcor
-## sp:(mp:rep) (Intercept) <NA>  193.3 13.9
-##      mp:rep (Intercept) <NA>  203.8 14.28
-##         rep (Intercept) <NA>  197.3 14.05
-##    Residual        <NA> <NA> 1015   31.86
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ars.earlywhitecorn96-1.png b/docs/reference/ars.earlywhitecorn96-1.png deleted file mode 100644 index 974d8ca4..00000000 Binary files a/docs/reference/ars.earlywhitecorn96-1.png and /dev/null differ diff --git a/docs/reference/ars.earlywhitecorn96-2.png b/docs/reference/ars.earlywhitecorn96-2.png deleted file mode 100644 index a35354c9..00000000 Binary files a/docs/reference/ars.earlywhitecorn96-2.png and /dev/null differ diff --git a/docs/reference/ars.earlywhitecorn96.html b/docs/reference/ars.earlywhitecorn96.html deleted file mode 100644 index bbad06ae..00000000 --- a/docs/reference/ars.earlywhitecorn96.html +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - -Multi-environment trial of early white food corn — ars.earlywhitecorn96 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of early white food corn

    - - -
    - -
    -

    Multi-environment trial of early white food corn for 60 white hybrids.

    -
    - - - -

    Format

    - -

    A data frame with 540 observations on the following 9 variables.

    -
    loc

    location, 9 levels

    -
    gen

    gen, 60 levels

    -
    yield

    yield, bu/ac

    -
    stand

    stand, percent

    -
    rootlodge

    root lodging, percent

    -
    stalklodge

    stalk lodging, percent

    -
    earht

    ear height, inches

    -
    flower

    days to flower

    -
    moisture

    moisture, percent

    - -
    - -

    Details

    - -

    Data are the average of 3 replications.

    -

    Yields were measured for each plot and converted to bushels / acre and - adjusted to 15.5 percent moisture.

    -

    Stand is expressed as a percentage of the optimum plant stand.

    -

    Lodging is expressed as a percentage of the total plants for each hybrid.

    -

    Ear height was measured from soil level to the top ear leaf collar. - Heights are expressed in inches.

    -

    Days to flowering is the number of days from planting to mid-tassel or mid-silk.

    -

    Moisture of the grain was measured at harvest.

    -

    Source

    - -

    L. Darrah, R. Lundquist, D. West, C. Poneleit, B. Barry, B. Zehr, - A. Bockholt, L. Maddux, K. Ziegler, and P. Martin. (1996). - White Food Corn 1996 Performance Tests. - Agricultural Research Service Special Report 502.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  
-  data(ars.earlywhitecorn96)
-  dat <- ars.earlywhitecorn96
-
-  libs(lattice)
-  # These views emphasize differences between locations
-  dotplot(gen~yield, dat, group=loc, auto.key=list(columns=3),
-          main="ars.earlywhitecorn96")
-
      ## dotplot(gen~stalklodge, dat, group=loc, auto.key=list(columns=3),
-  ##         main="ars.earlywhitecorn96")
-  splom(~dat[,3:9], group=dat$loc, auto.key=list(columns=3),
-        main="ars.earlywhitecorn96")
-
      
-  # MANOVA
-  m1 <- manova(cbind(yield,earht,moisture) ~ gen + loc, dat)
-  m1
-
    #> Call:
-#>    manova(cbind(yield, earht, moisture) ~ gen + loc, dat)
-#> 
-#> Terms:
-#>                      gen      loc Residuals
-#> yield            19200.2 378467.7   51875.0
-#> earht             4782.2   4750.6    1652.1
-#> moisture           446.1   6430.5     258.5
-#> Deg. of Freedom       59        4       236
-#> 
-#> Residual standard errors: 14.82596 2.645798 1.046639
-#> Estimated effects may be unbalanced
-#> 240 observations deleted due to missingness
      summary(m1)
-
    #>            Df Pillai approx F num Df den Df    Pr(>F)    
-#> gen        59 1.6158    4.669    177    708 < 2.2e-16 ***
-#> loc         4 2.1686  153.898     12    708 < 2.2e-16 ***
-#> Residuals 236                                            
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/australia.soybean-1.png b/docs/reference/australia.soybean-1.png deleted file mode 100644 index b4c5d75d..00000000 Binary files a/docs/reference/australia.soybean-1.png and /dev/null differ diff --git a/docs/reference/australia.soybean-2.png b/docs/reference/australia.soybean-2.png deleted file mode 100644 index dc22a440..00000000 Binary files a/docs/reference/australia.soybean-2.png and /dev/null differ diff --git a/docs/reference/australia.soybean.html b/docs/reference/australia.soybean.html deleted file mode 100644 index 4a9d1e75..00000000 --- a/docs/reference/australia.soybean.html +++ /dev/null @@ -1,299 +0,0 @@ - - - - - - - - -Multi-environment trial of soybean in Australia — australia.soybean • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of soybean in Australia

    - - -
    - -
    -

    Yield and other traits of 58 varieties of soybeans, grown in four - locations across two years in Australia. This is four-way data of - Year x Loc x Gen x Trait.

    -
    - - - -

    Format

    - -

    A data frame with 464 observations on the following 10 variables.

    -
    env

    environment, 8 levels, first character of location - and last two characters of year

    -
    loc

    location

    -
    year

    year

    -
    gen

    genotype of soybeans, 1-58

    -
    yield

    yield, metric tons / hectare

    -
    height

    height (meters)

    -
    lodging

    lodging

    -
    size

    seed size, (millimeters)

    -
    protein

    protein (percentage)

    -
    oil

    oil (percentage)

    - -
    - -

    Details

    - - - -

    Measurement are available from four locations in Queensland, Australia in two - consecutive years 1970, 1971.

    -

    The 58 different genotypes of soybeans consisted of 43 lines (40 local - Australian selections from a cross, their two parents, and one other - which was used a parent in earlier trials) and 15 other lines of which - 12 were from the US.

    -

    Lines 1-40 were local Australian selections from Mamloxi (CPI 172) and - Avoyelles (CPI 15939).

    - - - - - - - - - - - - - - - - - - - - - -
    No.Line
    1-40Local selections
    41Avoyelles (CPI 15939) Tanzania
    42Hernon 49 (CPI 15948) Tanzania
    43Mamloxi (CPI 172) Nigeria
    44Dorman USA
    45Hampton USA
    46Hill USA
    47Jackson USA
    48Leslie USA
    49Semstar Australia
    50Wills USA
    51C26673 Morocco
    52C26671 Morocco
    53Bragg USA
    54Delmar USA
    55Lee USA
    56Hood USA
    57Ogden USA
    58Wayne USA
    - - -

    Note on the data in Basford and Tukey book. The values for line 58 for -Nambour 1970 and Redland Bay 1971 are incorrectly listed on page 477 as -20.490 and 15.070. They should be 17.350 and 13.000, respectively. In -the data set made available here, these values have been corrected.

    -

    Used with permission of Kaye Basford, Pieter Kroonenberg.

    -

    Source

    - -

    Basford, K. E., and Tukey, J. W. (1999). - Graphical analysis of multiresponse data illustrated with a plant breeding trial. - Chapman and Hall/CRC.

    -

    Retrieved from: - https://three-mode.leidenuniv.nl/data/soybeaninf.htm

    -

    References

    - -

    K E Basford (1982). The Use of Multidimensional Scaling in Analysing - Multi-Attribute Genotype Response Across Environments, - Aust J Agric Res, 33, 473--480.

    -

    Kroonenberg, P. M., & Basford, K. E. B. (1989). An investigation of - multi-attribute genotype response across environments using three-mode - principal component analysis. - Euphytica, 44, 109--123.

    -

    Marcin Kozak (2010). - Use of parallel coordinate plots in multi-response selection of - interesting genotypes. - Communications in Biometry and Crop Science, 5, 83-95.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(australia.soybean)
-dat <- australia.soybean
-
-libs(reshape2)
-dm <- melt(dat, id.var=c('env', 'year','loc','gen'))
-
-# Joint plot of genotypes & traits. Similar to Figure 1 of Kroonenberg 1989
-dmat <- acast(dm, gen~variable, fun=mean)
-dmat <- scale(dmat)
-biplot(princomp(dmat), main="australia.soybean trait x gen biplot", cex=.75)
-
    
-
-# Figure 1 of Kozak 2010, lines 44-58
-  libs(reshape2, lattice, latticeExtra)
-  data(australia.soybean)
-  dat <- australia.soybean
-  dat <- melt(dat, id.var=c('env', 'year','loc','gen'))
-  dat <- acast(dat, gen~variable, fun=mean)
-  dat <- scale(dat)
-  dat <- as.data.frame(dat)[,c(2:6,1)]
-  dat$gen <- rownames(dat)
-  # data for the graphic by Kozak
-  dat2 <- dat[44:58,]
-  dat3 <- subset(dat2, is.element(gen, c("G48","G49","G50","G51")))
-  
-  parallelplot( ~ dat3[,1:6]|dat3$gen, main="australia.soybean",
-               as.table=TRUE, horiz=FALSE) +
-    parallelplot( ~ dat2[,1:6], horiz=FALSE, col="gray80") +
-    parallelplot( ~ dat3[,1:6]|dat3$gen,
-                 as.table=TRUE, horiz=FALSE, lwd=2)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/baker.barley.uniformity-1.png b/docs/reference/baker.barley.uniformity-1.png deleted file mode 100644 index 1c4e25a1..00000000 Binary files a/docs/reference/baker.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/baker.barley.uniformity-2.png b/docs/reference/baker.barley.uniformity-2.png deleted file mode 100644 index 312cbfcf..00000000 Binary files a/docs/reference/baker.barley.uniformity-2.png and /dev/null differ diff --git a/docs/reference/baker.barley.uniformity-3.png b/docs/reference/baker.barley.uniformity-3.png deleted file mode 100644 index e305c7f4..00000000 Binary files a/docs/reference/baker.barley.uniformity-3.png and /dev/null differ diff --git a/docs/reference/baker.barley.uniformity.html b/docs/reference/baker.barley.uniformity.html deleted file mode 100644 index 25ef455e..00000000 --- a/docs/reference/baker.barley.uniformity.html +++ /dev/null @@ -1,240 +0,0 @@ - - - - - - - - -Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of barley, 10 years on same ground

    - - -
    - -
    -

    Uniformity trials of barley at Davis, California, 1925-1935, 10 years on same ground.

    -
    - - - -

    Format

    - -

    A data frame with 570 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    year

    year

    -
    yield

    yield, pounds/acre

    - -
    - -

    Details

    - - - -

    Ten years of uniformity trials were sown on the same ground. Baker - (1952) shows a map of the field, which gravel subsoil extended from - the upper right corner diagonally lower-left. This results in lower - yields as seen on the 10-year average map.

    -

    Field width: 19 plots = 827 ft

    -

    Field length: 3 plots * 161 ft + 2 alleys * 15 feet = 513 ft

    -

    Source

    - -

    Baker, GA and Huberty, MR and Veihmeyer, FJ. (1952) - A uniformity trial on unirrigated barley of ten years' duration. - Agronomy Journal, 44, 267-270. - https://doi.org/10.2134/agronj1952.00021962004400050011x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(baker.barley.uniformity)
-dat <- baker.barley.uniformity
-
-# Ten-year average
-dat2 <- aggregate(yield ~ row*col, data=dat, FUN=mean, na.rm=TRUE)
-
-libs(desplot)
-desplot(dat, yield~col*row|year,
-        aspect = 513/827, # true aspect
-        main="baker.barley.uniformity - heatmaps by year")
-
    
-desplot(dat2, yield~col*row,
-        aspect = 513/827, # true aspect
-        main="baker.barley.uniformity - heatmap of 10-year average")
-
    # Note low yield in upper right, slanting to left a bit due to sandy soil
-# as shown in Baker figure 1.
-
-
-# Baker fig 2, stdev vs mean
-dat3 <- aggregate(yield ~ row*col, data=dat, FUN=sd, na.rm=TRUE)
-plot(dat2$yield, dat3$yield, xlab="Mean yield", ylab="Std Dev yield",
-     main="baker.barley.uniformity")
-
    
-# Baker table 4, correlation of plots across years
-# libs(reshape2)
-# mat <- acast(dat, row+col~year)
-# round(cor(mat, use='pair'),2)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/baker.strawberry.uniformity-1.png b/docs/reference/baker.strawberry.uniformity-1.png deleted file mode 100644 index ac22d0a4..00000000 Binary files a/docs/reference/baker.strawberry.uniformity-1.png and /dev/null differ diff --git a/docs/reference/baker.strawberry.uniformity-2.png b/docs/reference/baker.strawberry.uniformity-2.png deleted file mode 100644 index 65ffd6eb..00000000 Binary files a/docs/reference/baker.strawberry.uniformity-2.png and /dev/null differ diff --git a/docs/reference/baker.strawberry.uniformity.html b/docs/reference/baker.strawberry.uniformity.html deleted file mode 100644 index 73b91efb..00000000 --- a/docs/reference/baker.strawberry.uniformity.html +++ /dev/null @@ -1,249 +0,0 @@ - - - - - - - - -Uniformity trial of strawberry — baker.strawberry.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of strawberry

    - - -
    - -
    -

    Uniformity trial of strawberry

    -
    - - 
    data("baker.strawberry.uniformity")
    - - -

    Format

    - -

    A data frame with 700 observations on the following 4 variables.

    -
    trial

    a factor with levels T1 T2

    -
    row

    a numeric vector

    -
    col

    a numeric vector

    -
    yield

    a numeric vector

    - -
    - -

    Details

    - -

    In trial T1, 200 plants were grown in two double-row beds at Davis, - CA, in 1946. The rows were 1 foot apart. The beds were 42 inches - apart. The plants were 10 inches apart within a row, each row - consisting of 50 plants.

    -

    Field length: 50 plants * 10 inches = 500 inches.

    -

    Field width: 12 in + 42 in + 12 in =

    -

    In trial T2, 500 plants were grown in single beds. The beds were 30 - inches apart. Each bed was 50 plants long with 10 inches between - plants.

    -

    Field length: 50 plants * 10 in = 500 in.

    -

    Field width: 10 beds * 30 in = 300 in.

    -

    Source

    - -

    G. A. Baker and R. E. Baker (1953). - Strawberry Uniformity Yield Trials. - Biometrics, 9, 412-421. - https://doi.org/10.2307/3001713

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(baker.strawberry.uniformity)
-dat <- baker.strawberry.uniformity
-
-# Match mean and cv of Baker p 414.
-libs(dplyr)
-
    #> 
-#> Attaching package: 'dplyr'
    #> The following object is masked from 'package:car':
-#> 
-#>     recode
    #> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
    #> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
    dat <- group_by(dat, trial)
-summarize(dat, mn=mean(yield), cv=sd(yield)/mean(yield))
-
    #> `summarise()` ungrouping output (override with `.groups` argument)
    #> # A tibble: 2 x 3
-#>   trial    mn    cv
-#>   <fct> <dbl> <dbl>
-#> 1 T1     209. 0.263
-#> 2 T2     173. 0.394
    
-dat <- mutate(dat, col = ifelse(trial=="T1" & col==4, 7, col),
-                   col = ifelse(trial=="T1" & col==3, 6, col))
-
-libs(desplot)
-desplot(dat, yield ~ col*row, subset=trial=="T1",
-        flip=TRUE, aspect=500/(5*12),
-        main="baker.strawberry.uniformity - trial T1")
-
    desplot(dat, yield ~ col*row, subset=trial=="T2",
-        flip=TRUE, aspect=500/300,
-        main="baker.strawberry.uniformity - trial T2")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/baker.wheat.uniformity-1.png b/docs/reference/baker.wheat.uniformity-1.png deleted file mode 100644 index be75ef3f..00000000 Binary files a/docs/reference/baker.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/baker.wheat.uniformity.html b/docs/reference/baker.wheat.uniformity.html deleted file mode 100644 index 25ca1482..00000000 --- a/docs/reference/baker.wheat.uniformity.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — baker.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat

    -
    - - 
    data("baker.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 225 observations on the following 3 variables.

    -
    row

    row

    -
    col

    col

    -
    yield

    yield

    - -
    - -

    Details

    - - - -

    Data was collected in 1939-1940. The trial consists of sixteen 40 ft. x -40 ft. blocks subdivided into nine plots each. The data were secured in -1939-1940 from White Federation wheat. The design of the experiment was -square with alleys 20 feet wide between blocks. The plots were 10 feet -long with two guard rows on each side.

    -

    Morning glories infested the middle two columns of blocks, uniformly -over the blocks affected.

    -

    The data here include missing values for the alleys so that the field -map is approximately correct.

    -

    Field width: 4 blocks of 40 feet + 3 alleys of 20 feet = 220 feet.

    -

    Field length: 4 blocks of 40 feet + 3 alleys of 20 feet = 220 feet.

    -

    Source

    - -

    G. A. Baker, E. B. Roessler (1957). - Implications of a uniformity trial with small plots of wheat. - Hilgardia, 27, 183-188. - https://hilgardia.ucanr.edu/Abstract/?a=hilg.v27n05p183

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(baker.wheat.uniformity)
-  dat <- baker.wheat.uniformity
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=1,
-          main="baker.wheat.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bancroft.peanut.uniformity-1.png b/docs/reference/bancroft.peanut.uniformity-1.png deleted file mode 100644 index ab33c2b1..00000000 Binary files a/docs/reference/bancroft.peanut.uniformity-1.png and /dev/null differ diff --git a/docs/reference/bancroft.peanut.uniformity.html b/docs/reference/bancroft.peanut.uniformity.html deleted file mode 100644 index 486581e2..00000000 --- a/docs/reference/bancroft.peanut.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trial of peanuts — bancroft.peanut.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of peanuts

    - - -
    - -
    -

    Uniformity trial of peanuts in Alabama, 1946.

    -
    - - 
    data("bancroft.peanut.uniformity")
    - - -

    Format

    - -

    A data frame with 216 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds

    -
    block

    block

    - - - -

    Details

    - -

    The data are obtained from two parts of the same field, located at - Wiregrass Substation, Headland, Alabama, USA. Each part had 18 rows, - 3 feet wide, 100 feet long. Plots were harvested in 1946. Green - weights in pounds were recorded.

    -

    Each plot was 16.66 linear feet of row and 3 feet in width, 50 sq feet.

    -

    Field width: 6 plots * 16.66 feet = 100 feet

    -

    Field length: 18 plots * 3 feet = 54 feet

    -

    Conclusions: - Based on the relative efficiencies, increasing the size of the plot - along the row is better than across the row. Narrow, rectangular - plots are more efficient.

    -

    Source

    - -

    Bancroft, T. A. et a1., (1948). - Size and Shape of Plots and Distribution of Plot Yield for Field Experiments with Peanuts. - Alabama Agricultural Experiment Station Progress Report, sec. 39. - Table 4, page 6. - https://aurora.auburn.edu/bitstream/handle/11200/1345/0477PROG.pdf;sequence=1

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(bancroft.peanut.uniformity)
-dat <- bancroft.peanut.uniformity
-
-# match means Bancroft page 3
-## dat 
-## # A tibble: 2 x 2
-##   block    mn
-##   <chr> <dbl>
-## 1 B1     2.46
-## 2 B2     2.05
-
-libs(desplot)
-desplot(dat, yield ~ col*row|block,
-        flip=TRUE, aspect=(18*3)/(6*16.66), # true aspect
-        main="bancroft.peanut.uniformity")
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/barrero.maize.html b/docs/reference/barrero.maize.html deleted file mode 100644 index 1a40c68e..00000000 --- a/docs/reference/barrero.maize.html +++ /dev/null @@ -1,218 +0,0 @@ - -Multi-environment trial of maize in Texas. — barrero.maize • agridat - - -
    -
    - - - -
    -
    -
    -

    Multi-environment trial of maize in Texas.

    - - -
    - -
    -

    Multi-environment trial of maize in Texas.

    -
    - -
    - 
    data("barrero.maize")
    -
    - -
    -

    Format

    -

    A data frame with 14568 observations on the following 15 variables.

    year
    -

    year of testing, 2000-2010

    - -
    yor
    -

    year of release, 2000-2010

    - -
    loc
    -

    location, 16 places in Texas

    - -
    env
    -

    environment (year+loc), 107 levels

    - -
    rep
    -

    replicate, 1-4

    - -
    gen
    -

    genotype, 847 levels

    - -
    daystoflower
    -

    numeric

    - -
    plantheight
    -

    plant height, cm

    - -
    earheight
    -

    ear height, cm

    - -
    population
    -

    plants per hectare

    - -
    lodged
    -

    percent of plants lodged

    - -
    moisture
    -

    moisture percent

    - -
    testweight
    -

    test weight kg/ha

    - -
    yield
    -

    yield, kg/ha

    - - -
    -
    -

    Details

    - - -

    This is a large (14500 records), multi-year, multi-location, 10-trait - dataset from the Texas AgriLife Corn Performance Trials.

    -

    These data are from 2-row plots approximately 36in wide by 25 feet long.

    -

    Barrero et al. used this data to estimate the genetic gain in maize - hybrids over a 10-year period of time.

    -

    Used with permission of Seth Meyers.

    -
    -
    -

    Source

    -

    Barrero, Ivan D. et al. (2013). - A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. - Field Crops Research, 149, Pages 167-176. - https://doi.org/10.1016/j.fcr.2013.04.017

    -
    -
    -

    References

    -

    None.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  library(agridat)  
-  dat <- barrero.maize
-#> Error in eval(expr, envir, enclos): object 'barrero.maize' not found
-
-  library(lattice)
-  bwplot(yield ~ factor(year)|loc, dat,
-         main="barrero.maize - Yield trends by loc",
-         scales=list(x=list(rot=90)))
-#> Error in eval(substitute(groups), data, environment(formula)): object 'dat' not found
-  
-  # Table 6 of Barrero. Model equation 1.
-  pacman::p_load(dplyr, asreml, lucid)
-#> Offline License checked out Fri Dec 17 15:16:49 2021
-  dat <- arrange(dat, env)
-#> Error in arrange(dat, env): object 'dat' not found
-  dat <- mutate(dat,
-                yearf=factor(year), env=factor(env),
-                loc=factor(loc), gen=factor(gen), rep=factor(rep))
-#> Error in mutate(dat, yearf = factor(year), env = factor(env), loc = factor(loc),     gen = factor(gen), rep = factor(rep)): object 'dat' not found
-  
-  m1 <- asreml(yield ~ loc + yearf + loc:yearf, data=dat,
-               random = ~ gen + rep:loc:yearf +
-                 gen:yearf + gen:loc +
-                 gen:loc:yearf,
-               residual = ~ dsum( ~ units|env) )
-#> Error in asreml(yield ~ loc + yearf + loc:yearf, data = dat, random = ~gen +     rep:loc:yearf + gen:yearf + gen:loc + gen:loc:yearf, residual = ~dsum(~units |     env)): object 'dat' not found
-  
-  # Variance components for yield match Barrero table 6.
-  lucid::vc(m1)[1:5,]
-#> Error in lucid::vc(m1): object 'm1' not found
-  ##        effect component std.error z.ratio bound 
-  ## rep:loc:yearf   0.111     0.01092    10       P 0  
-  ##           gen   0.505     0.03988    13       P 0  
-  ##     gen:yearf   0.05157   0.01472     3.5     P 0  
-  ##       gen:loc   0.02283   0.0152      1.5     P 0.2
-  ## gen:loc:yearf   0.2068    0.01806    11       P 0  
-  
-  summary(vc(m1)[6:112,"component"]) # Means match last row of table 6
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'm1' not found
-  ##   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-  ## 0.1286  0.3577  0.5571  0.8330  1.0322  2.9867 
-  
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/batchelor.uniformity-1.png b/docs/reference/batchelor.uniformity-1.png deleted file mode 100644 index a4227510..00000000 Binary files a/docs/reference/batchelor.uniformity-1.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity-2.png b/docs/reference/batchelor.uniformity-2.png deleted file mode 100644 index 2e49f657..00000000 Binary files a/docs/reference/batchelor.uniformity-2.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity-3.png b/docs/reference/batchelor.uniformity-3.png deleted file mode 100644 index 5c1a6946..00000000 Binary files a/docs/reference/batchelor.uniformity-3.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity-4.png b/docs/reference/batchelor.uniformity-4.png deleted file mode 100644 index deb494c3..00000000 Binary files a/docs/reference/batchelor.uniformity-4.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity-5.png b/docs/reference/batchelor.uniformity-5.png deleted file mode 100644 index 6b8656e4..00000000 Binary files a/docs/reference/batchelor.uniformity-5.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity-6.png b/docs/reference/batchelor.uniformity-6.png deleted file mode 100644 index d71d6035..00000000 Binary files a/docs/reference/batchelor.uniformity-6.png and /dev/null differ diff --git a/docs/reference/batchelor.uniformity.html b/docs/reference/batchelor.uniformity.html deleted file mode 100644 index ce10fb8b..00000000 --- a/docs/reference/batchelor.uniformity.html +++ /dev/null @@ -1,320 +0,0 @@ - - - - - - - - -Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of apples, lemons, oranges, and walnuts

    - - -
    - -
    -

    Uniformity trials of apples, lemons, oranges, and walnuts, in - California & Utah, 1915-1918.

    -
    - - - -

    Format

    - -

    Each dataset has the following format

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per tree (pounds)

    - -
    - -

    Details

    - - - -

    A few of the trees affected by disease were eliminated and the yield - was replaced by the average of the eight surrounding trees.

    -

    The following details are from Batchelor (1918).

    -

    Jonathan Apples

    -

    "The apple records were obtained from a 10-year old - Jonathan apple orchard located at Providence, Utah. The surface soil - of this orchard is very uniform to all appearances except on the - extreme eastern edge, where the percentage of gravel increases - slightly. The trees are planted 16 feet apart, east and west, and 30 - feet apart north and south."

    -

    Note: The orientation of the field is not given in the paper, but all other - fields in the paper have north at the top, so that is assumed to be - true for this field as well. Yields may be from 1916.

    -

    Field width: 8 trees * 16 feet = 128 feet

    -

    Field length: 28 rows * 30 feet = 840 feet

    - -

    Eureka Lemon

    -

    The lemon (Citrus limonia) tree yields were obtained from a grove of - 364 23-year-old trees, located at Upland, California. The records - extend from October 1, 1915, to October 1, 1916. The grove consists of - 14 rows of 23-year-old trees, extending north and south, with 26 trees - in a row, planted 24 by 24 feet apart. This grove presents the most - uniform appearance of any under consideration [in this paper]. The - land is practically level, and the soil is apparently uniform in - texture. The records show a grouping of several low-yielding trees; - yet a field observation gives one the impression that the grove as a - whole is remarkably uniform.

    -

    Field width: 14 trees * 24 feet = 336 feet

    -

    Field length: 26 trees * 24 feet = 624 feet

    -

    Navel 1 at Arlington

    -

    These records were of the 1915-16 yields of one thousand 24-year-old - navel-orange trees near Arlington station, Riverside, California. The - grove consists of 20 rows of trees from north to south, with 50 trees - in a row, planted 22 by 22 feet. A study of the records shows certain - distinct high- and low-yielding areas. The northeast corner and the - south end contain notably high-yielding trees. The north two-thirds of - the west side contains a large number of low-yielding trees. These - areas are apparently correlated with soil variation. Variations from - tree to tree also occur, the cause of which is not evident. These - variations, which are present in every orchard, bring uncertainty into - the results offield experiments.

    -

    Field width: 20 trees * 22 feet = 440 feet

    -

    Field length: 50 trees * 22 feet = 1100 feet

    -

    Navel 2 at Antelope

    -

    The navel-orange grove later referred to as the Antelope Heights - navels is a plantation of 480 ten-yearold trees planted 22 by 22 feet, - located at Naranjo, California. The yields are from 1916. The general - appearance of the trees gives a visual impression of uniformity - greater than a comparison of the individual tree production - substantiates.

    -

    Field width: 15 trees * 22 feet = 330 feet

    -

    Field length: 33 trees * 22 feet = 726 feet

    -

    Valencia Orange

    -

    The Valencia orange grove is composed of 240 15-year-old trees, - planted 21 feet 6 inches by 22 feet 6 inches, located at Villa Park, - California. The yields were obtained in 1916.

    -

    Field width: 12 rows * 22 feet = 264 feet

    -

    Field length: 20 rows * 22 feet = 440 feet

    -

    Walnut

    -

    The walnut (Juglans regia) yields were obtained during the seasons of - 1915 and 1916 from a 24-year-old Santa Barbara softshell seedling - grove, located at Whittier, California. - [Note, The yields here appear to be the 1915 yields.] - The planting is laid out 10 trees wide and 32 trees long, entirely - surrounded by additional walnut plantings, except on a part of one - side which is adjacent to an orange grove. The trees are planted on - the square system, 50 feet apart.

    -

    Field width: 10 trees * 50 feet = 500 feet

    -

    Field length: 32 trees * 50 feet = 1600 feet

    -

    Source

    - -

    L. D. Batchelor and H. S. Reed. (1918). - Relation of the variability of yields of fruit trees to the accuracy - of field trials. J. Agric. Res, 12, 245--283. - https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-libs(desplot)
-
-  # Apple
-  data(batchelor.apple.uniformity)
-  desplot(batchelor.apple.uniformity, yield~col*row,
-          aspect=840/128, tick=TRUE, # true aspect
-          main="batchelor.apple.uniformity")
-
    
-  # Lemon
-  data(batchelor.lemon.uniformity)
-  desplot(batchelor.lemon.uniformity, yield~col*row,
-          aspect=624/336, # true aspect
-          main="batchelor.lemon.uniformity")
-
    
-  # Navel1 (Arlington)
-  data(batchelor.navel1.uniformity)
-  desplot(batchelor.navel1.uniformity, yield~col*row,
-          aspect=1100/440, # true aspect
-          main="batchelor.navel1.uniformity - Arlington")
-
    
-  # Navel2 (Antelope)
-  data(batchelor.navel2.uniformity)
-  desplot(batchelor.navel2.uniformity, yield~col*row,
-          aspect=726/330, # true aspect
-          main="batchelor.navel2.uniformity - Antelope")
-
    
-  # Valencia
-  data(batchelor.valencia.uniformity)
-  desplot(batchelor.valencia.uniformity, yield~col*row,
-          aspect=440/264, # true aspect
-          main="batchelor.valencia.uniformity")
-
    
-  # Walnut
-  data(batchelor.walnut.uniformity)
-  desplot(batchelor.walnut.uniformity, yield~col*row,
-          aspect=1600/500, # true aspect
-          main="batchelor.walnut.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/battese.survey-1.png b/docs/reference/battese.survey-1.png deleted file mode 100644 index 7b8f5d57..00000000 Binary files a/docs/reference/battese.survey-1.png and /dev/null differ diff --git a/docs/reference/battese.survey-2.png b/docs/reference/battese.survey-2.png deleted file mode 100644 index 907a2e8d..00000000 Binary files a/docs/reference/battese.survey-2.png and /dev/null differ diff --git a/docs/reference/battese.survey.html b/docs/reference/battese.survey.html deleted file mode 100644 index ee7095d5..00000000 --- a/docs/reference/battese.survey.html +++ /dev/null @@ -1,308 +0,0 @@ - - - - - - - - -Survey and satellite data for corn and soy areas in Iowa — battese.survey • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Survey and satellite data for corn and soy areas in Iowa

    - - -
    - -
    -

    Survey and satellite data for corn and soy areas in Iowa

    -
    - - 
    data("battese.survey")
    - - -

    Format

    - -

    A data frame with 37 observations on the following 9 variables.

    -
    county

    county name

    -
    segment

    sample segment number (within county)

    -
    countysegs

    number of segments in county

    -
    cornhect

    hectares of corn in segment

    -
    soyhect

    hectares of soy

    -
    cornpix

    pixels of corn in segment

    -
    soypix

    pixels of soy

    -
    cornmean

    county mean of corn pixels per segment

    -
    soymean

    county mean of soy pixels per segment

    - -
    - -

    Details

    - -

    The data are for 12 counties in north-central Iowa in 1978.

    -

    The USDA determined the area of soybeans in 37 area sampling units - (called 'segments'). Each segment is about one square mile (about 259 - hectares). The number of pixels of that were classified as corn and - soybeans came from Landsat images obtained in Aug/Sep 1978. Each pixel - represents approximately 0.45 hectares.

    -

    Data originally compiled by USDA.

    -

    This data is also available in R packages: 'rsae::landsat' and 'JoSAE::landsat'.

    -

    Source

    - -

    Battese, George E and Harter, Rachel M and Fuller, Wayne A. (1988). - An error-components model for prediction of county crop areas using - survey and satellite data. - emphJournal of the American Statistical Association, 83, 28-36. - https://doi.org/10.2307/2288915

    -

    Battese (1982) preprint version. - https://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf

    -

    References

    - -

    Pushpal K Mukhopadhyay and Allen McDowell. (2011). - Small Area Estimation for Survey Data Analysis Using SAS Software - SAS Global Forum 2011.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(battese.survey)
-dat <- battese.survey
-
-# Battese fig 1 & 2.  Corn plot shows outlier in Hardin county
-libs(lattice)
-dat <- dat[order(dat$cornpix),]
-xyplot(cornhect ~ cornpix, data=dat, group=county, type=c('p','l'),
-       main="battese.survey", xlab="Pixels of corn", ylab="Hectares of corn",
-       auto.key=list(columns=3))
-
    
-dat <- dat[order(dat$soypix),]
-xyplot(soyhect ~ soypix, data=dat, group=county, type=c('p','l'),
-       main="battese.survey", xlab="Pixels of soy", ylab="Hectares of soy",
-       auto.key=list(columns=3))
-
    
-libs(lme4, lucid)
-
    #> Loading required package: Matrix
    #> Registered S3 methods overwritten by 'lme4':
-#>   method                          from
-#>   cooks.distance.influence.merMod car 
-#>   influence.merMod                car 
-#>   dfbeta.influence.merMod         car 
-#>   dfbetas.influence.merMod        car 
      
-# Fit the models of Battese 1982, p.18.  Results match
-m1 <- lmer(cornhect ~ 1 + cornpix + (1|county), data=dat)
-fixef(m1)
-
    #> (Intercept)     cornpix 
-#>   5.4661899   0.3878358 
    ## (Intercept)     cornpix 
-##   5.4661899   0.3878358 
-vc(m1)
-
    #>       grp        var1 var2   vcov  sdcor
-#>    county (Intercept) <NA>  62.83  7.926
-#>  Residual        <NA> <NA> 290.4  17.04 
    ##      grp        var1 var2   vcov  sdcor
-##   county (Intercept) <NA>  62.83  7.926
-## Residual        <NA> <NA> 290.4  17.04 
-m2 <- lmer(soyhect ~ 1 + soypix + (1|county), data=dat)
-fixef(m2)
-
    #> (Intercept)      soypix 
-#>  -3.8223566   0.4756781 
    ## (Intercept)      soypix 
-##  -3.8223566   0.4756781 
-vc(m2)
-
    #>       grp        var1 var2  vcov sdcor
-#>    county (Intercept) <NA> 239.2 15.47
-#>  Residual        <NA> <NA> 180   13.42
    ##      grp        var1 var2  vcov sdcor
-##   county (Intercept) <NA> 239.2 15.47
-## Residual        <NA> <NA> 180   13.42
-  
-# Predict for Humboldt county as in Battese 1982 table 2
-5.4662+.3878*290.74
-
    #> [1] 118.2152
    # 118.2152 # mu_i^0
-5.4662+.3878*290.74+ -2.8744
-
    #> [1] 115.3408
    # 115.3408 # mu_i^gamma
-(185.35+116.43)/2
-
    #> [1] 150.89
    # 150.89 # y_i bar
-  
-# Survey regression estimator of Battese 1988
-  
-# Delete the outlier
-dat2 <- subset(dat, !(county=="Hardin" & soyhect < 30))
-  
-# Results match top-right of Battese 1988, p. 33
-m3 <- lmer(cornhect ~ cornpix + soypix + (1|county), data=dat2)
-fixef(m3)
-
    #> (Intercept)     cornpix      soypix 
-#>  51.0703979   0.3287217  -0.1345684 
    ## (Intercept)     cornpix      soypix 
-##  51.0703979   0.3287217  -0.1345684 
-vc(m3)
-
    #>       grp        var1 var2  vcov sdcor
-#>    county (Intercept) <NA> 140   11.83
-#>  Residual        <NA> <NA> 147.3 12.14
    ##      grp        var1 var2  vcov sdcor
-##   county (Intercept) <NA> 140   11.83
-## Residual        <NA> <NA> 147.3 12.14
-m4 <- lmer(soyhect ~ cornpix + soypix + (1|county), data=dat2)
-fixef(m4)
-
    #>  (Intercept)      cornpix       soypix 
-#> -15.59027112   0.02717639   0.49439320 
    ##  (Intercept)      cornpix       soypix 
-## -15.59027098   0.02717639   0.49439320 
-vc(m4)
-
    #>       grp        var1 var2  vcov sdcor
-#>    county (Intercept) <NA> 247.5 15.73
-#>  Residual        <NA> <NA> 190.5 13.8 
    ##      grp        var1 var2  vcov sdcor
-##   county (Intercept) <NA> 247.5 15.73
-## Residual        <NA> <NA> 190.5 13.8 
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/beall.webworms-1.png b/docs/reference/beall.webworms-1.png deleted file mode 100644 index 1bb06875..00000000 Binary files a/docs/reference/beall.webworms-1.png and /dev/null differ diff --git a/docs/reference/beall.webworms-2.png b/docs/reference/beall.webworms-2.png deleted file mode 100644 index d5c86186..00000000 Binary files a/docs/reference/beall.webworms-2.png and /dev/null differ diff --git a/docs/reference/beall.webworms.html b/docs/reference/beall.webworms.html deleted file mode 100644 index 9c2680f4..00000000 --- a/docs/reference/beall.webworms.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - - -Counts of webworms in a beet field, with insecticide treatments. — beall.webworms • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Counts of webworms in a beet field, with insecticide treatments.

    - - -
    - -
    -

    Counts of webworms in a beet field, with insecticide treatments.

    -
    - - 
    data("beall.webworms")
    - - -

    Format

    - -

    A data frame with 1300 observations on the following 7 variables.

    -
    row

    row

    -
    col

    column

    -
    y

    count of webworms

    -
    block

    block

    -
    trt

    treatment

    -
    spray

    spray treatment yes/no

    -
    lead

    lead treatment yes/no

    - -
    - -

    Details

    - - - -

    The beet webworm lays egg masses as small as 1 egg, seldom exceeding 5 - eggs. The larvae can move freely, but usually mature on the plant on - which they hatch.

    -

    Each plot contained 25 unit areas, each 1 row by 3 feet long. The row - width is 22 inches. The arrangement of plots within the blocks seems - certain, but the arrangement of the blocks/treatments is not certain, - since the authors say "since the plots were 5 units long and 5 wide - it is only practicable to combine them into groups of 5 in one - direction or the other".

    -

    Treatment 1 = None. Treatment 2 = Contact spray. Treatment 3 = Lead - arsenate. Treatment 4 = Both spray, lead arsenate.

    -

    Source

    - -

    Beall, Geoffrey (1940). - The fit and significance of contagious distributions when applied to - observations on larval insects. - Ecology, 21, 460-474. Table 6. - https://doi.org/10.2307/1930285

    -

    References

    - -

    Michal Kosma et al. (2019). - Over-dispersed count data in crop and agronomy research. - Journal of Agronomy and Crop Science. - https://doi.org/10.1111/jac.12333

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(beall.webworms)
-dat <- beall.webworms
-
-# Match Beall table 1
-# with(dat, table(y,trt))
-
-libs(lattice)
-histogram(~y|trt, data=dat, layout=c(1,4), as.table=TRUE,
-          main="beall.webworms")
-
    
-# Visualize Beall table 6.  Block effects may exist, but barely.
-libs(desplot)
-grays <- colorRampPalette(c("white","#252525"))
-desplot(dat, y ~ col*row,
-        col.regions=grays(10),
-        at=0:10-0.5,
-        out1=block, out2=trt, num=trt, flip=TRUE, # aspect unknown
-        main="beall.webworms (count of worms)")
-
    
-# Following plot suggests interaction is needed
-# with(dat, interaction.plot(spray, lead, y))
-
-# Try the models of Kosma et al, Table 1.
-
-# Poisson model
-m1 <- glm(y ~ block + spray*lead, data=dat, family="poisson")
-logLik(m1) # -1497.719 (df=16)
-
    #> 'log Lik.' -1497.719 (df=16)
    
-# Negative binomial model
-# libs(MASS)
-# m2 <- glm.nb(y ~ block + spray*lead, data=dat)
-# logLik(m2) # -1478.341 (df=17)
-
-# # Conway=Maxwell-Poisson model (takes several minutes)
-# libs(spaMM)
-# # estimate nu parameter
-# m3 <- fitme(y ~ block + spray*lead, data=dat, family = COMPoisson())
-# logLik(m3) # -1475.999 
-# # Kosma logLik(m3)=-1717 seems too big. Typo? Different model?
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/beaven.barley-1.png b/docs/reference/beaven.barley-1.png deleted file mode 100644 index 7772e034..00000000 Binary files a/docs/reference/beaven.barley-1.png and /dev/null differ diff --git a/docs/reference/beaven.barley.html b/docs/reference/beaven.barley.html deleted file mode 100644 index e530c34e..00000000 --- a/docs/reference/beaven.barley.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - -Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Yields of 8 barley varieties in 1913 as used by Student.

    - - -
    - -
    -

    Yields of 8 barley varieties in 1913.

    -
    - - 
    data("beaven.barley")
    - - -

    Format

    - -

    A data frame with 160 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype

    -
    yield

    yield (grams)

    - -
    - -

    Details

    - - - -

    Eight races of barley were grown on a regular pattern of plots.

    -

    These data were prepared from Richey (1926) because the text was - cleaner.

    -

    Each plot was planted 40 inches on a side, but only the middle square - 36 inches on a side was harvested.

    -

    Field width: 32 plots * 3 feet = 96 feet

    -

    Field length: 5 plots * 3 feet = 15 feet

    -

    Source

    - - - -

    Student. (1923). - On testing varieties of cereals. - Biometrika, 271-293.

    -

    https://doi.org/10.1093/biomet/15.3-4.271

    -

    References

    - - - -

    Frederick D. Richey (1926). - The moving average as a basis for measuring correlated - variation in agronomic experiments. - Jour. Agr. Research, 32, 1161-1175.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(beaven.barley)
-dat <- beaven.barley
-
-# Match the means shown in Richey table IV
-tapply(dat$yield, dat$gen, mean)
-
    #>       a       b       c       d       e       f       g       h 
-#> 298.080 300.710 318.685 295.260 306.410 276.475 304.605 271.820 
    ##       a       b       c       d       e       f       g       h
-## 298.080 300.710 318.685 295.260 306.410 276.475 304.605 271.820
-
-# Compare to Student 1923, diagram I,II
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        aspect=15/96, # true aspect
-        main="beaven.barley - variety trial", text=gen)
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/becker.chicken-1.png b/docs/reference/becker.chicken-1.png deleted file mode 100644 index 99c60d98..00000000 Binary files a/docs/reference/becker.chicken-1.png and /dev/null differ diff --git a/docs/reference/becker.chicken.html b/docs/reference/becker.chicken.html deleted file mode 100644 index 7b3e0d6e..00000000 --- a/docs/reference/becker.chicken.html +++ /dev/null @@ -1,239 +0,0 @@ - - - - - - - - -Mating crosses of chickens — becker.chicken • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Mating crosses of chickens

    - - -
    - -
    -

    Mating crosses of chickens

    -
    - - 
    data("becker.chicken")
    - - -

    Format

    - -

    A data frame with 45 observations on the following 3 variables.

    -
    male

    male parent

    -
    female

    female parent

    -
    weight

    weight (g) at 8 weeks

    - - - -

    Details

    - - - -

    From a large flock White Rock chickens, five male sires were chosen - and mated to each of three female dams, producing 3 female progeny. - The data are body weights at eight weeks of age.

    -

    Becker (1984) used these data to demonstrate the calculation of - heritability.

    -

    Source

    - -

    Walter A. Becker (1984). - Manual of Quantitative Genetics, 4th ed. Page 83.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(becker.chicken)
-  dat <- becker.chicken
-
-  libs(lattice)
-  dotplot(weight ~ female, data=dat, group=male,
-          main="becker.chicken - progeny weight by M*F",
-          xlab="female parent",ylab="progeny weight",
-          auto.key=list(columns=5))
    
-  # Sums match Becker
-  # sum(dat$weight)
-  # aggregate(weight ~  male + female, dat, FUN=sum)
-
-  # Variance components
-  libs(lme4,lucid)
-  m1 <- lmer(weight ~  (1|male) + (1|female), data=dat)
-  # vc(m1)
-  ## grp        var1 var2      vcov    sdcor
-  ## 1   female (Intercept) <NA> 1096   33.1
-  ## 2     male (Intercept) <NA>  776.8 27.87
-  ## 3 Residual        <NA> <NA> 5524   74.32
-
-  # Calculate heritabilities
-  # s2m <- 776  # variability for males
-  # s2f <- 1095 # variability for females
-  # s2w <- 5524 # variability within crosses
-  # vp <- s2m + s2f + s2w # 7395
-  # 4*s2m/vp # .42 male heritability
-  #4*s2f/vp # .59 female heritability
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.bayesian-1.png b/docs/reference/besag.bayesian-1.png deleted file mode 100644 index 78936ac1..00000000 Binary files a/docs/reference/besag.bayesian-1.png and /dev/null differ diff --git a/docs/reference/besag.bayesian-2.png b/docs/reference/besag.bayesian-2.png deleted file mode 100644 index 7c813ba1..00000000 Binary files a/docs/reference/besag.bayesian-2.png and /dev/null differ diff --git a/docs/reference/besag.bayesian-3.png b/docs/reference/besag.bayesian-3.png deleted file mode 100644 index b0874e0a..00000000 Binary files a/docs/reference/besag.bayesian-3.png and /dev/null differ diff --git a/docs/reference/besag.bayesian-4.png b/docs/reference/besag.bayesian-4.png deleted file mode 100644 index b6753b82..00000000 Binary files a/docs/reference/besag.bayesian-4.png and /dev/null differ diff --git a/docs/reference/besag.bayesian.html b/docs/reference/besag.bayesian.html deleted file mode 100644 index 27be314d..00000000 --- a/docs/reference/besag.bayesian.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - - -RCB experiment of spring barley in United Kingdom — besag.bayesian • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    RCB experiment of spring barley in United Kingdom

    - - -
    - -
    -

    RCB experiment of spring barley in United Kingdom

    -
    - - - -

    Format

    - -

    A data frame with 225 observations on the following 4 variables.

    -
    col

    column (also blocking factor)

    -
    row

    row

    -
    yield

    yield

    -
    gen

    variety/genotype

    - -
    - -

    Details

    - -

    RCB design, each column is one rep.

    -

    Used with permission of David Higdon.

    -

    Source

    - -

    Besag, J. E., Green, P. J., Higdon, D. and Mengersen, K. (1995). - Bayesian computation and stochastic systems. - Statistical Science, 10, 3-66. - https://www.jstor.org/stable/2246224

    -

    References

    - -

    Davison, A. C. 2003. - Statistical Models. - Cambridge University Press. Pages 534-535.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(besag.bayesian)
-  dat <- besag.bayesian
-
-  # Yield values were scaled to unit variance
-  # var(dat$yield, na.rm=TRUE)
-  # .999
-
-  # Besag Fig 2. Reverse row numbers to match Besag, Davison
-  dat$rrow <- 76 - dat$row
-  libs(lattice)
-  xyplot(yield ~ rrow|col, dat, layout=c(1,3), type='s',
-         xlab="row", ylab="yield", main="besag.bayesian")
-
    
-  libs(asreml)
-
    #> Loading required package: Matrix
    #> Offline License checked out Mon Jan 11 17:07:42 2021
    
-  # Use asreml to fit a model with AR1 gradient in rows  
-  dat <- transform(dat, cf=factor(col), rf=factor(rrow))
-  m1 <- asreml(yield ~ -1 + gen, data=dat, random= ~ ar1v(rf))
-
    #> Offline License checked out Mon Jan 11 17:07:43 2021
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:43 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -94.4798      0.689559    149 17:07:43    0.0 (2 restrained)
-#>  2      -95.8277      0.763261    149 17:07:43    0.0 (2 restrained)
-#>  3      -96.2472      0.772025    149 17:07:43    0.0 (2 restrained)
-#>  4      -95.2684      0.758604    149 17:07:43    0.0 (1 restrained)
-#>  5      -88.7865      0.645542    149 17:07:43    0.0 (2 restrained)
-#>  6      -95.1889      0.757622    149 17:07:43    0.0 (1 restrained)
-#>  7      -90.7329      0.677440    149 17:07:43    0.0 (2 restrained)
    #> Warning: Oscillating parameter(s) reset to average value (iteration 7).
    #>  8      -95.6188      0.762867    149 17:07:43    0.0 (1 restrained)
-#>  9      -91.3575      0.705741    149 17:07:43    0.0
-#> 10      -87.7909      0.644795    149 17:07:43    0.0 (1 restrained)
-#> 11      -83.9513      0.601796    149 17:07:43    0.0
-#> 12      -81.8901      0.548584    149 17:07:43    0.0 (1 restrained)
-#> 13      -82.1499      0.568466    149 17:07:43    0.0
    #> Warning: Log-likelihood not converged
    #> Warning: Some components changed by more than 1% on the last iteration.
      m1 <- update(m1)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:43 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -82.2785      0.567157    149 17:07:43    0.0
-#>  2      -81.8595      0.561558    149 17:07:43    0.0
-#>  3      -81.3047      0.543601    149 17:07:43    0.0
-#>  4      -81.2374      0.541386    149 17:07:43    0.0
-#>  5      -81.1807      0.538231    149 17:07:43    0.0
-#>  6      -81.1162      0.532665    149 17:07:43    0.0
-#>  7      -81.1079      0.528363    149 17:07:43    0.0
-#>  8      -81.1147      0.531189    149 17:07:43    0.0
-#>  9      -81.1090      0.530548    149 17:07:43    0.0
-#> 10      -81.1089      0.530215    149 17:07:43    0.0
-#> 11      -81.1068      0.529576    149 17:07:43    0.0
-#> 12      -81.1077      0.529867    149 17:07:43    0.0
-#> 13      -81.1065      0.529367    149 17:07:43    0.0
    #> Warning: Log-likelihood not converged
    #> Warning: Some components changed by more than 1% on the last iteration.
      m1 <- update(m1)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:43 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -81.1071      0.529705    149 17:07:43    0.0
-#>  2      -81.1062      0.529401    149 17:07:43    0.0
      m1 <- update(m1)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:43 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -81.1063      0.529138    149 17:07:43    0.0
-#>  2      -81.1062      0.529216    149 17:07:43    0.0
        
-  # Visualize trends, similar to Besag figure 2.
-  # Need 'as.vector' because asreml4 uses a named vector
-  dat$res <- unname(m1$resid)
-  dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)]
-  dat <- transform(dat, fert=yield-geneff-res)
-  libs(lattice)
-  xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s',
-         main="besag.bayesian - Variety effects", ylim=c(5,15 ))
-
      xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s',
-         main="besag.bayesian - Fertility", ylim=c(-2,2))
-
      xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s',
-         main="besag.bayesian - Residuals", ylim=c(-4,4))
-
    
-# } 
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.beans-1.png b/docs/reference/besag.beans-1.png deleted file mode 100644 index 3e899319..00000000 Binary files a/docs/reference/besag.beans-1.png and /dev/null differ diff --git a/docs/reference/besag.beans-2.png b/docs/reference/besag.beans-2.png deleted file mode 100644 index 1fc16efd..00000000 Binary files a/docs/reference/besag.beans-2.png and /dev/null differ diff --git a/docs/reference/besag.beans.html b/docs/reference/besag.beans.html deleted file mode 100644 index c76d6f6e..00000000 --- a/docs/reference/besag.beans.html +++ /dev/null @@ -1,280 +0,0 @@ - - - - - - - - -Competition experiment in beans with height measurements — besag.beans • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Competition experiment in beans with height measurements

    - - -
    - -
    -

    Competition experiment in beans with height measurements

    -
    - - 
    data("besag.beans")
    - - -

    Format

    - -

    A data frame with 152 observations on the following 6 variables.

    -
    gen

    genotype / variety

    -
    height

    plot height, cm

    -
    yield

    plot yield, g

    -
    row

    row / block

    -
    rep

    replicate factor

    -
    col

    column

    - -
    - -

    Details

    - -

    Field beans of regular height were grown beside shorter varieties. - In each block, each variety occurred once as a left-side neighbor and - once as a right-side neighbor of every variety (including itself). - Border plots were placed at the ends of each block. - Each block with 38 adjacent plots. - Each plot was one row, 3 meters long with 50 cm spacing between rows. - No gaps between plots. Spacing between plants was 6.7 cm. - Four blocks (rows) were used, each with six replicates.

    -

    Plot yield and height was recorded.

    -

    Kempton and Lockwood used models that adjusted yield according to the - difference in height of neighboring plots.

    -

    Field length: 4 plots * 3m = 12m

    -

    Field width: 38 plots * 0.5 m = 19m

    -

    Source

    - -

    Julian Besag and Rob Kempton (1986). - Statistical Analysis of Field Experiments Using Neighbouring Plots. - Biometrics, 42, 231-251. Table 6. - https://doi.org/10.2307/2531047

    -

    References

    - -

    Kempton, RA and Lockwood, G. (1984). - Inter-plot competition in variety trials of field beans (Vicia faba L.). - The Journal of Agricultural Science, 103, 293--302.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(besag.beans)
-dat = besag.beans
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        aspect=12/19, out1=row, out2=rep, num=gen, cex=1, # true aspect
-        main="besag.beans")
-
    
-
-libs(reshape2)
-# Add a covariate = excess height of neighbors
-mat <- acast(dat, row~col, value.var='height')
-mat2 <- matrix(NA, nrow=4, ncol=38)
-mat2[,2:37] <- (mat[,1:36] + mat[,3:38] - 2*mat[,2:37])
-dat2 <- melt(mat2)
-colnames(dat2) <- c('row','col','cov')
-dat <- merge(dat, dat2)
-  
-# Drop border plots
-dat <- subset(dat, rep != 'R0')
-  
-libs(lattice)
-# Plot yield vs neighbors height advantage
-xyplot(yield~cov, data=dat, group=gen,
-       main="besag.beans",
-       xlab="Mean excess heights of neighbor plots",
-       auto.key=list(columns=3))
-
      
-# Trial mean.
-mean(dat$yield) # 391 matches Kempton table 3
-
    #> [1] 390.9722
      
-# Mean excess height of neighbors for each genotype
-# tapply(dat$cov, dat$gen, mean)/2 # Matches Kempton table 4
-
-# Variety means, matches Kempton table 4 mean yield
-m1 <- lm(yield ~ -1 + gen, dat)
-coef(m1)
-
    #>   genDwarf   genMaris genMetissa  genMinica  genStella genTopless 
-#>   318.7500   434.1667   387.7083   456.2500   469.5833   279.3750 
    
-# Full model used by Kempton, eqn 5.  Not perfectly clear.
-# Appears to include rep term, perhaps within block
-dat$blk <- factor(dat$row)
-dat$blkrep <- factor(paste(dat$blk, dat$rep))
-m2 <- lm(yield ~ -1 + gen + blkrep + cov, data=dat)
-coef(m2) # slope 'cov' = -.72, while Kempton says -.79
-
    #>    genDwarf    genMaris  genMetissa   genMinica   genStella  genTopless 
-#> 271.9249148 304.9869194 314.3474168 362.8740025 331.7998182 186.9650396 
-#>  blkrep1 R2  blkrep1 R3  blkrep1 R4  blkrep1 R5  blkrep1 R6  blkrep2 R1 
-#>   9.6257870  90.1207546 130.3503148 153.5628935 133.5867917  74.2635231 
-#>  blkrep2 R2  blkrep2 R3  blkrep2 R4  blkrep2 R5  blkrep2 R6  blkrep3 R1 
-#>  61.9201251  92.8503148 134.6257870 188.8163519  98.6716990  44.1666667 
-#>  blkrep3 R2  blkrep3 R3  blkrep3 R4  blkrep3 R5  blkrep3 R6  blkrep4 R1 
-#>  69.0339629 119.5169815  94.8792454 165.7974860  89.6616343  74.3962268 
-#>  blkrep4 R2  blkrep4 R3  blkrep4 R4  blkrep4 R5  blkrep4 R6         cov 
-#>  53.5509444  71.5578612  96.0150970 152.1855326  93.3094351  -0.7245278 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.checks-1.png b/docs/reference/besag.checks-1.png deleted file mode 100644 index 2dfbefbf..00000000 Binary files a/docs/reference/besag.checks-1.png and /dev/null differ diff --git a/docs/reference/besag.checks.html b/docs/reference/besag.checks.html deleted file mode 100644 index b46effac..00000000 --- a/docs/reference/besag.checks.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Check variety yields in winter wheat. — besag.checks • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Check variety yields in winter wheat.

    - - -
    - -
    -

    Check variety yields in winter wheat.

    -
    - - 
    data("besag.checks")
    - - -

    Format

    - -

    A data frame with 364 observations on the following 4 variables.

    -
    yield

    yield, units of 10g

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype/variety

    - -
    - -

    Details

    - -

    This data was used by Besag to show the spatial variation in a field - experiment, but Besag did not use the data for any analysis.

    -

    Yields of winter wheat varieties (Bounty and Huntsman) at the Plant - Breeding Institute, Cambridge, in 1980. These data are the 'checks' - genotypes in a larger variety trial.

    -

    There is a column of checks, then five columns of new varieties. Repeat.

    -

    Plot dimensions approx 1.5 by 4.5 metres

    -

    Field length: 52 rows * 4.5 m = 234 m

    -

    Field width: 31 columns * 1.5 m = 46.5

    -

    Electronic version of data supplied by David Clifford.

    -

    Source

    - -

    Besag, J.E. & Kempton R.A. (1986). - Statistical analysis of field experiments using neighbouring plots. - Biometrics, 42, 231-251. - https://doi.org/10.2307/2531047

    -

    References

    - -

    Kempton, Statistical Methods for Plant Variety Evaluation, page 91--92

    - -

    Examples

    - 
    library(agridat)
-data(besag.checks)
-dat <- besag.checks
-# \dontrun{
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          num=gen, aspect=234/46.5, # true aspect
-          main="besag.checks")
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.elbatan-1.png b/docs/reference/besag.elbatan-1.png deleted file mode 100644 index 9eae2c11..00000000 Binary files a/docs/reference/besag.elbatan-1.png and /dev/null differ diff --git a/docs/reference/besag.elbatan-2.png b/docs/reference/besag.elbatan-2.png deleted file mode 100644 index 35087d60..00000000 Binary files a/docs/reference/besag.elbatan-2.png and /dev/null differ diff --git a/docs/reference/besag.elbatan-3.png b/docs/reference/besag.elbatan-3.png deleted file mode 100644 index 93af5699..00000000 Binary files a/docs/reference/besag.elbatan-3.png and /dev/null differ diff --git a/docs/reference/besag.elbatan-4.png b/docs/reference/besag.elbatan-4.png deleted file mode 100644 index 832b163d..00000000 Binary files a/docs/reference/besag.elbatan-4.png and /dev/null differ diff --git a/docs/reference/besag.elbatan.html b/docs/reference/besag.elbatan.html deleted file mode 100644 index 86225d18..00000000 --- a/docs/reference/besag.elbatan.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - - -RCB experiment of wheat, 50 varieties in 3 blocks with strong - spatial trend. — besag.elbatan • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of wheat, 50 varieties in 3 blocks with strong - spatial trend.

    - - -
    - -
    -

    RCB experiment of wheat, 50 varieties in 3 blocks with strong - spatial trend.

    -
    - - - -

    Format

    - -

    A data frame with 150 observations on the following 4 variables.

    -
    yield

    yield of wheat

    -
    gen

    genotype, factor with 50 levels

    -
    col

    column/block

    -
    row

    row

    - -
    - -

    Details

    - -

    RCB experiment on wheat at El Batan, Mexico. There are three - single-column replicates with 50 varieties in each replicate.

    -

    Plot dimensions are not given by Besag.

    -

    Data retrieved from - https://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat

    -

    Used with permission of David Higdon.

    -

    Source

    - -

    Julian Besag and D Higdon, 1999. - Bayesian Analysis of Agricultural Field Experiments, - Journal of the Royal Statistical Society: Series B,61, 691--746. - Table 1. - https://doi.org/10.1111/1467-9868.00201

    -

    References

    - -

    Wilkinson 1984.

    -

    Besag & Seheult 1989.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(besag.elbatan)
-dat <- besag.elbatan
-
-libs(desplot)
-desplot(dat, yield~col*row,
-        num=gen, # aspect unknown
-        main="besag.elbatan - wheat yields")
-
    
-
-# Besag figure 1
-library(lattice)
-xyplot(yield~row|col, dat, type=c('l'),
-       layout=c(1,3), main="besag.elbatan wheat yields")
-
    
-
-# RCB
-m1 <- lm(yield ~ 0 + gen + factor(col), dat)
-p1 <- coef(m1)[1:50]
-
-# Formerly used gam package, but as of R 3.1, Rcmd check --as-cran
-# is complaining
-# Calls: plot.gam ... model.matrix.gam -> predict -> predict.gam -> array
-# but it works perfectly in interactive mode !!!
-# Remove the FALSE to run the code below
-if(is.element("gam", search())) detach(package:gam)
-libs(mgcv)
-
    #> Loading required package: nlme
    #> 
-#> Attaching package: 'nlme'
    #> The following object is masked from 'package:lme4':
-#> 
-#>     lmList
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     collapse
    #> This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
    m2 <- mgcv::gam(yield ~ -1 + gen + factor(col) + s(row), data=dat)
-plot(m2, residuals=TRUE, main="besag.elbatan")
-
    pred <- cbind(dat, predict(m2, dat, type="terms"))
-# Need to correct for the average loess effect, which is like
-# an overall intercept term.
-adjlo <-  mean(pred$"s(row)")
-p2 <- coef(m2)[1:50] + adjlo
-
-# Compare estimates
-lims <- range(c(p1,p2))
-plot(p1, p2, xlab="RCB prediction",
-     ylab="RCB with smooth trend (predicted)",
-     type='n', xlim=lims, ylim=lims,
-     main="besag.elbatan")
-
    text(p1, p2, 1:50, cex=.5)
-
    abline(0,1,col="gray")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.endive-1.png b/docs/reference/besag.endive-1.png deleted file mode 100644 index cf7de468..00000000 Binary files a/docs/reference/besag.endive-1.png and /dev/null differ diff --git a/docs/reference/besag.endive.html b/docs/reference/besag.endive.html deleted file mode 100644 index a44cc372..00000000 --- a/docs/reference/besag.endive.html +++ /dev/null @@ -1,304 +0,0 @@ - - - - - - - - -Presence of footroot disease in an endive field — besag.endive • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Presence of footroot disease in an endive field

    - - -
    - -
    -

    Presence of footroot disease in an endive field

    -
    - - - -

    Format

    - -

    A data frame with 2506 observations on the following 3 variables.

    -
    col

    column

    -
    row

    row

    -
    disease

    plant is diseased, Y=yes,N=no

    - -
    - -

    Details

    - -

    In a field of endives, does each plant have footrot, or not? Data are - binary on a lattice of 14 x 179 plants.

    -

    Modeled as an autologistic distribution.

    -

    We assume the endives are a single genotype.

    -

    Besag (1978) may have had data taken at 4 time points. This data - was extracted from Friel and Pettitt. It is not clear what, if any, - time point was used.

    -

    Friel does not give the dimensions. Besag is not available.

    -

    Source

    - -

    J Besag (1978). - Some Methods of Statistical Analysis for Spatial Data. - Bulletin of the International Statistical Institute, 47, 77-92.

    -

    References

    - -

    N Friel & A. N Pettitt (2004). Likelihood Estimation and Inference - for the Autologistic Model. - Journal of Computational and Graphical Statistics, 13:1, 232-246. - https://doi.org/10.1198/1061860043029

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(besag.endive)
-  dat <- besag.endive
-
-  # Incidence map.  Figure 2 of Friel and Pettitt
-  libs(desplot)
-  grays <- colorRampPalette(c("#d9d9d9","#252525"))
-  desplot(dat, disease~col*row,
-          col.regions=grays(2),
-          aspect = 0.5, # aspect unknown
-          main="besag.endive - Disease incidence")
-
      
-  
-  # Besag (2000) "An Introduction to Markov Chain Monte Carlo" suggested
-  # that the autologistic model is not a very good fit for this data.
-  # We try it anyway.  No idea if this is correct or how to interpret...
-  
-  libs(ngspatial)
-
    #> Loading required package: Rcpp
    #> Loading required package: batchmeans
    #> batchmeans: Consistent Batch Means Estimation of Monte Carlo Standard Errors
-#> Version 1.0-4 created on 2020-05-07.
-#> copyright (c) 2012-2020, Murali Haran, Penn State University
-#>                          John Hughes
-#> For citation information, type citation("batchmeans").
-#> Type help(package = batchmeans) to get started.
    #> ngspatial: Fitting the Centered Autologistic and Sparse Spatial Generalized
-#> Linear Mixed Models for Areal Data
-#> Version 1.2-2 created on 2020-05-08.
-#> copyright (c) 2013-2020, John Hughes
-#> For citation information, type citation("ngspatial").
-#> Type help(package = ngspatial) to get started.
      A = adjacency.matrix(179,14)
-  X = cbind(x=dat$col, y=dat$row)
-  Z = as.numeric(dat$disease=="Y")
-  m1 <- autologistic(Z ~ 0+X, A=A, control=list(confint="none"))
-  
-  summary(m1)
-
    #> 
-#> Call:
-#> 
-#> autologistic(formula = Z ~ 0 + X, A = A, control = list(confint = "none"))
-#> 
-#> Control parameters:
-#>             
-#> confint none
-#> 
-#> Coefficients:
-#> 
-#>      Estimate Lower Upper MCSE
-#> Xx  -0.007824    NA    NA   NA
-#> Xy  -0.144800    NA    NA   NA
-#> eta  0.806200    NA    NA   NA
-#> 
-#> Number of iterations: 0 
-#> 
      ## Coefficients:
-  ##      Estimate Lower Upper MCSE
-  ## Xx  -0.007824    NA    NA   NA
-  ## Xy  -0.144800    NA    NA   NA
-  ## eta  0.806200    NA    NA   NA
-
-
-  libs(asreml)
-    
-  # Now try an AR1xAR1 model.
-  dat2 <- transform(dat, xf=factor(col), yf=factor(row),
-                    pres=as.numeric(disease=="Y"))
-  
-  m2 <- asreml(pres ~ 1, data=dat2,
-               resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:46 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1340.117      0.128274   2505 17:07:46    0.0
-#>  2      1343.298      0.128543   2505 17:07:46    0.0
-#>  3      1345.676      0.129204   2505 17:07:46    0.0
-#>  4      1346.278      0.129798   2505 17:07:46    0.0
-#>  5      1346.331      0.130062   2505 17:07:46    0.0
-#>  6      1346.331      0.130074   2505 17:07:46    0.0
      # The 0/1 response is arbitrary, but there is some suggestion
-  # of auto-correlation in the x (.17) and y (.10) directions,
-  # suggesting the pattern is more 'patchy' than just random noise,
-  # but is it meaningful?
-  
-  libs(lucid)
-  vc(m2)
-
    #>        effect component std.error z.ratio bound %ch
-#>       xf:yf!R   0.1301   0.003798    34       P   0
-#>  xf:yf!xf!cor   0.1699   0.01942      8.7     U   0
-#>  xf:yf!yf!cor   0.09842  0.02038      4.8     U   0
      ##       effect component std.error z.ratio bound 
-  ##     xf:yf(R)   0.1301   0.003798    34       P   0
-  ## xf:yf!xf!cor   0.1699   0.01942      8.7     U   0
-  ## xf:yf!yf!cor   0.09842  0.02038      4.8     U   0
-  
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.met-1.png b/docs/reference/besag.met-1.png deleted file mode 100644 index 693d7bbd..00000000 Binary files a/docs/reference/besag.met-1.png and /dev/null differ diff --git a/docs/reference/besag.met-2.png b/docs/reference/besag.met-2.png deleted file mode 100644 index 9506e834..00000000 Binary files a/docs/reference/besag.met-2.png and /dev/null differ diff --git a/docs/reference/besag.met-3.png b/docs/reference/besag.met-3.png deleted file mode 100644 index b80b07ca..00000000 Binary files a/docs/reference/besag.met-3.png and /dev/null differ diff --git a/docs/reference/besag.met.html b/docs/reference/besag.met.html deleted file mode 100644 index bbd7a5af..00000000 --- a/docs/reference/besag.met.html +++ /dev/null @@ -1,451 +0,0 @@ - - - - - - - - -Multi-environment trial of corn, incomplete-block design — besag.met • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn, incomplete-block design

    - - -
    - -
    -

    Multi-environment trial of corn, incomplete-block designlocation.

    -
    - - - -

    Format

    - -

    A data frame with 1152 observations on the following 7 variables.

    -
    county

    county

    -
    row

    row

    -
    col

    column

    -
    rep

    rep

    -
    block

    incomplete block

    -
    yield

    yield

    -
    gen

    genotype, 1-64

    - -
    - -

    Details

    - - - -

    Multi-environment trial of 64 corn hybrids in six counties in North - Carolina. Each location had 3 replicates in in incomplete-block - design with an 18x11 lattice of plots whose length-to-width ratio was - about 2:1.

    -

    Note: In the original data, each county had 6 missing plots. This - data has rows for each missing plot that uses the same - county/block/rep to fill-out the row, sets the genotype to G01, and - sets the yield to missing. These missing values were added to the - data so that asreml could more easily do AR1xAR1 analysis using - rectangular regions.

    -

    Each location/panel is:

    -

    Field length: 18 rows * 2 units = 36 units.

    -

    Field width: 11 plots * 1 unit = 11 units.

    - -

    Retrieved from - https://web.archive.org/web/19990505223413/www.stat.duke.edu/~higdon/trials/nc.dat

    -

    Used with permission of David Higdon.

    -

    Source

    - -

    Julian Besag and D Higdon, 1999. - Bayesian Analysis of Agricultural Field Experiments, - Journal of the Royal Statistical Society: Series B, 61, 691--746. - Table 1. - https://doi.org/10.1111/1467-9868.00201

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(besag.met)
-  dat <- besag.met
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row|county,
-          aspect=36/11, # true aspect
-          out1=rep, out2=block,
-          main="besag.met")
-
    
-
-  # Average reps
-  datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean)
-  
-  # Sections below fit heteroskedastic variance models (variance for each variety)
-  # asreml takes 1 second, lme 73 seconds, SAS PROC MIXED 30 minutes
-
-
-
-  # lme
-  # libs(nlme)
-  # m1l <- lme(yield ~ -1 + gen, data=datm, random=~1|county,
-  #            weights = varIdent(form=~ 1|gen))
-  # m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2
-  ##           G02    G03    G04    G05    G06    G07    G08
-  ##  91.90 210.75  63.03 112.05  28.39 237.36  72.72  42.97
-  ## ... etc ...
-  
-
-  # Note, the FA biplots from asreml3 and asreml4 are surprisingly
-  # different from each other.  The predicted-value biplots are
-  # almost identical.
-  
-  libs(asreml)
-  # asreml4
-
-  # Average reps
-  datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean)
-  # asreml Using 'rcov' ALWAYS requires sorting the data
-  datm <- datm[order(datm$gen),]
-  
-  m1 <- asreml(yield ~ gen, data=datm,
-               random = ~ county,
-               residual = ~ dsum( ~ units|gen))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1172.163           1.0    320 17:07:47    0.0 (60 restrained)
-#>  2     -1011.805           1.0    320 17:07:47    0.0 (1 restrained)
-#>  3      -970.591           1.0    320 17:07:47    0.0
-#>  4      -940.691           1.0    320 17:07:47    0.0
-#>  5      -927.170           1.0    320 17:07:47    0.0
-#>  6      -923.727           1.0    320 17:07:47    0.0
-#>  7      -923.239           1.0    320 17:07:47    0.0
-#>  8      -922.976           1.0    320 17:07:47    0.0
-#>  9      -922.903           1.0    320 17:07:47    0.0
-#> 10      -922.899           1.0    320 17:07:47    0.0
      libs(lucid)
-  vc(m1)[1:7,]
-
    #>     effect component std.error z.ratio bound %ch
-#>     county   1324       836.1      1.6     P 0.2
-#>  gen_G01!R     91.98     58.91     1.6     P 0.1
-#>  gen_G02!R    210.6     133.6      1.6     P 0.1
-#>  gen_G03!R     63.06     40.58     1.6     P 0.1
-#>  gen_G04!R    112.1      71.59     1.6     P 0.1
-#>  gen_G05!R     28.35     18.57     1.5     P 0.2
-#>  gen_G06!R    237.4     150.8      1.6     P 0  
      ##      effect component std.error z.ratio bound 
-  ##    county   1324       836.1      1.6     P 0.2
-  ## gen_G01!R     91.98     58.91     1.6     P 0.1
-  ## gen_G02!R    210.6     133.6      1.6     P 0.1
-  ## gen_G03!R     63.06     40.58     1.6     P 0.1
-  ## gen_G04!R    112.1      71.59     1.6     P 0.1
-  ## gen_G05!R     28.35     18.57     1.5     P 0.2
-  ## gen_G06!R    237.4     150.8      1.6     P 0  
-  
-  # We get the same results from asreml & lme
-  # plot(m1$vparameters[-1],
-  #      m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2)
-  
-  # The following example shows how to construct a GxE biplot
-  # from the FA2 model.
-
-  
-  dat <- besag.met
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$county, dat$xf, dat$yf), ]
-
-  # First, AR1xAR1
-  m1 <- asreml(yield ~ county, data=dat,
-               random = ~ gen:county,
-               residual = ~ dsum( ~ ar1(xf):ar1(yf)|county))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3931.453           1.0   1146 17:07:47    0.0
-#>  2     -3846.882           1.0   1146 17:07:47    0.0
-#>  3     -3765.127           1.0   1146 17:07:47    0.0
-#>  4     -3724.260           1.0   1146 17:07:47    0.0
-#>  5     -3710.877           1.0   1146 17:07:47    0.0
-#>  6     -3709.388           1.0   1146 17:07:47    0.0
-#>  7     -3709.308           1.0   1146 17:07:47    0.0
-#>  8     -3709.304           1.0   1146 17:07:47    0.0
      # Add FA1
-  m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3960.869           1.0   1146 17:07:47    0.0 (1 restrained)
-#>  2     -3838.939           1.0   1146 17:07:47    0.0
-#>  3     -3738.501           1.0   1146 17:07:48    0.0
-#>  4     -3685.886           1.0   1146 17:07:48    0.0
-#>  5     -3667.580           1.0   1146 17:07:48    0.0
-#>  6     -3665.255           1.0   1146 17:07:48    0.0
-#>  7     -3665.089           1.0   1146 17:07:48    0.0
-#>  8     -3665.077           1.0   1146 17:07:48    0.0
-#>  9     -3665.076           1.0   1146 17:07:48    0.0
      # FA2
-  m3 <- update(m2, random=~gen:fa(county,2))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:48 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3960.862           1.0   1146 17:07:48    0.0 (1 restrained)
-#>  2     -3926.560           1.0   1146 17:07:48    0.0 (1 restrained)
-#>  3     -3833.085           1.0   1146 17:07:48    0.0 (1 restrained)
-#>  4     -3735.147           1.0   1146 17:07:48    0.0 (1 restrained)
-#>  5     -3683.543           1.0   1146 17:07:48    0.0 (1 restrained)
-#>  6     -3665.368           1.0   1146 17:07:48    0.0
-#>  7     -3662.711           1.0   1146 17:07:48    0.0
-#>  8     -3662.471           1.0   1146 17:07:48    0.0
-#>  9     -3662.452           1.0   1146 17:07:48    0.0
-#> 10     -3662.449           1.0   1146 17:07:48    0.0
-#> 11     -3662.449           1.0   1146 17:07:48    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      asreml.options(extra=50)
-  m3 <- update(m3, maxit=50)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:48 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3662.448           1.0   1146 17:07:48    0.0
-#>  2     -3662.448           1.0   1146 17:07:48    0.0
-#>  3     -3662.448           1.0   1146 17:07:48    0.0
-#>  4     -3662.448           1.0   1146 17:07:48    0.0
-#>  5     -3662.448           1.0   1146 17:07:49    0.0
-#>  6     -3662.448           1.0   1146 17:07:49    0.0
-#>  7     -3662.448           1.0   1146 17:07:49    0.0
-#>  8     -3662.448           1.0   1146 17:07:49    0.0
-#>  9     -3662.448           1.0   1146 17:07:49    0.0
-#> 10     -3662.448           1.0   1146 17:07:49    0.0
-#> 11     -3662.448           1.0   1146 17:07:49    0.0
-#> 12     -3662.448           1.0   1146 17:07:49    0.0
-#> 13     -3662.448           1.0   1146 17:07:49    0.0
-#> 14     -3662.448           1.0   1146 17:07:49    0.0
-#> 15     -3662.448           1.0   1146 17:07:49    0.0
-#> 16     -3662.448           1.0   1146 17:07:49    0.0
-#> 17     -3662.448           1.0   1146 17:07:49    0.0
-#> 18     -3662.448           1.0   1146 17:07:49    0.0
-#> 19     -3662.448           1.0   1146 17:07:49    0.0
-#> 20     -3662.448           1.0   1146 17:07:49    0.0
-#> 21     -3662.448           1.0   1146 17:07:49    0.0
-#> 22     -3662.448           1.0   1146 17:07:49    0.0
-#> 23     -3662.447           1.0   1146 17:07:49    0.0
-#> 24     -3662.447           1.0   1146 17:07:49    0.0
-#> 25     -3662.447           1.0   1146 17:07:49    0.0
-#> 26     -3662.447           1.0   1146 17:07:49    0.0
-#> 27     -3662.447           1.0   1146 17:07:49    0.0
-#> 28     -3662.447           1.0   1146 17:07:49    0.0
-#> 29     -3662.447           1.0   1146 17:07:49    0.0
-#> 30     -3662.447           1.0   1146 17:07:49    0.0
-#> 31     -3662.447           1.0   1146 17:07:49    0.0
-#> 32     -3662.447           1.0   1146 17:07:49    0.0
-#> 33     -3662.447           1.0   1146 17:07:49    0.0
-#> 34     -3662.447           1.0   1146 17:07:49    0.0
-#> 35     -3662.447           1.0   1146 17:07:49    0.0
-#> 36     -3662.447           1.0   1146 17:07:50    0.0
-#> 37     -3662.447           1.0   1146 17:07:50    0.0
-#> 38     -3662.447           1.0   1146 17:07:50    0.0
-#> 39     -3662.447           1.0   1146 17:07:50    0.0
-#> 40     -3662.447           1.0   1146 17:07:50    0.0
-#> 41     -3662.447           1.0   1146 17:07:50    0.0
-#> 42     -3662.447           1.0   1146 17:07:50    0.0
-#> 43     -3662.447           1.0   1146 17:07:50    0.0
-#> 44     -3662.447           1.0   1146 17:07:50    0.0
-#> 45     -3662.447           1.0   1146 17:07:50    0.0
-#> 46     -3662.447           1.0   1146 17:07:50    0.0
-#> 47     -3662.447           1.0   1146 17:07:50    0.0
-#> 48     -3662.446           1.0   1146 17:07:50    0.0
-#> 49     -3662.446           1.0   1146 17:07:50    0.0
-#> 50     -3662.446           1.0   1146 17:07:50    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      asreml.options(extra=0)
-
-  # Use the loadings to make a biplot
-  vars <- vc(m3)
-  psi <- vars[grepl("!var$", vars$effect), "component"]
-  la1 <- vars[grepl("!fa1$", vars$effect), "component"]
-  la2 <- vars[grepl("!fa2$", vars$effect), "component"]
-  mat <- as.matrix(data.frame(psi, la1, la2))
-  # I tried using rotate.fa=FALSE, but it did not seem to
-  # give orthogonal vectors.  Rotate by hand.
-  rot <- svd(mat[,-1])$v # rotation matrix
-  lam <- mat[,-1] 
-  colnames(lam) <- c("load1", "load2")
-
-  co3 <- coef(m3)$random # Scores are the GxE coefficients
-  ix1 <- grepl("_Comp1$", rownames(co3))
-  ix2 <- grepl("_Comp2$", rownames(co3))
-  sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE)
-  sco <- sco 
-  dimnames(sco) <- list(levels(dat$gen) , c('load1','load2'))
-  rownames(lam) <- levels(dat$county)
-  sco[,1:2] <- -1 * sco[,1:2]
-  lam[,1:2] <- -1 * lam[,1:2]
-  biplot(sco, lam, cex=.5, main="FA2 coefficient biplot (asreml4)")
-
      # G variance matrix
-  gvar <- lam 
-  
-  # Now get predictions and make an ordinary biplot
-  p3 <- predict(m3, data=dat, classify="county:gen")
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:50 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3662.446           1.0   1146 17:07:50    0.1
-#>  2     -3662.446           1.0   1146 17:07:50    0.0
-#>  3     -3662.446           1.0   1146 17:07:50    0.1
    #> Warning: Some components changed by more than 1% on the last iteration.
      p3 <- p3$pvals
-  libs("gge")  
-
    #> 
-#> Attaching package: 'gge'
    #> The following object is masked from 'package:desplot':
-#> 
-#>     RedGrayBlue
      bi3 <- gge(p3, predicted.value ~ gen*county, scale=FALSE)
-  if(interactive()) dev.new()
-  # Very similar to the coefficient biplot
-  biplot(bi3, stand=FALSE, main="SVD biplot of FA2 predictions")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/besag.triticale-1.png b/docs/reference/besag.triticale-1.png deleted file mode 100644 index 8ef3266e..00000000 Binary files a/docs/reference/besag.triticale-1.png and /dev/null differ diff --git a/docs/reference/besag.triticale-2.png b/docs/reference/besag.triticale-2.png deleted file mode 100644 index 4623fe10..00000000 Binary files a/docs/reference/besag.triticale-2.png and /dev/null differ diff --git a/docs/reference/besag.triticale.html b/docs/reference/besag.triticale.html deleted file mode 100644 index 176807c1..00000000 --- a/docs/reference/besag.triticale.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - - -Four-way factorial agronomic experiment in triticale — besag.triticale • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Four-way factorial agronomic experiment in triticale

    - - -
    - -
    -

    Four-way factorial agronomic experiment in triticale

    -
    - - 
    data("besag.triticale")
    - - -

    Format

    - -

    A data frame with 54 observations on the following 7 variables.

    -
    yield

    yield, g/m^2

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype / variety, 3 levels

    -
    rate

    seeding rate, kg/ha

    -
    nitro

    nitrogen rate, kw/ha

    -
    regulator

    growth regulator, 3 levels

    - -
    - -

    Details

    - -

    Experiment conducted as a factorial on the yields of triticale. Fully - randomized. Plots were 1.5m x 5.5m, but the orientation is not clear.

    -

    Besag and Kempton show how accounting for neighbors changes - non-significant genotype differences into significant differences.

    -

    Source

    - -

    Julian Besag and Rob Kempton (1986). - Statistical Analysis of Field Experiments Using Neighbouring Plots. - Biometrics, 42, 231-251. Table 2. - https://doi.org/10.2307/2531047

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(besag.triticale)
-  dat <- besag.triticale
-  dat <- transform(dat, rate=factor(rate), nitro=factor(nitro))
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          # aspect unknown
-          main="besag.triticale")
-
    
-  # Besag & Kempton are not perfectly clear on the model, but
-  # indicate that there was no evidence of any two-way interactions.
-  # A reduced, main-effect model had genotype effects that were
-  # "close to significant" at the five percent level.
-  # The model below has p-value of gen at .04, so must be slightly
-  # different than their model.
-  m2 <- lm(yield ~ gen + rate + nitro + regulator + yf, data=dat)
-  anova(m2)
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq F value  Pr(>F)  
-#> gen        2  68245   34123  3.4115 0.04174 *
-#> rate       1  31344   31344  3.1338 0.08346 .
-#> nitro      1   5547    5547  0.5546 0.46032  
-#> regulator  2  36612   18306  1.8302 0.17212  
-#> yf         2  34756   17378  1.7374 0.18757  
-#> Residuals 45 450098   10002                  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-  # Similar, but not exact, to Besag figure 5
-  dat$res <- resid(m2)
-  libs(lattice)
-  xyplot(res ~ col|as.character(row), data=dat,
-         as.table=TRUE, type="s", layout=c(1,3),
-         main="besag.triticale")
-
      
-  libs(asreml) # asreml4
-
-  # Besag uses an adjustment based on neighboring plots.
-  # This analysis fits the standard AR1xAR1 residual model
-  
-  dat <- dat[order(dat$xf, dat$yf), ]
-  m3 <- asreml(yield ~ gen + rate + nitro + regulator +
-                 gen:rate + gen:nitro + gen:regulator +
-                 rate:nitro + rate:regulator +
-                 nitro:regulator + yf, data=dat,
-               resid = ~ ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:52 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -177.197      8485.373     32 17:07:52    0.0 (1 restrained)
-#>  2      -170.008      6592.311     32 17:07:52    0.0 (1 restrained)
-#>  3      -165.648      8290.072     32 17:07:52    0.0
-#>  4      -165.203     13100.375     32 17:07:52    0.0
-#>  5      -165.198     12237.087     32 17:07:52    0.0
-#>  6      -165.198     12425.594     32 17:07:52    0.0
      wald(m3) # Strongly significant gen, rate, regulator
-
    #> Wald tests for fixed effects.
-#> Response: yield
-#> Terms added sequentially; adjusted for those above.
-#> 
-#>                 Df Sum of Sq Wald statistic Pr(Chisq)    
-#> (Intercept)      1   1283427        103.289 < 2.2e-16 ***
-#> gen              2    908222         36.546 1.159e-08 ***
-#> rate             1    105412          8.483  0.003584 ** 
-#> nitro            1       275          0.022  0.881825    
-#> regulator        2    232696          9.364  0.009262 ** 
-#> yf               2      3787          0.152  0.926634    
-#> gen:rate         2      1383          0.056  0.972564    
-#> gen:nitro        2     30994          1.247  0.536019    
-#> gen:regulator    4     37599          0.756  0.944186    
-#> rate:nitro       1      1484          0.119  0.729610    
-#> rate:regulator   2     49772          2.003  0.367366    
-#> nitro:regulator  2     41220          1.659  0.436342    
-#> residual (MS)          12426                             
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      ##                 Df Sum of Sq Wald statistic Pr(Chisq)    
-  ## (Intercept)      1   1288255        103.971 < 2.2e-16 ***
-  ## gen              2    903262         72.899 < 2.2e-16 ***
-  ## rate             1    104774          8.456  0.003638 ** 
-  ## nitro            1       282          0.023  0.880139    
-  ## regulator        2    231403         18.676 8.802e-05 ***
-  ## yf               2      3788          0.306  0.858263    
-  ## gen:rate         2      1364          0.110  0.946461    
-  ## gen:nitro        2     30822          2.488  0.288289    
-  ## gen:regulator    4     37269          3.008  0.556507    
-  ## rate:nitro       1      1488          0.120  0.728954    
-  ## rate:regulator   2     49296          3.979  0.136795    
-  ## nitro:regulator  2     41019          3.311  0.191042    
-  ## residual (MS)          12391                             
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/blackman.wheat-1.png b/docs/reference/blackman.wheat-1.png deleted file mode 100644 index cb718ff4..00000000 Binary files a/docs/reference/blackman.wheat-1.png and /dev/null differ diff --git a/docs/reference/blackman.wheat-2.png b/docs/reference/blackman.wheat-2.png deleted file mode 100644 index dbb494e8..00000000 Binary files a/docs/reference/blackman.wheat-2.png and /dev/null differ diff --git a/docs/reference/blackman.wheat-3.png b/docs/reference/blackman.wheat-3.png deleted file mode 100644 index 6e8af451..00000000 Binary files a/docs/reference/blackman.wheat-3.png and /dev/null differ diff --git a/docs/reference/blackman.wheat.html b/docs/reference/blackman.wheat.html deleted file mode 100644 index 35397b68..00000000 --- a/docs/reference/blackman.wheat.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat, conventional and semi-dwarf varieties

    - - -
    - -
    -

    Multi-environment trial of wheat, conventional and semi-dwarf - varieties, 7 locs with low/high fertilizer levels.

    -
    - - - -

    Format

    - -

    A data frame with 168 observations on the following 5 variables.

    -
    gen

    genotype

    -
    loc

    location

    -
    nitro

    nitrogen fertilizer, low/high

    -
    yield

    yield (g/m^2)

    -
    type

    type factor, conventional/semi-dwarf

    - -
    - -

    Details

    - -

    Conducted in U.K. in 1975. Each loc had three reps, two nitrogen - treatments.

    -

    Locations were Begbroke, Boxworth, Crafts Hill, Earith, Edinburgh, - Fowlmere, Trumpington.

    -

    At the two highest-yielding locations, Earith and Edinburgh, yield was - _lower_ for the high-nitrogen treatment. Blackman et al. say "it - seems probable that effects on development and structure of the crop - were responsible for the reductions in yield at high nitrogen".

    -

    Source

    - -

    Blackman, JA and Bingham, J. and Davidson, JL (1978). - Response of semi-dwarf and conventional winter wheat varieties to the - application of nitrogen fertilizer. - The Journal of Agricultural Science, 90, 543--550. - https://doi.org/10.1017/S0021859600056070

    -

    References

    - -

    Gower, J. and Lubbe, S.G. and Gardner, S. and Le Roux, N. (2011). - Understanding Biplots, Wiley.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(blackman.wheat)
-dat <- blackman.wheat
-
-libs(lattice)
-
-# Semi-dwarf generally higher yielding than conventional
-# bwplot(yield~type|loc,dat, main="blackman.wheat")
-
-# Peculiar interaction--Ear/Edn locs have reverse nitro response
-dotplot(gen~yield|loc, dat, group=nitro, auto.key=TRUE,
-        main="blackman.wheat: yield for low/high nitrogen")
-
    
-# Height data from table 6 of Blackman.  Height at Trumpington loc.
-# Shorter varieties have higher yields, greater response to nitro.
-heights <- data.frame(gen=c("Cap", "Dur", "Fun", "Hob", "Hun", "Kin",
-                            "Ran", "Spo", "T64", "T68","T95", "Tem"),
-                      ht=c(101,76,76,80,98,88,98,81,86,73,78,93))
-dat$height <- heights$ht[match(dat$gen, heights$gen)]
-xyplot(yield~height|loc,dat,group=nitro,type=c('p','r'),
-       main="blackman.wheat",
-       subset=loc=="Tru", auto.key=TRUE)
-
    
-
-libs(reshape2)
-# AMMI-style biplot Fig 6.4 of Gower 2011
-dat$env <- factor(paste(dat$loc,dat$nitro,sep="-"))
-datm <- acast(dat, gen~env, value.var='yield')
-datm <- sweep(datm, 1, rowMeans(datm))
-datm <- sweep(datm, 2, colMeans(datm))
-biplot(prcomp(datm), main="blackman.wheat AMMI-style biplot")
-
    
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bliss.borers-1.png b/docs/reference/bliss.borers-1.png deleted file mode 100644 index 932c603f..00000000 Binary files a/docs/reference/bliss.borers-1.png and /dev/null differ diff --git a/docs/reference/bliss.borers-2.png b/docs/reference/bliss.borers-2.png deleted file mode 100644 index 9a291eaa..00000000 Binary files a/docs/reference/bliss.borers-2.png and /dev/null differ diff --git a/docs/reference/bliss.borers-3.png b/docs/reference/bliss.borers-3.png deleted file mode 100644 index dfb782ba..00000000 Binary files a/docs/reference/bliss.borers-3.png and /dev/null differ diff --git a/docs/reference/bliss.borers-4.png b/docs/reference/bliss.borers-4.png deleted file mode 100644 index 1782373e..00000000 Binary files a/docs/reference/bliss.borers-4.png and /dev/null differ diff --git a/docs/reference/bliss.borers.html b/docs/reference/bliss.borers.html deleted file mode 100644 index a3e2b7c8..00000000 --- a/docs/reference/bliss.borers.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Corn borer infestation under four treatments — bliss.borers • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Corn borer infestation under four treatments

    - - -
    - -
    -

    Corn borer infestation under four treatments

    -
    - - - -

    Format

    - -

    A data frame with 48 observations on the following 3 variables.

    -
    borers

    number of borers per hill

    -
    treat

    treatment factor

    -
    freq

    frequency of the borer count

    - -
    - -

    Details

    - -

    Four treatments to control corn borers. Treatment 1 is the control.

    -

    In 15 blocks, for each treatment, 8 hills of plants were examined, and the - number of corn borers present was recorded. The data here are - aggregated across blocks.

    -

    Bliss mentions that the level of infestation varied significantly - between the blocks.

    -

    Source

    - -

    C. Bliss and R. A. Fisher. (1953). - Fitting the Negative Binomial Distribution to Biological Data. - Biometrics, 9, 176--200. Table 3. - https://doi.org/10.2307/3001850

    -

    Geoffrey Beall. 1940. The Fit and Significance of Contagious - Distributions when Applied to Observations on Larval Insects. - Ecology, 21, 460-474. Page 463. - https://doi.org/10.2307/1930285

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(bliss.borers)
-dat <- bliss.borers
-
-# Add 0 frequencies
-dat0 <- expand.grid(borers=0:26, treat=c('T1','T2','T3','T4'))
-dat0 <- merge(dat0,dat, all=TRUE)
-dat0$freq[is.na(dat0$freq)] <- 0
-
-# Expand to individual (non-aggregated) counts for each hill
-dd <- data.frame(borers = rep(dat0$borers, times=dat0$freq),
-                 treat = rep(dat0$treat, times=dat0$freq))
-
-libs(lattice)
-histogram(~borers|treat, dd, type='count', breaks=0:27-.5,
-          layout=c(1,4), main="bliss.borers", xlab="Borers per hill")
-
    
-
-libs(MASS)
-
    #> 
-#> Attaching package: 'MASS'
    #> The following object is masked from 'package:asreml':
-#> 
-#>     oats
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     select
      m1 <- glm.nb(borers~0+treat, data=dd)
-  # Bliss, table 3, presents treatment means, which are matched by:
-  exp(coef(m1)) # 4.033333 3.166667 1.483333 1.508333
-
    #>  treatT1  treatT2  treatT3  treatT4 
-#> 4.033333 3.166667 1.483333 1.508333 
      # Bliss gives treatment values k = c(1.532,1.764,1.333,1.190).
-  # The mean of these is 1.45, similar to this across-treatment estimate
-  m1$theta # 1.47
-
    #> [1] 1.47145
    
-
-# Plot observed and expected distributions for treatment 2
-libs(latticeExtra)
-  xx <- 0:26
-  yy <- dnbinom(0:26, mu=3.17, size=1.47)*120 # estimates are from glm.nb
-  histogram(~borers, dd, type='count', subset=treat=='T2',
-            main="bliss.borers - trt T2 observed and expected",
-            breaks=0:27-.5) +
-              xyplot(yy~xx, col='navy', type='b')
-
    
-
-# "Poissonness"-type plot
-libs(vcd)
-
    #> Loading required package: grid
    #> 
-#> Attaching package: 'vcd'
    #> The following object is masked from 'package:latticeExtra':
-#> 
-#>     rootogram
      dat2 <- droplevels(subset(dat, treat=='T2'))
-  vcd::distplot(dat2$borers, type = "nbinomial",
-           main="bliss.borers neg binomialness plot")
-
      # Better way is a rootogram
-  g1 <- vcd::goodfit(dat2$borers, "nbinomial")
-  plot(g1, main="bliss.borers - Treatment 2")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bond.diallel-1.png b/docs/reference/bond.diallel-1.png deleted file mode 100644 index fe80605a..00000000 Binary files a/docs/reference/bond.diallel-1.png and /dev/null differ diff --git a/docs/reference/bond.diallel-2.png b/docs/reference/bond.diallel-2.png deleted file mode 100644 index 26014fd0..00000000 Binary files a/docs/reference/bond.diallel-2.png and /dev/null differ diff --git a/docs/reference/bond.diallel.html b/docs/reference/bond.diallel.html deleted file mode 100644 index 96e0194e..00000000 --- a/docs/reference/bond.diallel.html +++ /dev/null @@ -1,228 +0,0 @@ - -Diallel cross of winter beans — bond.diallel • agridat - - -
    -
    - - - -
    -
    -
    -

    Diallel cross of winter beans

    - - -
    - -
    -

    Diallel cross of winter beans

    -
    - - -
    -

    Format

    -

    A data frame with 36 observations on the following 3 variables.

    female
    -

    female parent

    - -
    male
    -

    male parent

    - -
    yield
    -

    yield, grams/plot

    - -
    stems
    -

    stems per plot

    - -
    nodes
    -

    podded nodes per stem

    - -
    pods
    -

    pods per podded node

    - -
    seeds
    -

    seeds per pod

    - -
    weight
    -

    weight (g) per 100 seeds

    - -
    height
    -

    height (cm) in April

    - -
    width
    -

    width (cm) in April

    - -
    flower
    -

    mean flowering date in May

    - - -
    -
    -

    Details

    -

    Yield in grams/plot for full diallel cross between 6 inbred lines - of winter beans. Values are means over two years.

    -
    -
    -

    Source

    -

    D. A. Bond (1966). Yield and components of yield in diallel crosses - between inbred lines of winter beans (Viciafaba). - The Journal of Agricultural Science, 67, 325--336. - https://doi.org/10.1017/S0021859600017329

    -
    -
    -

    References

    -

    Peter John, Statistical Design and Analysis of Experiments, p. 85.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(bond.diallel)
-  dat <- bond.diallel
-  
-  # Because these data are means, we will not be able to reproduce
-  # the anova table in Bond. More useful as a multivariate example.
-
-  libs(corrgram)
-#> 
-#> Attaching package: 'corrgram'
-#> The following object is masked from 'package:lattice':
-#> 
-#>     panel.fill
-  corrgram(dat[ , 3:11], main="bond.diallel",
-           lower=panel.pts)
-
-
-  # Multivariate example from sommer package
-  corrgram(dat[,c("stems","pods","seeds")],
-           lower=panel.pts, upper=panel.conf, main="bond.diallel")
-
-  
-  libs(sommer)           
-#> Loading required package: MASS
-#> 
-#> Attaching package: 'MASS'
-#> The following object is masked from 'package:asreml':
-#> 
-#>     oats
-#> The following object is masked from 'package:dplyr':
-#> 
-#>     select
-#> Loading required package: crayon
-#> 
-#> Attaching package: 'sommer'
-#> The following object is masked from 'package:asreml':
-#> 
-#>     vpredict
-  m1 <- mmer(cbind(stems,pods,seeds) ~ 1,
-             random= ~ vs(female)+vs(male),
-             rcov= ~ vs(units),
-             dat)
-#> Version out of date. Please update sommer to the newest version using:
-#> install.packages('sommer') in a new session
-#>  Use the 'date.warning' argument to disable the warning message.iteration    LogLik     wall    cpu(sec)   restrained
-#>     1      -63.6729   15:16:51      0           0
-#>     2      -40.8016   15:16:51      0           0
-#>     3      -27.2147   15:16:51      0           0
-#>     4      -24.2403   15:16:51      0           0
-#>     5      -24.1945   15:16:51      0           0
-#>     6      -24.194   15:16:51      0           0
-
-  #### genetic variance covariance
-  cov2cor(m1$sigma$`u:female`)
-#>            stems       pods      seeds
-#> stems  1.0000000  0.5745963 -0.8714338
-#> pods   0.5745963  1.0000000 -0.7946327
-#> seeds -0.8714338 -0.7946327  1.0000000
-  cov2cor(m1$sigma$`u:male`)
-#>            stems       pods      seeds
-#> stems  1.0000000  0.3173646 -0.6610472
-#> pods   0.3173646  1.0000000 -0.4276277
-#> seeds -0.6610472 -0.4276277  1.0000000
-  cov2cor(m1$sigma$`u:units`)
-#>            stems       pods     seeds
-#> stems  1.0000000 -0.3354943 0.2294149
-#> pods  -0.3354943  1.0000000 0.1294200
-#> seeds  0.2294149  0.1294200 1.0000000
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/bose.multi.uniformity-1.png b/docs/reference/bose.multi.uniformity-1.png deleted file mode 100644 index d27c0863..00000000 Binary files a/docs/reference/bose.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/bose.multi.uniformity.html b/docs/reference/bose.multi.uniformity.html deleted file mode 100644 index 196e6f38..00000000 --- a/docs/reference/bose.multi.uniformity.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - -Uniformity trials of barley, wheat, lentils — bose.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trials of barley, wheat, lentils

    - - -
    - -
    -

    Uniformity trials of barley, wheat, lentils in India 1930-1932.

    -
    - - 
    data("bose.multi.uniformity")
    - - -

    Format

    - -

    A data frame with 1170 observations on the following 5 variables.

    -
    year

    year

    -
    crop

    crop

    -
    row

    row ordinate

    -
    col

    column ordinate

    -
    yield

    plot yield, grams

    - -
    - -

    Details

    - -

    A field about 1/4 acre was sown in three consecutive years (beginning - in 1929-1930) with barley, wheat, and lentil.

    -

    At harvest, borders 3 feet on east and west and 6 feet on north and - south were removed. The field was divided into plots four feet square, - which were harvested separately, measured in grams.

    -

    Fertility contours of the field were somewhat similar across years, - with correlation values across years 0.45, 0.48, 0.21.

    -

    Field width: 15 plots * 4 feet = 60 feet.

    -

    Field length: 26 plots * 4 feet = 104 feet.

    -

    Conclusions:

    -

    "An experimental field which may be sensibly uniform for one crop or - for one season may not be so for another crop or in a different - season" p. 592.

    -

    Source

    - -

    Bose, R. D. (1935). - Some soil heterogeneity trials at Pusa and the size and shape of experimental plots. - Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). - https://archive.org/details/in.ernet.dli.2015.271739

    -

    References

    - -

    Shaw (1935). - Handbook of Statistics for Use in Plant-Breeding and Agricultural Problems, p. 149-170. - https://krishikosh.egranth.ac.in/handle/1/21153

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(bose.multi.uniformity)
-  dat <- bose.multi.uniformity
-
-  # match sum at bottom of Bose tables 1, 4, 5
-  # library(dplyr)
-  # dat 
-
-  libs(desplot, dplyr)
-  # Calculate percent of mean yield for each year
-  dat <- group_by(dat, year)
-  dat <- mutate(dat, pctyld = (yield-mean(yield))/mean(yield))
-
-  dat <- ungroup(dat)
-  dat <- mutate(dat, year=as.character(year))
-  # Bose smoothed the data by averaging 2x3 plots together before drawing
-  # contour maps.  Heatmaps of raw data have similar structure to Bose Fig 1.
-  desplot(dat, pctyld ~ col*row|year,
-          tick=TRUE, flip=TRUE, aspect=(26)/(15),
-          main="bose.multi.* - Percent of mean yield")
-
      
-  # contourplot() results need to be mentally flipped upside down
-  # contourplot(pctyld ~ col*row|year, dat,
-  #   region=TRUE, as.table=TRUE, aspect=26/15)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/box.cork-1.png b/docs/reference/box.cork-1.png deleted file mode 100644 index b3a36426..00000000 Binary files a/docs/reference/box.cork-1.png and /dev/null differ diff --git a/docs/reference/box.cork-2.png b/docs/reference/box.cork-2.png deleted file mode 100644 index 5e89d09c..00000000 Binary files a/docs/reference/box.cork-2.png and /dev/null differ diff --git a/docs/reference/box.cork.html b/docs/reference/box.cork.html deleted file mode 100644 index d1a91a2c..00000000 --- a/docs/reference/box.cork.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - - -Weight of cork samples on four sides of trees — box.cork • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Weight of cork samples on four sides of trees

    - - -
    - -
    -

    The cork data gives the weights of cork borings of the trunk for 28 trees - on the north (N), east (E), south (S) and west (W) directions.

    -
    - - - -

    Format

    - -

    Data frame with 28 observations on the following 5 variables.

    -
    tree

    tree number

    -
    dir

    direction N,E,S,W

    -
    y

    weight of cork deposit (centigrams), north direction

    - -
    - -

    Source

    - - - -

    C.R. Rao (1948). - Tests of significance in multivariate analysis. - Biometrika, 35, 58-79. - https://doi.org/10.2307/2332629

    -

    References

    - - - -

    K.V. Mardia, J.T. Kent and J.M. Bibby (1979) Multivariate Analysis, - Academic Press.

    -

    Russell D Wolfinger, (1996). - Heterogeneous Variance: Covariance Structures for Repeated Measures. - Journal of Agricultural, Biological, and Environmental - Statistics, 1, 205-230.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(box.cork)
-  dat <- box.cork
-
-  libs(reshape2, lattice)
-  dat2 <- acast(dat, tree ~ dir, value.var='y')
-  splom(dat2, pscales=3,
-        prepanel.limits = function(x) c(25,100),
-        main="box.cork", xlab="Cork yield on side of tree",
-        panel=function(x,y,...){
-          panel.splom(x,y,...)
-          panel.abline(0,1,col="gray80")
-        })
-
    
-
-  ## Radial star plot, each tree is one line
-  libs(plotrix)
-  libs(reshape2)
-  dat2 <- acast(dat, tree ~ dir, value.var='y')
-  radial.plot(dat2, start=pi/2, rp.type='p', clockwise=TRUE,
-              radial.lim=c(0,100), main="box.cork",
-              lwd=2, labels=c('North','East','South','West'),
-              line.col=rep(c("royalblue","red","#009900","dark orange",
-                             "#999999","#a6761d","deep pink"),
-                           length=nrow(dat2)))
-
      
-  # asreml 4
-  libs(asreml)
-
-  # Unstructured covariance
-  dat$dir <- factor(dat$dir)
-  dat$tree <- factor(dat$tree)  
-  dat <- dat[order(dat$tree, dat$dir), ]
-  
-  # Unstructured covariance matrix
-  m1 <- asreml(y~dir, data=dat, residual = ~ tree:us(dir))
-
    #> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:31:56 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -307.529           1.0    108 17:31:56    0.0
-#>  2      -300.714           1.0    108 17:31:56    0.0
-#>  3      -294.337           1.0    108 17:31:56    0.0
-#>  4      -291.522           1.0    108 17:31:56    0.0
-#>  5      -290.967           1.0    108 17:31:56    0.0
-#>  6      -290.959           1.0    108 17:31:56    0.0
        
-  libs(lucid)
-  vc(m1)
-
    #>            effect component std.error z.ratio bound %ch
-#>        tree:dir!R       1          NA      NA     F   0
-#>  tree:dir!dir_E:E     219.9     59.83     3.7     P   0
-#>  tree:dir!dir_N:E     223.8     64.93     3.4     P   0
-#>  tree:dir!dir_N:N     290.4     79        3.7     P   0
-#>  tree:dir!dir_S:E     229.1     69.21     3.3     P   0
-#>  tree:dir!dir_S:N     288.4     82.7      3.5     P   0
-#>  tree:dir!dir_S:S     350       95.22     3.7     P   0
-#>  tree:dir!dir_W:E     171.4     54.09     3.2     P   0
-#>  tree:dir!dir_W:N     226.3     65.75     3.4     P   0
-#>  tree:dir!dir_W:S     259.5     73.62     3.5     P   0
-#>  tree:dir!dir_W:W     226       61.48     3.7     P   0
        
-  # Note: 'rcor' is a personal function to extract the correlations
-  # into a matrix format
-  # round(kw::rcor(m1)$dir, 2)
-  #        E      N      S      W
-  # E 219.93 223.75 229.06 171.37
-  # N 223.75 290.41 288.44 226.27
-  # S 229.06 288.44 350.00 259.54
-  # W 171.37 226.27 259.54 226.00
-    
-  # Note: Wolfinger used a common diagonal variance
-  
-  # Factor Analytic with different specific variances
-  # fixme: does not work with asreml4
-  # m2 <- update(m1, residual = ~tree:facv(dir,1))
-  # round(kw::rcor(m2)$dir, 2)
-  #       E       N      S      W
-  # E 219.94 209.46 232.85 182.27
-  # N 209.46 290.41 291.82 228.43
-  # S 232.85 291.82 349.99 253.94
-  # W 182.27 228.43 253.94 225.99
-    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bradley.multi.uniformity-1.png b/docs/reference/bradley.multi.uniformity-1.png deleted file mode 100644 index 898e4945..00000000 Binary files a/docs/reference/bradley.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/bradley.multi.uniformity-2.png b/docs/reference/bradley.multi.uniformity-2.png deleted file mode 100644 index 11fdde2d..00000000 Binary files a/docs/reference/bradley.multi.uniformity-2.png and /dev/null differ diff --git a/docs/reference/bradley.multi.uniformity-3.png b/docs/reference/bradley.multi.uniformity-3.png deleted file mode 100644 index 37bda3d0..00000000 Binary files a/docs/reference/bradley.multi.uniformity-3.png and /dev/null differ diff --git a/docs/reference/bradley.multi.uniformity-4.png b/docs/reference/bradley.multi.uniformity-4.png deleted file mode 100644 index b1e3047c..00000000 Binary files a/docs/reference/bradley.multi.uniformity-4.png and /dev/null differ diff --git a/docs/reference/bradley.multi.uniformity-5.png b/docs/reference/bradley.multi.uniformity-5.png deleted file mode 100644 index 0a398d2d..00000000 Binary files a/docs/reference/bradley.multi.uniformity-5.png and /dev/null differ diff --git a/docs/reference/bradley.multi.uniformity.html b/docs/reference/bradley.multi.uniformity.html deleted file mode 100644 index c36fe381..00000000 --- a/docs/reference/bradley.multi.uniformity.html +++ /dev/null @@ -1,295 +0,0 @@ - - - - - - - - -Uniformity trial of 4 crops on the same land — bradley.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of 4 crops on the same land

    - - -
    - -
    -

    Uniformity trial of 4 crops on the same land in Trinidad.

    -
    - - 
    data("bradley.multi.uniformity")
    - - -

    Format

    - -

    A data frame with 440 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds per plot

    -
    season

    season

    -
    crop

    crop

    - -
    - -

    Details

    - - - -

    Experiments conducted in Trinidad.

    -

    Plots were marked in May 1939 in Fields 1, 2, and 3. Prior to 1939 it - was difficult to obtain significant results on this land.

    -

    Plots were 1/40 acre each, 33 feet square. Discard between blocks (the - rows) was 7 feet and between plots (the columns) was 4 feet. For - roadways, a gap of 14 feet is between blocks 10 and 11 and a gap of 10 - feet between plots E/F (which we call columns 5/6).

    -

    Data was collected for 4 crops. Two other crops had poor germination - and were omitted.

    -

    Field width: 10 plots * 33 feet + 8 gaps * 4 feet + 1 gap * 10 = 372 feet

    -

    Field length: 11 blocks (plots) * 33 feet + 9 gaps * 7 feet + 1 gap * 14 feet = 440 feet

    - -

    Crop 1. Woolly Pyrol. Crop cut at flowering and weighed in pounds. - Note, woolly pyrol appears to be a bean also called black gram, - phaseolus mungo.

    -

    Crop 2. Woolly Pyrol. Crop cut at flowering and weighed in pounds.

    -

    Crop 3. Maize. Net weight of cobs in pounds. Source document also has - number of cobs.

    -

    Crop 4. Yams. Weights in pounds. Source document has weight to 1/4 - pound, which has here been rounded to the nearest pound. (Half pounds - were rounded to nearest even pound.) Source document also has number - of yams.

    -

    Notes by Bradley.

    -

    The edges of the field tended to be slightly higher yielding. Thought - to be due to the heavier cultivation which the edges recieve (p. 18).

    -

    The plot in row 9, col 7 (9G in Bradley) is higher yielding than its - neighbors, thought to be the site of a saman tree dug up and burned - when the field was plotted. Bits of charcoal were still in the soil.

    -

    Bradley also examined soil samples on selected plots and looked at - nutrients, moisture, texture, etc. The selected plots were 4 - high-yielding plots and 4 low-yielding plots. Little difference was - observed. Unexpectedly, yams gave higher yield on plots with more - compaction.

    -

    Source

    - -

    P. L. Bradley (1941). - A study of the variation in productivity over a number of fixed plots in field 2. - Dissertation: The University of the West Indies. Appendix 1a, 1b, 1c, 1d. - https://hdl.handle.net/2139/41264

    -

    The data are repeated in: - C. E. Wilson. - Study of the plots laid out on field II with a view to obtaining - plot-fertility data for use in future experiments on these plots, - season 1940-41. - Dissertation: The University of the West Indies. Page 36-39. - https://uwispace.sta.uwi.edu/dspace/handle/2139/43658

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(bradley.multi.uniformity)
-dat <- bradley.multi.uniformity
-
-# figures similar to Bradley, pages 11-15
-libs(desplot)
-desplot(dat, yield ~ col*row, subset=season==1,
-        flip=TRUE, aspect=433/366, # true aspect (omits roadways)
-        main="bradley.multi.uniformity - season 1, woolly pyrol")
-
    
-desplot(dat, yield ~ col*row, subset=season==2,
-        flip=TRUE, aspect=433/366, # true aspect (omits roadways)
-        main="bradley.multi.uniformity - season 2, woolly pyrol")
-
    
-desplot(dat, yield ~ col*row, subset=season==3,
-        flip=TRUE, aspect=433/366, # true aspect (omits roadways)
-        main="bradley.multi.uniformity - season 3, maize")
-
    
-
-desplot(dat, yield ~ col*row, subset=season==4,
-        flip=TRUE, aspect=433/366, # true aspect (omits roadways)
-        main="bradley.multi.uniformity - season 4, yams")
-
    
-dat1 <- subset(bradley.multi.uniformity, season==1)
-dat2 <- subset(bradley.multi.uniformity, season==2)
-dat3 <- subset(bradley.multi.uniformity, season==3)
-dat4 <- subset(bradley.multi.uniformity, season==4)
-  # to combine plots across seasons, each yield value was converted to percent
-  # of maximum yield in that season. Same as Bradley, page 17.
-  dat1$percent <- dat1$yield / max(dat1$yield) * 100
-  dat2$percent <- dat2$yield / max(dat2$yield) * 100
-  dat3$percent <- dat3$yield / max(dat3$yield) * 100
-  dat4$percent <- dat4$yield / max(dat4$yield) * 100
-  # make sure data is in same order, then combine
-  dat1 <- dat1[order(dat1$col, dat1$row),]
-  dat2 <- dat2[order(dat2$col, dat2$row),]
-  dat3 <- dat3[order(dat3$col, dat3$row),]
-  dat4 <- dat4[order(dat4$col, dat4$row),]
-  dat14 <- dat1[,c('row','col')]
-  dat14$fertility <- dat1$percent + dat2$percent + dat3$percent + dat4$percent
-
-  libs(desplot)
-  desplot(dat14, fertility ~ col*row,
-          tick=TRUE, flip=TRUE, aspect=433/366, # true aspect (omits roadways)
-          main="bradley.multi.uniformity - fertility")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/brandle.rape-1.png b/docs/reference/brandle.rape-1.png deleted file mode 100644 index c31df2e1..00000000 Binary files a/docs/reference/brandle.rape-1.png and /dev/null differ diff --git a/docs/reference/brandle.rape.html b/docs/reference/brandle.rape.html deleted file mode 100644 index d780e1c4..00000000 --- a/docs/reference/brandle.rape.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Multi-environment trial of rape in Manitoba — brandle.rape • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of rape in Manitoba

    - - -
    - -
    - -

    Rape seed yields for 5 genotypes, 3 years, 9 locations.

    - -
    - - -

    Format

    - -

    A data frame with 135 observations on the following 4 variables.

    -
    gen

    genotype

    -
    year

    year, numeric

    -
    loc

    location, 9 levels

    -
    yield

    yield, kg/ha

    -
    - - -

    Details

    - -

    The yields are the mean of 4 reps.

    -

    Note, in table 2 of Brandle, the value of Triton in 1985 at Bagot is - shown as 2355, but should be 2555 to match the means reported - in the paper.

    - -

    Source

    - -

    Brandle, JE and McVetty, PBE. (1988). - Genotype x environment interaction and stability analysis of seed - yield of oilseed rape grown in Manitoba. - Canadian Journal of Plant Science, 68, 381--388.

    -

    Used with permission of P. McVetty.

    - - -

    Examples

    - 
    
-library(agridat)
-data(brandle.rape)
-dat <- brandle.rape
-
-libs(lattice)
-dotplot(gen~yield|loc, dat, group=year, auto.key=list(columns=3),
-        main="brandle.rape, yields per location", ylab="Genotype")
    
-# Matches table 4 of Brandle
-# round(tapply(dat$yield, dat$gen, mean),0)
-
-# Brandle reports variance components:
-# sigma^2_gl: 9369  gy: 14027 g: 72632 resid: 150000
-# Brandle analyzed rep-level data, so the residual variance is different.
-# The other components are matched by the following analysis.
-
-libs(lme4)
-libs(lucid)
-dat$year <- factor(dat$year)
-m1 <- lmer(yield ~ year + loc + year:loc + (1|gen) +
-             (1|gen:loc) + (1|gen:year), data=dat)
-vc(m1)
    #>       grp        var1 var2  vcov  sdcor
-#>   gen:loc (Intercept) <NA>  9361  96.75
-#>  gen:year (Intercept) <NA> 14030 118.4 
-#>       gen (Intercept) <NA> 72590 269.4 
-#>  Residual        <NA> <NA> 75010 273.9 
    ##      grp        var1 var2  vcov  sdcor
-##  gen:loc (Intercept)   9363  96.76
-## gen:year (Intercept)  14030 118.4
-##      gen (Intercept)  72630 269.5
-## Residual          75010 273.9
-  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/brandt.switchback-1.png b/docs/reference/brandt.switchback-1.png deleted file mode 100644 index 269ac9c3..00000000 Binary files a/docs/reference/brandt.switchback-1.png and /dev/null differ diff --git a/docs/reference/brandt.switchback.html b/docs/reference/brandt.switchback.html deleted file mode 100644 index 75131dfd..00000000 --- a/docs/reference/brandt.switchback.html +++ /dev/null @@ -1,256 +0,0 @@ - - - - - - - - -Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Switchback experiment on dairy cattle, milk yield for two treatments

    - - -
    - -
    -

    Switchback experiment on dairy cattle, milk yield for two treatments

    -
    - - 
    data("brandt.switchback")
    - - -

    Format

    - -

    A data frame with 30 observations on the following 5 variables.

    -
    group

    group: A,B

    -
    cow

    cow, 10 levels

    -
    trt

    treatment, 2 levels

    -
    period

    period, 3 levels

    -
    yield

    milk yield, pounds

    - -
    - -

    Details

    - - - -

    In this experiment, 10 cows were selected from the Iowa State College - Holstein-Friesian herd and divided into two equal groups. Care was - taken to have the groups as nearly equal as possible with regard to - milk production, stage of gestation, body weight, condition and - age. These cows were each given 10 pounds of timothy hay and 30 pounds - of corn silage daily but were fed different grain mixtures. Treatment - T1, then, consisted of feeding a grain mixture of 1 part of corn and - cob meal to 1 part of ground oats, while treatment T2 consisted of - feeding a grain mixture of 4 parts corn and cob meal, 4 parts of - ground oats and 3 parts of gluten feed. The three treatment periods - covered 105 days -- three periods of 35 days each. The yields for the - first 7 days of each period were not considered because of the - possible effect of the transition from one treatment to the other. The - data, together with sums and differences which aid in the calculations - incidental to testing, are given in table 2.

    -

    It seems safe to conclude that the inclusion of gluten feed in the - grain mixture fed in a timothy hay ration to Holstein-Friesian cows - increased the production of milk. The average increase was 21.7 pounds - per cow for a 28-day period.

    -

    Source

    - -

    A.E. Brandt (1938). Tests of Significance in Reversal or Switchback Trials - Iowa State College, Agricultural Research Bulletins. Bulletin 234. Book 22. - https://lib.dr.iastate.edu/ag_researchbulletins/22/

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-
-data(brandt.switchback)
-dat <- brandt.switchback
-
-# In each period, treatment 2 is slightly higher
-# bwplot(yield~trt|period,dat, layout=c(3,1), main="brandt.switchback",
-#     xlab="Treatment", ylab="Milk yield")
-
-# Yield at period 2 (trt T2) is above the trend in group A,
-# below the trend (trt T1) in group B.
-# Equivalently, treatment T2 is above the trend line
-libs(lattice)
-xyplot(yield~period|group, data=dat, group=cow, type=c('l','r'),
-    auto.key=list(columns=5), main="brandt.switchback",
-    xlab="Period.  Group A: T1,T2,T1.  Group B: T2,T1,T2",
-    ylab="Milk yield (observed and trend) per cow")
-
    
-# Similar to Brandt Table 10
-m1 <- aov(yield~period+group+cow:group+period:group, data=dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> period        2  34979   17489 10.0589  0.001483 ** 
-#> group         1   3874    3874  2.2280  0.154985    
-#> group:cow     8 950792  118849 68.3560 6.516e-11 ***
-#> period:group  2   4282    2141  1.2315  0.318096    
-#> Residuals    16  27819    1739                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bridges.cucumber-1.png b/docs/reference/bridges.cucumber-1.png deleted file mode 100644 index e6565e2c..00000000 Binary files a/docs/reference/bridges.cucumber-1.png and /dev/null differ diff --git a/docs/reference/bridges.cucumber-2.png b/docs/reference/bridges.cucumber-2.png deleted file mode 100644 index f66f313e..00000000 Binary files a/docs/reference/bridges.cucumber-2.png and /dev/null differ diff --git a/docs/reference/bridges.cucumber.html b/docs/reference/bridges.cucumber.html deleted file mode 100644 index 3b1c9a72..00000000 --- a/docs/reference/bridges.cucumber.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - - -Multi-environment trial of cucumbers in a latin square design — bridges.cucumber • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of cucumbers in a latin square design

    - - -
    - -
    -

    Cucumber yields in latin square design at two locs.

    -
    - - - -

    Format

    - -

    A data frame with 32 observations on the following 5 variables.

    -
    loc

    location

    -
    gen

    genotype/cultivar

    -
    row

    row

    -
    col

    column

    -
    yield

    weight of marketable fruit per plot

    - -
    - -

    Details

    - - - -

    Conducted at Clemson University in 1985. four cucumber cultivars - were grown in a latin square design at Clemson, SC, and Tifton, GA.

    -

    Separate variances are modeled each location.

    -

    Plot dimensions are not given.

    -

    Bridges (1989) used this data to illustrate fitting a heterogeneous - mixed model.

    -

    Used with permission of William Bridges.

    -

    Source

    - -

    William Bridges (1989). - Analysis of a plant breeding experiment with - heterogeneous variances using mixed model equations. - Applications of mixed models in agriculture and related - disciplines, S. Coop. Ser. Bull, 45--51.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(bridges.cucumber)
-  dat <- bridges.cucumber
-  dat <- transform(dat, rowf=factor(row), colf=factor(col))
-
-  libs(desplot)
-  desplot(dat, yield~col*row|loc,
-          # aspect unknown
-          text=gen, cex=1,
-          main="bridges.cucumber")
-
    
-  # Graphical inference test for heterogenous variances
-  libs(nullabor)
-  # Create a lineup of datasets
-  fun <- null_permute("loc")
-  dat20 <- lineup(fun, dat, n=20, pos=9)
-
-  # Now plot
-  libs(lattice)
-  bwplot(yield ~ loc|factor(.sample), dat20,
-         main="bridges.cucumber - graphical inference")
-
    
-  libs(asreml) # asreml4
-    
-  ## Random row/col/resid. Same as Bridges 1989, p. 147
-  m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
-               random = ~ rowf:loc + colf:loc, data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:53 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -69.1828       62.4832     24 17:07:53    0.0
-#>  2      -68.2565       48.7835     24 17:07:53    0.0
-#>  3      -67.6053       38.4916     24 17:07:53    0.0
-#>  4      -67.3668       32.3653     24 17:07:53    0.0
-#>  5      -67.3559       31.5313     24 17:07:53    0.0
-#>  6      -67.3558       31.4799     24 17:07:53    0.0
      
-  libs(lucid)
-  lucid::vc(m1)
-
    #>    effect component std.error z.ratio bound %ch
-#>  rowf:loc     31.62     23.02     1.4     P   0
-#>  colf:loc     18.08     15.33     1.2     P   0
-#>   units!R     31.48     12.85     2.4     P   0
      ##   effect component std.error z.ratio bound 
-  ## rowf:loc     31.62     23.02     1.4     P   0
-  ## colf:loc     18.08     15.32     1.2     P   0
-  ## units(R)     31.48     12.85     2.4     P   0
-  
-  ## Random row/col/resid at each loc. Matches p. 147
-  m2 <- asreml(yield ~ 1 + gen + loc + loc:gen,
-               random = ~ at(loc):rowf + at(loc):colf, data=dat,
-               resid = ~ dsum( ~ units|loc))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:54 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -68.5253           1.0     24 17:07:54    0.0
-#>  2      -66.8318           1.0     24 17:07:54    0.0
-#>  3      -66.4698           1.0     24 17:07:54    0.0
-#>  4      -66.3601           1.0     24 17:07:54    0.0
-#>  5      -66.3562           1.0     24 17:07:54    0.0
      lucid::vc(m2)
-
    #>                 effect component std.error z.ratio bound %ch
-#>  at(loc, Clemson):rowf     32.32    36.54     0.88     P 0.1
-#>   at(loc, Tifton):rowf     30.92    28.46     1.1      P 0.4
-#>  at(loc, Clemson):colf     22.55    28.78     0.78     P 0  
-#>   at(loc, Tifton):colf     13.62    14.59     0.93     P 0  
-#>          loc_Clemson!R     46.85    27.05     1.7      P 0  
-#>           loc_Tifton!R     16.11     9.298    1.7      P 0  
      ##                effect component std.error z.ratio bound 
-  ## at(loc, Clemson):rowf     32.32    36.58     0.88     P   0
-  ##  at(loc, Tifton):rowf     30.92    28.63     1.1      P   0
-  ## at(loc, Clemson):colf     22.55    28.78     0.78     P   0
-  ##  at(loc, Tifton):colf     13.62    14.59     0.93     P   0
-  ##        loc_Clemson(R)     46.85    27.05     1.7      P   0
-  ##         loc_Tifton(R)     16.11     9.299    1.7      P   0
-  
-  predict(m2, data=dat, classify='loc:gen')$pvals
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:54 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -66.3562           1.0     24 17:07:54    0.0
-#>  2      -66.3562           1.0     24 17:07:54    0.0
-#>  3      -66.3562           1.0     24 17:07:54    0.0
    #> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The ignored set: rowf,colf
-#> 
-#> 
-#>       loc      gen predicted.value std.error    status
-#> 1 Clemson   Dasher        45.55000  5.042806 Estimable
-#> 2 Clemson Guardian        31.62500  5.042806 Estimable
-#> 3 Clemson Poinsett        21.42500  5.042806 Estimable
-#> 4 Clemson   Sprint        25.95000  5.042806 Estimable
-#> 5  Tifton   Dasher        50.48460  3.893635 Estimable
-#> 6  Tifton Guardian        38.71697  3.893635 Estimable
-#> 7  Tifton Poinsett        33.01487  3.893635 Estimable
-#> 8  Tifton   Sprint        39.17952  3.893635 Estimable
      ##       loc      gen predicted.value std.error    status
-  ## 1 Clemson   Dasher            45.6      5.04 Estimable
-  ## 2 Clemson Guardian            31.6      5.04 Estimable
-  ## 3 Clemson Poinsett            21.4      5.04 Estimable
-  ## 4 Clemson   Sprint            26        5.04 Estimable
-  ## 5  Tifton   Dasher            50.5      3.89 Estimable
-  ## 6  Tifton Guardian            38.7      3.89 Estimable
-  ## 7  Tifton Poinsett            33        3.89 Estimable
-  ## 8  Tifton   Sprint            39.2      3.89 Estimable
-  
-  # Is a heterogeneous model justified? Maybe not.
-  # m1$loglik
-  ## -67.35585
-  # m2$loglik
-  ## -66.35621
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/broadbalk.wheat-1.png b/docs/reference/broadbalk.wheat-1.png deleted file mode 100644 index bc83e954..00000000 Binary files a/docs/reference/broadbalk.wheat-1.png and /dev/null differ diff --git a/docs/reference/broadbalk.wheat-2.png b/docs/reference/broadbalk.wheat-2.png deleted file mode 100644 index 187091a1..00000000 Binary files a/docs/reference/broadbalk.wheat-2.png and /dev/null differ diff --git a/docs/reference/broadbalk.wheat.html b/docs/reference/broadbalk.wheat.html deleted file mode 100644 index 1d577b0c..00000000 --- a/docs/reference/broadbalk.wheat.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - - -Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Long term wheat yields on Broadbalk fields at Rothamsted.

    - - -
    - -
    -

    Long term wheat yields on Broadbalk fields at Rothamsted.

    -
    - - - -

    Format

    - -

    A data frame with 1258 observations on the following 4 variables.

    -
    year

    year

    -
    plot

    plot

    -
    grain

    grain yield, tonnes

    -
    straw

    straw yield, tonnes

    - -
    - -

    Details

    - - - -

    Note: This data is only 1852-1925. You can find recent data for these - experiments at the Electronic Rothamsted Archive: - https://www.era.rothamsted.ac.uk/

    - -

    Rothamsted Experiment station conducted wheat experiments on the - Broadbalk Fields beginning in 1844 with data for yields of grain and - straw collected from 1852 to 1925. - Ronald Fisher was hired to analyze data from the agricultural trials. - Organic manures and inorganic fertilizer treatments were applied in - various combinations to the plots.

    -

    N1 is 48kg, N1.5 is 72kg, N2 is 96kg, N4 is 192kg nitrogen.

    -

    - - - - - - - - - - - - - - - - - - -
    PlotTreatment
    2bmanure
    3No fertilizer or manure
    5P K Na Mg (No N)
    6N1 P K Na Mg
    7N2 P K Na Mg
    8N3 P K Na Mg
    9N1* P K Na Mg since 1894; 9A and 9B received different treatments 1852-93
    10N2
    11N2 P
    12N2 P Na*
    13N2 P K
    14N2 P Mg*
    15N2 P K Na Mg (timing of N application different to other plots, see below)
    16N4 P K Na Mg 1852-64; unmanured 1865-83; N2*P K Na Mg since 1884
    17N2 applied in even years; P K Na Mg applied in odd years
    18N2 applied in odd years; P K Na Mg applied in even years
    19N1.5 P and rape cake 1852-78, 1879-1925 rape cake only
    - - -

    Source

    - -

    D.F. Andrews and A.M. Herzberg. 1985. - Data: A Collection of Problems from Many Fields for the Student - and Research Worker. Springer.

    -

    Retrieved from https://lib.stat.cmu.edu/datasets/Andrews/

    -

    References

    - -

    Broadbalk Winter Wheat Experiment. - https://www.era.rothamsted.ac.uk/index.php?area=home&page=index&dataset=4

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(broadbalk.wheat)
-dat <- broadbalk.wheat
-
-libs(lattice)
-## xyplot(grain~straw|plot, dat, type=c('p','smooth'), as.table=TRUE,
-##        main="broadbalk.wheat")
-xyplot(grain~year|plot, dat, type=c('p','smooth'), as.table=TRUE,
-       main="broadbalk.wheat") # yields are decreasing
-
    
-# See the treatment descriptions to understand the patterns
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(grain~year*plot, dat, main="broadbalk.wheat: Grain", col.regions=redblue)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/bryan.corn.uniformity-1.png b/docs/reference/bryan.corn.uniformity-1.png deleted file mode 100644 index 68d1027d..00000000 Binary files a/docs/reference/bryan.corn.uniformity-1.png and /dev/null differ diff --git a/docs/reference/bryan.corn.uniformity.html b/docs/reference/bryan.corn.uniformity.html deleted file mode 100644 index 6935b978..00000000 --- a/docs/reference/bryan.corn.uniformity.html +++ /dev/null @@ -1,235 +0,0 @@ - - - - - - - - -Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of corn at 3 locations in Iowa.

    - - -
    - -
    -

    Uniformity trial of corn at 3 locations in Iowa.

    -
    - - 
    data("bryan.corn.uniformity")
    - - -

    Format

    - -

    A data frame with 1728 observations on the following 4 variables.

    -
    expt

    experiment (variety/orientation)

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds per plot

    - - - -

    Details

    - -

    Three varieties of corn were planted. - Each experiment was 48 rows, each row 48 hills long, .65 acres. (A - "hill" is a single hole with possibly multiple seeds). - Spacing between the hills would be sqrt(43560 sq ft * .64) / 48 = 3.5 feet.

    -

    In the experiment code, K=Krug, I=Iodent, M=McCulloch (varieties of - corn), 23=1923, 25=1925, E=East/West, N=North/South.

    -

    Each experiment was aggregated into experimental units by combining 8 - hills, both in East/West direction and also in North/South direction. - Thus, each field is represented twice in the data.

    -

    Source

    - -

    Arthur Bryan (1933). - Factors Affecting Experimental Error in Field Plot Tests With Corn. - Agricultural Experiment Station, Iowa State College. Tables 22-27. - https://hdl.handle.net/2027/uiug.30112019568168

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  data(bryan.corn.uniformity)
-  dat <- bryan.corn.uniformity
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row|expt,
-          main="bryan.corn.uniformity",
-          aspect=(48*3.5/(6*8*3.5)), # true aspect
-          flip=TRUE, tick=TRUE)
    
-  # CVs in Table 5, column 8 hills
-  # libs(dplyr)
-  # dat 
-  #   summarize(cv=sd(yield)/mean(yield)*100)
-  ##   expt    cv
-  ## 1 K23E  10.9
-  ## 2 K23N  10.9
-  ## 3 I25E  16.3
-  ## 4 I25N  17.0
-  ## 5 M25E  16.2
-  ## 6 M25N  17.2
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/buntaran.wheat-1.png b/docs/reference/buntaran.wheat-1.png deleted file mode 100644 index 55d7b749..00000000 Binary files a/docs/reference/buntaran.wheat-1.png and /dev/null differ diff --git a/docs/reference/buntaran.wheat.html b/docs/reference/buntaran.wheat.html deleted file mode 100644 index 9a1ddaec..00000000 --- a/docs/reference/buntaran.wheat.html +++ /dev/null @@ -1,154 +0,0 @@ - -Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat • agridat - - -
    -
    - - - -
    -
    -
    -

    Multi-environment trial of wheat in Sweden in 2016.

    - - -
    - -
    -

    Multi-environment trial of wheat in Sweden in 2016.

    -
    - -
    - 
    data("buntaran.wheat")
    -
    - -
    -

    Format

    -

    A data frame with 1069 observations on the following 7 variables.

    zone
    -

    Geographic zone: south, middle, north

    - -
    loc
    -

    Location

    - -
    rep
    -

    Block replicate (up to 4)

    - -
    alpha
    -

    Incomplete-block in the alpha design

    - -
    gen
    -

    Genotype (cultivar)

    - -
    yield
    -

    Dry matter yield, kg/ha

    - - -
    -
    -

    Details

    -

    Dry matter yield from wheat trials in Sweden in 2016. The experiments - in each location were multi-rep with incomplete blocks in an alpha - design.

    -

    Electronic data are from the online supplement of Buntaran (2020) and also from the "init" package at https://github.com/Flavjack/inti.

    -
    -
    -

    Source

    -

    Buntaran, Harimurti et al. (2020). - Cross-validation of stagewise mixed-model analysis of Swedish variety trials with winter wheat and spring barley. - Crop Science, 60, 2221-2240. - http://doi.org/10.1002/csc2.20177

    -
    -
    -

    References

    -

    None.

    -
    - -
    -

    Examples

    - 
    data(buntaran.wheat)
-library(agridat)
-dat <- buntaran.wheat
-library(lattice)
-bwplot(yield~loc|zone, dat, layout=c(1,3),
-       scales=list(x=list(rot=90)),
-       main="buntaran.wheat")
-
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/burgueno.alpha-1.png b/docs/reference/burgueno.alpha-1.png deleted file mode 100644 index 3f22b678..00000000 Binary files a/docs/reference/burgueno.alpha-1.png and /dev/null differ diff --git a/docs/reference/burgueno.alpha-2.png b/docs/reference/burgueno.alpha-2.png deleted file mode 100644 index 68aeabaa..00000000 Binary files a/docs/reference/burgueno.alpha-2.png and /dev/null differ diff --git a/docs/reference/burgueno.alpha.html b/docs/reference/burgueno.alpha.html deleted file mode 100644 index 837b062a..00000000 --- a/docs/reference/burgueno.alpha.html +++ /dev/null @@ -1,473 +0,0 @@ - - - - - - - - -Incomplete block alpha design — burgueno.alpha • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Incomplete block alpha design

    - - -
    - -
    -

    Incomplete block alpha design

    -
    - - 
    data("burgueno.alpha")
    - - -

    Format

    - -

    A data frame with 48 observations on the following 6 variables.

    -
    rep

    rep, 3 levels

    -
    block

    block, 12 levels

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype, 16 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    A field experiment with 3 reps, 4 blocks per rep, laid out as an alpha - design.

    -

    The plot size is not given.

    -

    Electronic version of the data obtained from CropStat software.

    -

    Used with permission of Juan Burgueno.

    -

    Source

    - -

    J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis. 2000. - User's guide for spatial analysis of field variety trials using ASREML. - CIMMYT. - https://books.google.com/books?id=PR_tYCFyLCYC&pg=PA1

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(burgueno.alpha)
-  dat <- burgueno.alpha
-
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          out1=rep, out2=block, # aspect unknown
-          text=gen, cex=1,shorten="none",
-          main='burgueno.alpha')
-
    
-
-  libs(lme4,lucid)
-  # Inc block model
-  m0 <- lmer(yield ~ gen + (1|rep/block), data=dat)
-  vc(m0) # Matches Burgueno p. 26
-
    #>        grp        var1 var2   vcov sdcor
-#>  block:rep (Intercept) <NA>  86900 294.8
-#>        rep (Intercept) <NA> 200900 448.2
-#>   Residual        <NA> <NA> 133200 365  
      ##        grp        var1 var2   vcov sdcor
-  ##  block:rep (Intercept) <NA>  86900 294.8
-  ##        rep (Intercept) <NA> 200900 448.2
-  ##   Residual        <NA> <NA> 133200 365  
-
-
-  libs(asreml) # asreml4
-    
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf, dat$yf),]                 
-  
-  # Sequence of models on page 36
-  
-  m1 <- asreml(yield ~  gen, data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -232.128      424504.3     32 17:07:56    0.0
      m1$loglik # -232.13
-
    #> [1] -232.1277
      
-  m2 <- asreml(yield ~  gen, data=dat,
-               random = ~ rep)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -226.562      282408.0     32 17:07:56    0.0
-#>  2      -225.458      258453.6     32 17:07:56    0.0
-#>  3      -224.388      235077.8     32 17:07:56    0.0
-#>  4      -223.676      216846.8     32 17:07:56    0.0
-#>  5      -223.503      209444.3     32 17:07:56    0.0
-#>  6      -223.483      206934.7     32 17:07:56    0.0
-#>  7      -223.482      206516.9     32 17:07:56    0.0
      m2$loglik # -223.48
-
    #> [1] -223.4824
      
-  # Inc Block model
-  m3 <- asreml(yield ~  gen, data=dat,
-               random = ~ rep/block)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -225.610      246800.7     32 17:07:56    0.0
-#>  2      -223.872      199712.8     32 17:07:56    0.0
-#>  3      -222.453      162923.0     32 17:07:56    0.0
-#>  4      -221.644      140326.6     32 17:07:56    0.0
-#>  5      -221.443      135401.9     32 17:07:56    0.0
-#>  6      -221.417      133560.7     32 17:07:56    0.0
-#>  7      -221.417      133229.8     32 17:07:56    0.0
      m3$loglik # -221.42
-
    #> [1] -221.4167
      m3$coef$fixed # Matches solution on p. 27
-
    #>                     bu
-#> gen_G01        0.00000
-#> gen_G02     -284.28340
-#> gen_G03      493.85828
-#> gen_G04      699.29379
-#> gen_G05      863.39910
-#> gen_G06       30.97441
-#> gen_G07     -115.81504
-#> gen_G08      157.76411
-#> gen_G09      407.24195
-#> gen_G10      777.17723
-#> gen_G11     1038.46296
-#> gen_G12      648.02712
-#> gen_G13      248.18656
-#> gen_G14      405.45778
-#> gen_G15      942.48436
-#> gen_G16      453.78449
-#> (Intercept)  907.97831
-#> attr(,"terms")
-#>                   tname  n
-#> gen                 gen 16
-#> (Intercept) (Intercept)  1
      
-  # AR1xAR1 model
-  m4 <- asreml(yield ~ 1 + gen, data=dat,
-               resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -229.632      369295.0     32 17:07:56    0.0
-#>  2      -225.313      310549.6     32 17:07:56    0.0
-#>  3      -222.576      315026.1     32 17:07:56    0.0
-#>  4      -221.714      350768.1     32 17:07:56    0.0
-#>  5      -221.498      390126.2     32 17:07:56    0.0
-#>  6      -221.475      406435.9     32 17:07:56    0.0
-#>  7      -221.473      412526.0     32 17:07:56    0.0
-#>  8      -221.472      414688.0     32 17:07:56    0.0
      m4$loglik # -221.47
-
    #> [1] -221.4725
      plot(varioGram(m4), main="burgueno.alpha") # Figure 1
-
      
-  m5 <- asreml(yield ~ 1 + gen, data=dat,
-               random= ~ yf, resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -226.301      279679.7     32 17:07:56    0.0
-#>  2      -223.503      233517.8     32 17:07:56    0.0
-#>  3      -221.336      202245.4     32 17:07:56    0.0
-#>  4      -220.254      181270.7     32 17:07:56    0.0
-#>  5      -220.081      172053.0     32 17:07:56    0.0
-#>  6      -220.068      170878.3     32 17:07:56    0.0
-#>  7      -220.068      170961.0     32 17:07:56    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m5$loglik # -220.07
-
    #> [1] -220.0677
      
-  m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat,
-               resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -205.690      156958.0     30 17:07:56    0.0
-#>  2      -205.189      156511.0     30 17:07:56    0.0
-#>  3      -204.651      170349.2     30 17:07:56    0.0
-#>  4      -204.640      171539.3     30 17:07:56    0.0
-#>  5      -204.639      171955.1     30 17:07:56    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m6$loglik # -204.64
-
    #> [1] -204.6394
      
-  m7 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
-               random= ~ spl(yf), resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:56 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -213.764      161236.1     31 17:07:57    0.0
-#>  2      -213.161      158001.3     31 17:07:57    0.0
-#>  3      -212.686      158486.3     31 17:07:57    0.0
-#>  4      -212.518      161586.3     31 17:07:57    0.0
-#>  5      -212.512      160691.0     31 17:07:57    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m7$loglik # -212.51
-
    #> [1] -212.5125
      
-  m8 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
-               random= ~ spl(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -214.401      162571.5     31 17:07:57    0.0
-#>  2      -214.190      158323.8     31 17:07:57    0.0
-#>  3      -214.000      153413.7     31 17:07:57    0.0
-#>  4      -213.916      149071.2     31 17:07:57    0.0
-#>  5      -213.914      148249.7     31 17:07:57    0.0
      m8$loglik # -213.91
-
    #> [1] -213.9141
      
-  # Polynomial model with predictions
-  m9 <- asreml(yield ~ 1 + gen + pol(yf,-2) + pol(xf,-2), data=dat,
-               random= ~ spl(yf),
-               resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -192.357      124656.6     28 17:07:57    0.0 (1 restrained)
-#>  2      -191.606      114513.8     28 17:07:57    0.0
-#>  3      -191.493      114311.7     28 17:07:57    0.0
-#>  4      -191.452      115391.7     28 17:07:57    0.0
-#>  5      -191.446      114966.4     28 17:07:57    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m9 <- update(m9)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -191.443      115328.5     28 17:07:57    0.0
-#>  2      -191.443      115251.7     28 17:07:57    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m9$loglik # -191.44 vs -189.61
-
    #> [1] -191.4426
      p9 <- predict(m9, classify="gen:xf:yf", levels=list(xf=1,yf=1)) 
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -191.442      115160.3     28 17:07:57    0.0
-#>  2      -191.442      115178.5     28 17:07:57    0.0
-#>  3      -191.442      115210.1     28 17:07:57    0.0
      p9
-
    #> $pvals
-#> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> 
-#> 
-#>    gen     xf     yf predicted.value std.error    status
-#> 1  G01 order1 order1        1967.154  265.0976 Estimable
-#> 2  G02 order1 order1        2018.990  282.6813 Estimable
-#> 3  G03 order1 order1        2374.678  233.9746 Estimable
-#> 4  G04 order1 order1        2539.440  238.5453 Estimable
-#> 5  G05 order1 order1        2817.841  261.9213 Estimable
-#> 6  G06 order1 order1        2134.310  260.1078 Estimable
-#> 7  G07 order1 order1        2028.879  287.5826 Estimable
-#> 8  G08 order1 order1        2205.963  271.0232 Estimable
-#> 9  G09 order1 order1        2346.313  251.1598 Estimable
-#> 10 G10 order1 order1        2831.710  233.6259 Estimable
-#> 11 G11 order1 order1        2868.783  238.5870 Estimable
-#> 12 G12 order1 order1        2685.266  246.1334 Estimable
-#> 13 G13 order1 order1        2202.347  277.7411 Estimable
-#> 14 G14 order1 order1        2633.541  285.9366 Estimable
-#> 15 G15 order1 order1        2798.175  270.0601 Estimable
-#> 16 G16 order1 order1        2365.906  238.3437 Estimable
-#> 
-#> $avsed
-#>  overall 
-#> 275.0335 
-#> 
      
-  m10 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
-                resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -218.438      319937.6     30 17:07:57    0.0
-#>  2      -214.809      284438.5     30 17:07:57    0.0
-#>  3      -212.478      301889.6     30 17:07:57    0.0
-#>  4      -211.749      343343.7     30 17:07:57    0.0
-#>  5      -211.575      384479.3     30 17:07:57    0.0
-#>  6      -211.559      399834.4     30 17:07:57    0.0
-#>  7      -211.557      405022.4     30 17:07:57    0.0
      m10$loglik # -211.56
-
    #> [1] -211.5574
      
-  m11 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
-                random= ~ spl(yf),
-                resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -210.199      166458.9     30 17:07:57    0.0
-#>  2      -209.562      164032.3     30 17:07:57    0.0
-#>  3      -209.064      166788.1     30 17:07:57    0.0
-#>  4      -208.901      173307.2     30 17:07:57    0.0
-#>  5      -208.898      172903.8     30 17:07:57    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m11$loglik # -208.90
-
    #> [1] -208.8981
      
-  m12 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
-                random= ~ spl(yf)+spl(xf),
-                resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#> Spline: design points closer than 0.0007 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -208.573      135590.1     30 17:07:57    0.0 (1 restrained)
-#>  2      -207.575      121857.9     30 17:07:57    0.0
-#>  3      -207.068      114896.2     30 17:07:57    0.0
-#>  4      -206.844      112501.6     30 17:07:57    0.0
-#>  5      -206.826      111035.7     30 17:07:57    0.0
-#>  6      -206.825      111519.0     30 17:07:57    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m12$loglik # -206.82
-
    #> [1] -206.8252
      
-  m13 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
-                random= ~ spl(yf)+spl(xf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:57 2021
-#> Spline: design points closer than 0.0005 have been merged.
-#> Spline: design points closer than 0.0007 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -208.583      129506.5     30 17:07:57    0.0
-#>  2      -208.160      124731.6     30 17:07:57    0.0
-#>  3      -207.765      118920.6     30 17:07:57    0.0
-#>  4      -207.541      113114.9     30 17:07:57    0.0
-#>  5      -207.517      111017.9     30 17:07:57    0.0
-#>  6      -207.516      110690.9     30 17:07:57    0.0
      m13$loglik # -207.52
-
    #> [1] -207.5164
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/burgueno.rowcol-1.png b/docs/reference/burgueno.rowcol-1.png deleted file mode 100644 index a7bb6105..00000000 Binary files a/docs/reference/burgueno.rowcol-1.png and /dev/null differ diff --git a/docs/reference/burgueno.rowcol-2.png b/docs/reference/burgueno.rowcol-2.png deleted file mode 100644 index 382456e7..00000000 Binary files a/docs/reference/burgueno.rowcol-2.png and /dev/null differ diff --git a/docs/reference/burgueno.rowcol.html b/docs/reference/burgueno.rowcol.html deleted file mode 100644 index 4ced0837..00000000 --- a/docs/reference/burgueno.rowcol.html +++ /dev/null @@ -1,274 +0,0 @@ - - - - - - - - -Row-column design — burgueno.rowcol • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Row-column design

    - - -
    - -
    -

    Row-column design

    -
    - - 
    data("burgueno.rowcol")
    - - -

    Format

    - -

    A data frame with 128 observations on the following 5 variables.

    -
    rep

    rep, 2 levels

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype, 64 levels

    -
    yield

    yield, tons/ha

    - -
    - -

    Details

    - -

    A field experiment with two contiguous replicates in 8 rows, 16 - columns.

    -

    The plot size is not given.

    -

    Electronic version of the data obtained from CropStat software.

    -

    Used with permission of Juan Burgueno.

    -

    Source

    - -

    J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis (2000). - User's guide for spatial analysis of field variety trials using ASREML. - CIMMYT.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(burgueno.rowcol)
-  dat <- burgueno.rowcol
-
-  # Two contiguous reps in 8 rows, 16 columns
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          out1=rep, # aspect unknown
-          text=gen, shorten="none", cex=.75,
-          main="burgueno.rowcol")
-
    
-  libs(lme4,lucid)
-  
-  # Random rep, row and col within rep
-  # m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat)
-  # vc(m1) # Match components of Burgueno p. 40
-  ##      grp        var1 var2   vcov  sdcor
-  ##  rep:col (Intercept) <NA> 0.2189 0.4679
-  ##  rep:row (Intercept) <NA> 0.1646 0.4057
-  ##      rep (Intercept) <NA> 0.1916 0.4378
-  ## Residual        <NA> <NA> 0.1796 0.4238
-  
-  libs(asreml) # asreml4
-
-  # AR1 x AR1 with linear row/col effects, random spline row/col
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf,dat$yf),]
-  m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat,
-               random = ~ spl(yf) + spl(xf),
-               resid = ~ ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:59 2021
-#> Spline: design points closer than 0.0007 have been merged.
-#> Spline: design points closer than 0.0015 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -11.7667      0.189040     62 17:07:59    0.0
-#>  2      -10.9242      0.179307     62 17:07:59    0.0
-#>  3      -10.0462      0.167806     62 17:07:59    0.0
-#>  4       -9.4052      0.156150     62 17:07:59    0.0
-#>  5       -9.2562      0.150710     62 17:07:59    0.0
-#>  6       -9.2402      0.148710     62 17:07:59    0.0
-#>  7       -9.2394      0.148344     62 17:07:59    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m2 <- update(m2) # More iterations
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:07:59 2021
-#> Spline: design points closer than 0.0007 have been merged.
-#> Spline: design points closer than 0.0015 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -9.23934      0.148220     62 17:07:59    0.0
-#>  2      -9.23934      0.148225     62 17:07:59    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      
-  # Scaling of spl components has changed in asreml from old versions
-  libs(lucid)
-  vc(m2) # Match Burgueno p. 42
-
    #>        effect component std.error z.ratio bound %ch
-#>       spl(yf)   0.09077   0.08252   1.1       P 0  
-#>       spl(xf)   0.08105   0.08198   0.99      P 0.1
-#>       xf:yf!R   0.1482    0.03119   4.8       P 0  
-#>  xf:yf!xf!cor   0.1153    0.2269    0.51      U 0.2
-#>  xf:yf!yf!cor   0.00953   0.2414    0.039     U 1.9
      ##       effect component std.error z.ratio bound 
-  ##      spl(yf)  0.09077    0.08252   1.1       P 0
-  ##      spl(xf)  0.08107    0.08209   0.99      P 0
-  ##     xf:yf(R)  0.1482     0.03119   4.8       P 0
-  ## xf:yf!xf!cor  0.1152     0.2269    0.51      U 0.1
-  ## xf:yf!yf!cor  0.009467   0.2414    0.039     U 0.9
-  
-  plot(varioGram(m2), main="burgueno.rowcol")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/burgueno.unreplicated-1.png b/docs/reference/burgueno.unreplicated-1.png deleted file mode 100644 index 9d23541f..00000000 Binary files a/docs/reference/burgueno.unreplicated-1.png and /dev/null differ diff --git a/docs/reference/burgueno.unreplicated-2.png b/docs/reference/burgueno.unreplicated-2.png deleted file mode 100644 index 0618888b..00000000 Binary files a/docs/reference/burgueno.unreplicated-2.png and /dev/null differ diff --git a/docs/reference/burgueno.unreplicated-3.png b/docs/reference/burgueno.unreplicated-3.png deleted file mode 100644 index 0a2a0864..00000000 Binary files a/docs/reference/burgueno.unreplicated-3.png and /dev/null differ diff --git a/docs/reference/burgueno.unreplicated.html b/docs/reference/burgueno.unreplicated.html deleted file mode 100644 index f6a9e2d5..00000000 --- a/docs/reference/burgueno.unreplicated.html +++ /dev/null @@ -1,274 +0,0 @@ - - - - - - - - -Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Field experiment with unreplicated genotypes plus one repeated check.

    - - -
    - -
    -

    Field experiment with unreplicated genotypes plus one repeated check.

    -
    - - 
    data("burgueno.unreplicated")
    - - -

    Format

    - -

    A data frame with 434 observations on the following 4 variables.

    -
    gen

    genotype, 281 levels

    -
    col

    column

    -
    row

    row

    -
    yield

    yield, tons/ha

    - -
    - -

    Details

    - -

    A field experiment with 280 new genotypes. A check genotype is - planted in every 4th column.

    -

    The plot size is not given.

    -

    Electronic version of the data obtained from CropStat software.

    -

    Used with permission of Juan Burgueno.

    -

    Source

    - -

    J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis (2000). - User's guide for spatial analysis of field variety trials using ASREML. - CIMMYT.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(burgueno.unreplicated)
-  dat <- burgueno.unreplicated
-
-  # Define a 'check' variable for colors
-  dat$check <- ifelse(dat$gen=="G000", 2, 1)
-  # Every fourth column is the 'check' genotype
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          col=check, num=gen, #text=gen, cex=.3, # aspect unknown
-          main="burgueno.unreplicated")
-
    
-  libs(asreml,lucid) # asreml4
-
-  # AR1 x AR1 with random genotypes
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf,dat$yf),]
-  m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
-               resid = ~ ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:01 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -237.568       1.03480    433 17:08:01    0.0
-#>  2      -229.545       0.89061    433 17:08:01    0.0
-#>  3      -217.471       0.65204    433 17:08:01    0.0
-#>  4      -212.727       0.53716    433 17:08:01    0.0
-#>  5      -211.786       0.50071    433 17:08:01    0.0
-#>  6      -211.729       0.49978    433 17:08:01    0.0
-#>  7      -211.725       0.49941    433 17:08:01    0.0
-#>  8      -211.724       0.49931    433 17:08:01    0.0
      vc(m2)
-
    #>        effect component std.error z.ratio bound %ch
-#>           gen    0.9122   0.127       7.2     P 0  
-#>       xf:yf!R    0.4993   0.05601     8.9     P 0  
-#>  xf:yf!xf!cor   -0.243    0.09154    -2.7     U 0.1
-#>  xf:yf!yf!cor    0.1254   0.07057     1.8     U 0.3
      ##       effect component std.error z.ratio bound 
-  ##          gen    0.9122   0.127       7.2     P 0  
-  ##     xf:yf(R)    0.4993   0.05601     8.9     P 0  
-  ## xf:yf!xf!cor   -0.2431   0.09156    -2.7     U 0  
-  ## xf:yf!yf!cor    0.1255   0.07057     1.8     U 0.1
-  
-  # Note the strong saw-tooth pattern in the variogram.  Seems to
-  # be column effects.
-  plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
-       main="burgueno.unreplicated - AR1xAR1")
-
      # libs(lattice) # Show how odd columns are high
-  # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)
-  
-  # Define an even/odd column factor as fixed effect
-  # dat$oddcol <- factor(dat$col 
-  # The modulus operator throws a bug, so do it the hard way.
-  dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )
-  
-  m3 <- update(m2, yield ~ 1 + oddcol)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:01 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -202.465      0.474601    432 17:08:01    0.0
-#>  2      -200.994      0.451132    432 17:08:01    0.0
-#>  3      -199.872      0.429339    432 17:08:01    0.0 (1 restrained)
-#>  4      -199.582      0.423518    432 17:08:01    0.0
-#>  5      -199.519      0.420714    432 17:08:01    0.0
-#>  6      -199.518      0.421386    432 17:08:01    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m3$loglik # Matches Burgueno table 3, line 3
-
    #> [1] -199.5175
      
-  plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
-       main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
-
      # Much better-looking variogram
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/butron.maize-1.png b/docs/reference/butron.maize-1.png deleted file mode 100644 index 3d6e65e9..00000000 Binary files a/docs/reference/butron.maize-1.png and /dev/null differ diff --git a/docs/reference/butron.maize.html b/docs/reference/butron.maize.html deleted file mode 100644 index 93528457..00000000 --- a/docs/reference/butron.maize.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - -Multi-environment trial of maize with pedigrees — butron.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize with pedigrees

    - - -
    - -
    -

    Maize yields in a multi-environment trial. Pedigree included.

    -
    - - - -

    Format

    - -

    A data frame with 245 observations on the following 5 variables.

    -
    gen

    genotype

    -
    male

    male parent

    -
    female

    female parent

    -
    env

    environment

    -
    yield

    yield, Mg/ha

    - -
    - -

    Details

    - -

    Ten inbreds were crossed to produce a diallel without reciprocals. - The 45 F1 crosses were evaluated along with 4 checks in a - triple-lattice 7x7 design. Pink stem borer infestation was natural.

    -

    Experiments were performed in 1995 and 1996 at three sites in - northwestern Spain: - Pontevedra (42 deg 24 min N, 8 deg 38 min W, 20 m over sea), - Pontecaldelas (42 deg 23 N, 8 min 32 W, 300 m above sea), - Ribadumia (42 deg 30 N, 8 min 46 W, 50 m above sea).

    -

    A two-letter location code and the year are concatenated to define the - environment.

    -

    The average number of larvae per plant in each environment:

    - - - - - - -
    EnvLarvae
    pc950.54
    pc960.91
    ri961.78
    pv952.62
    pv963.35
    - - -

    Used with permission of Ana Butron.

    -

    Source

    - -

    Butron, A and Velasco, P and Ord\'as, A and Malvar, RA (2004). - Yield evaluation of maize cultivars across environments with different - levels of pink stem borer infestation. - Crop Science, 44, 741-747. - https://doi.org/10.2135/cropsci2004.7410

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(butron.maize)
-  dat <- butron.maize
-
-  libs(reshape2)
-  mat <- acast(dat, gen~env, value.var='yield')
-  mat <- sweep(mat, 2, colMeans(mat))
-  mat.svd <- svd(mat)
-  # Calculate PC1 and PC2 scores as in Table 4 of Butron
-  # Comment out to keep Rcmd check from choking on '
-  # round(mat.svd$u[,1:2] 
-
-  biplot(princomp(mat), main="butron.maize", cex=.7) # Figure 1 of Butron
-
    
-
-  if(0){
-    # Fixme: This section is broken because synbreed has been removed from CRAN
-    # Here we see if including pedigree information is helpful for a
-    # multi-environment model
-    # Including the pedigree provided little benefit
-    
-    # Create the pedigree
-    ped <- dat[, c('gen','male','female')]
-    ped <- ped[!duplicated(ped),] # remove duplicates
-    unip <- unique(c(ped$male, ped$female)) # Unique parents
-    unip <- unip[!is.na(unip)]
-    # We have to define parents at the TOP of the pedigree
-    ped <- rbind(data.frame(gen=c("Dent","Flint"), # genetic groups
-                            male=c(0,0),
-                            female=c(0,0)),
-                 data.frame(gen=c("A509","A637","A661","CM105","EP28",
-                                  "EP31","EP42","F7","PB60","Z77016"),
-                            male=rep(c('Dent','Flint'),each=5),
-                            female=rep(c('Dent','Flint'),each=5)),
-                 ped)
-    ped[is.na(ped$male),'male'] <- 0
-    ped[is.na(ped$female),'female'] <- 0
-    
-  }
-
-
-  # asreml 4
-  if(0){
-    libs(asreml)
-    ped.ainv <- ainverse(ped)
-      
-    m0 <- asreml(yield ~ 1+env, data=dat, random = ~ gen)
-    m1 <- asreml(yield ~ 1+env, random = ~ vm(gen, ped.ainv), data=dat)
-    m2 <- update(m1, random = ~ idv(env):vm(gen, ped.ainv))
-    m3 <- update(m2, random = ~ diag(env):vm(gen, ped.ainv))
-    m4 <- update(m3, random = ~ fa(env,1):vm(gen, ped.ainv))
-    #summary(m0)$aic
-    #summary(m4)$aic
-    ##    df      AIC
-    ## m0  2 229.4037
-    ## m1  2 213.2487
-    ## m2  2 290.6156
-    ## m3  6 296.8061
-    ## m4 11 218.1568
-    
-    p0 <- predict(m0, data=dat, classify="gen")$pvals
-    p1 <- predict(m1, data=dat, classify="gen")$pvals
-    p1par <- p1[1:12,]   # parents
-    p1 <- p1[-c(1:12),]  # remove parents
-    # Careful!  Need to manually sort the predictions
-    p0 <- p0[order(as.character(p0$gen)),]
-    p1 <- p1[order(as.character(p1$gen)),]
-    
-    # lims <- range(c(p0$pred, p1$pred)) * c(.95,1.05)
-    lims <- c(6,8.25) # zoom in on the higher-yielding hybrids
-    plot(p0$predicted.value, p1$predicted.value,
-         pch="", xlim=lims, ylim=lims, main="butron.maize",
-         xlab="BLUP w/o pedigree", ylab="BLUP with pedigree")
-    abline(0,1,col="lightgray")
-    text(x=p0$predicted.value, y=p1$predicted.value,
-         p0$gen, cex=.5, srt=-45)
-    text(x=min(lims), y=p1par$predicted.value, p1par$gen, cex=.5, col="red")
-    round( cor(p0$predicted.value, p1$predicted.value), 3)
-    # Including the pedigree provided very little change
-    
-  }
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/byers.apple-1.png b/docs/reference/byers.apple-1.png deleted file mode 100644 index 4631be58..00000000 Binary files a/docs/reference/byers.apple-1.png and /dev/null differ diff --git a/docs/reference/byers.apple.html b/docs/reference/byers.apple.html deleted file mode 100644 index e3de4ded..00000000 --- a/docs/reference/byers.apple.html +++ /dev/null @@ -1,237 +0,0 @@ - - - - - - - - -Diameters of apples — byers.apple • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Diameters of apples

    - - -
    - -
    -

    Measurements of the diameters of apples

    -
    - - - -

    Format

    - -

    A data frame with 480 observations on the following 6 variables.

    -
    tree

    tree, 10 levels

    -
    apple

    apple, 24 levels

    -
    size

    size of apple

    -
    appleid

    unique id number for each apple

    -
    time

    time period, 1-6 = (week/2)

    -
    diameter

    diameter, inches

    - -
    - -

    Details

    - -

    Experiment conducted at the Winchester Agricultural Experiment Station - of Virginia Polytechnic Institute and State University. Twentyfive - apples were chosen from each of ten apple trees.

    -

    Of these, there were 80 apples in the largest size class, 2.75 inches - in diameter or greater.

    -

    The diameters of the apples were recorded every two weeks over a - 12-week period.

    -

    Source

    - -

    Schabenberger, Oliver and Francis J. Pierce. 2002. - Contemporary Statistical Models for the Plant and Soil - Sciences. CRC Press, Boca Raton, FL.

    - -

    Examples

    - 
      
-library(agridat)
-
-data(byers.apple)
-dat <- byers.apple
-
-libs(lattice)
-xyplot(diameter ~ time | factor(appleid), data=dat, type=c('p','l'),
-       strip=strip.custom(par.strip.text=list(cex=.7)),
-       main="byers.apple")
-
    
-# \dontrun{
-  # Overall fixed linear trend, plus random intercept/slope deviations
-  # for each apple.  Observations within each apple are correlated.
-  libs(nlme)
-  libs(lucid)
-  m1 <- lme(diameter ~ 1 + time, data=dat,
-            random = ~ time|appleid, method='ML',
-            cor = corAR1(0, form=~ time|appleid),
-            na.action=na.omit)
-  vc(m1)
-
    #>       effect   variance   stddev corr
-#>  (Intercept) 0.007354   0.08575    NA
-#>         time 0.00003632 0.006027 0.83
-#>     Residual 0.0004555  0.02134    NA
      ##       effect   variance   stddev corr
-  ##  (Intercept) 0.007354   0.08575    NA
-  ##         time 0.00003632 0.006027 0.83
-  ##     Residual 0.0004555  0.02134    NA
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/caribbean.maize-1.png b/docs/reference/caribbean.maize-1.png deleted file mode 100644 index 92599ec2..00000000 Binary files a/docs/reference/caribbean.maize-1.png and /dev/null differ diff --git a/docs/reference/caribbean.maize-2.png b/docs/reference/caribbean.maize-2.png deleted file mode 100644 index ef579ec5..00000000 Binary files a/docs/reference/caribbean.maize-2.png and /dev/null differ diff --git a/docs/reference/caribbean.maize-3.png b/docs/reference/caribbean.maize-3.png deleted file mode 100644 index ae2c83ce..00000000 Binary files a/docs/reference/caribbean.maize-3.png and /dev/null differ diff --git a/docs/reference/caribbean.maize.html b/docs/reference/caribbean.maize.html deleted file mode 100644 index f48f571a..00000000 --- a/docs/reference/caribbean.maize.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - -Multi-environment trial of maize with fertilization — caribbean.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize with fertilization

    - - -
    - -
    -

    Maize fertilization trial on Antigua and St. Vincent.

    -
    - - - -

    Format

    - -

    A data frame with 612 observations on the following 7 variables.

    -
    isle

    island, 2 levels

    -
    site

    site

    -
    block

    block

    -
    plot

    plot, numeric

    -
    trt

    treatment factor

    -
    ears

    number of ears harvested

    -
    yield

    yield in kilograms

    - -
    - -

    Details

    - -

    Antigua is a coral island in the Caribbean with sufficient level land - for experiments and a semi-arid climate, while St. Vincent is - volcanic and level areas are uncommon, but the rainfall can be seasonally heavy.

    -

    Plots were 16 feet by 18 feet. A central area 12 feet by 12 feet was - harvested and recorded.

    -

    The number of ears harvested was only recorded on the isle of Antigua.

    -

    The digits of the treatment represent the levels of nitrogen, - phosphorus, and potassium fertilizer, respectively.

    -

    The TEAN site suffered damage from goats on plot 27, 35 and 36. - The LFAN site suffered damage from cattle on one boundary--plots 9, 18, 27, 36.

    -

    Source

    - -

    D.F. Andrews and A.M. Herzberg. 1985. - Data: A Collection of Problems from Many Fields for the Student - and Research Worker.

    -

    Retrieved from https://lib.stat.cmu.edu/datasets/Andrews/

    -

    References

    - -

    Also in the DAAG package as data sets antigua, stVincent.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(caribbean.maize)
-dat <- caribbean.maize
-
-# Yield and ears are correlated
-libs(lattice)
-xyplot(yield~ears|site, dat, ylim=c(0,10), subset=isle=="Antigua",
-       main="caribbean.maize - Antiqua")
-
    
-# Some locs show large response to nitrogen (as expected), e.g. UISV, OOSV
-dotplot(trt~yield|site, data=dat, main="caribbean.maize treatment response")
-
    
-# The pattern is a bit hard to see, so we split the treatment factor
-# into separate factors, and group sites by island
-dat <- transform(dat, N=factor(substring(trt,2,2)),
-                      P=factor(substring(trt,3,3)),
-                      K=factor(substring(trt,4,4)))
-dat <- transform(dat, env=paste(substring(isle,1,1),site,sep="-"))
-# Now we can see the strong N*site interaction
-bwplot(yield~N|env, dat,
-       main="caribbean.maize", xlab="nitrogen")
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/carlson.germination-1.png b/docs/reference/carlson.germination-1.png deleted file mode 100644 index 611d8111..00000000 Binary files a/docs/reference/carlson.germination-1.png and /dev/null differ diff --git a/docs/reference/carlson.germination.html b/docs/reference/carlson.germination.html deleted file mode 100644 index 05178c92..00000000 --- a/docs/reference/carlson.germination.html +++ /dev/null @@ -1,240 +0,0 @@ - - - - - - - - -Germination of alfalfa seeds at various salt concentrations — carlson.germination • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Germination of alfalfa seeds at various salt concentrations

    - - -
    - -
    -

    Germination of alfalfa seeds at various salt concentrations

    -
    - - 
    data("carlson.germination")
    - - -

    Format

    - -

    A data frame with 120 observations on the following 3 variables.

    -
    gen

    genotype factor, 15 levels

    -
    germ

    germination percent, 0-100

    -
    nacl

    salt concentration percent, 0-2

    - -
    - -

    Details

    - -

    Data are means averaged over 5, 10, 15, and 20 day counts. - Germination is expressed as a percent of the no-salt control to - account for differences in germination among the cultivars.

    -

    Source

    - -

    Carlson, JR and Ditterline, RL and Martin, JM and Sands, DC and Lund, - RE. (1983). - Alfalfa Seed Germination in Antibiotic Agar Containing NaCl. - Crop science, 23, 882-885. - https://doi.org/10.2135/cropsci1983.0011183X002300050016x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(carlson.germination)
-dat <- carlson.germination
-dat$germ <- dat$germ/100 # Convert to percent
-
-# Separate response curve for each genotype.
-# Really, we should use a glmm with random int/slope for each genotype
-m1 <- glm(germ~ 0 + gen*nacl, data=dat, family=quasibinomial)
-
-# Plot data and fitted model
-libs(latticeExtra)
-newd <- data.frame(expand.grid(gen=levels(dat$gen), nacl=seq(0,2,length=100)))
-newd$pred <- predict(m1, newd, type="response")
-xyplot(germ~nacl|gen, dat, as.table=TRUE, main="carlson.germination",
-       xlab="Percent NaCl", ylab="Fraction germinated") +
-xyplot(pred~nacl|gen, newd, type='l', grid=list(h=1,v=0))
-
    
-
-# Calculate LD50 values.  Note, Carlson et al used quadratics, not glm.
-# MASS::dose.p cannot handle multiple slopes, so do a separate fit for
-# each genotype.  Results are vaguely similar to Carlson table 5.
-## libs(MASS)
-## for(ii in unique(dat$gen)){
-##   cat("\n", ii, "\n")
-##   mm <- glm(germ ~ 1 + nacl, data=dat, subset=gen==ii, family=quasibinomial(link="probit"))
-##   print(dose.p(mm))
-## }
-##              Dose         SE
-## Anchor    1.445728  0.05750418
-## Apollo    1.305804  0.04951644
-## Baker     1.444153  0.07653989
-## Drylander 1.351201  0.03111795
-## Grimm     1.395735  0.04206377
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/carmer.density-1.png b/docs/reference/carmer.density-1.png deleted file mode 100644 index 1cf8a5e4..00000000 Binary files a/docs/reference/carmer.density-1.png and /dev/null differ diff --git a/docs/reference/carmer.density.html b/docs/reference/carmer.density.html deleted file mode 100644 index 01e01372..00000000 --- a/docs/reference/carmer.density.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - - -Nonlinear maize yield-density model — carmer.density • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Nonlinear maize yield-density model

    - - -
    - -
    -

    Nonlinear maize yield-density model.

    -
    - - - -

    Format

    - -

    A data frame with 32 observations on the following 3 variables.

    -
    gen

    genotype/hybrid, 8 levels

    -
    pop

    population (plants)

    -
    yield

    yield, pounds per hill

    - -
    - -

    Details

    - -

    Eight single-cross hybrids were in the experiment--Hy2xOh7 and WF9xC103 were - included because it was believed they had optimum yields at relatively - high and low populations. Planted in 1963. Plots were thinned to - 2, 4, 6, 8 plants per hill, giving densities 8, 16, 24, 32 thousand - plants per acre. Hills were in rows 40 inches apart. - One hill = 1/4000 acre. - Split-plot design with 5 reps, density is main plot and subplot was hybrid.

    -

    Source

    - -

    S G Carmer and J A Jackobs (1965). - An Exponential Model for Predicting Optimum Plant Density and Maximum - Corn Yield. Agronomy Journal, 57, 241--244. - https://doi.org/10.2134/agronj1965.00021962005700030003x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(carmer.density)
-dat <- carmer.density
-dat$gen <- factor(dat$gen, levels=c('Hy2x0h7','WF9xC103','R61x187-2',
-                             'WF9x38-11','WF9xB14','C103xB14',
-                             '0h43xB37','WF9xH60'))
-
-# Separate analysis for each hybrid
-# Model: y = x * a * k^x.  Table 1 of Carmer and Jackobs.
-out <- data.frame(a=rep(NA,8), k=NA)
-preds <- NULL
-rownames(out) <- levels(dat$gen)
-newdat <- data.frame(pop=seq(2,8,by=.1))
-for(i in levels(dat$gen)){
-  print(i)
-  dati <- subset(dat, gen==i)
-  mi <- nls(yield ~ pop * a * k^pop, data=dati, start=list(a=10,k=1))
-  out[i, ] <- mi$m$getPars()
-  # Predicted values
-  pi <- cbind(gen=i, newdat, pred= predict(mi, newdat=newdat))
-  preds <- rbind(preds, pi)
-}
-
    #> [1] "Hy2x0h7"
-#> [1] "WF9xC103"
-#> [1] "R61x187-2"
-#> [1] "WF9x38-11"
-#> [1] "WF9xB14"
-#> [1] "C103xB14"
-#> [1] "0h43xB37"
-#> [1] "WF9xH60"
    # Optimum plant density is -1/log(k)
-out$pop.opt <- -1/log(out$k)
-round(out, 3)
-
    #>               a     k pop.opt
-#> Hy2x0h7   0.782 0.865   6.875
-#> WF9xC103  1.039 0.825   5.192
-#> R61x187-2 0.998 0.798   4.441
-#> WF9x38-11 1.042 0.825   5.203
-#> WF9xB14   1.067 0.806   4.647
-#> C103xB14  0.813 0.860   6.653
-#> 0h43xB37  0.673 0.862   6.740
-#> WF9xH60   0.858 0.854   6.358
    ##               a     k pop.opt
-## Hy2x0h7   0.782 0.865   6.875
-## WF9xC103  1.039 0.825   5.192
-## R61x187-2 0.998 0.798   4.441
-## WF9x38-11 1.042 0.825   5.203
-## WF9xB14   1.067 0.806   4.647
-## C103xB14  0.813 0.860   6.653
-## 0h43xB37  0.673 0.862   6.740
-## WF9xH60   0.858 0.854   6.358
-
-
-# Fit an overall fixed-effect with random deviations for each hybrid.
-libs(nlme)
-m1 <- nlme(yield ~ pop * a * k^pop,
-           fixed = a + k ~ 1,
-           random = a + k ~ 1|gen,
-           data=dat, start=c(a=10,k=1))
-# summary(m1) # Random effect for 'a' probably not needed
-
-
-libs(latticeExtra)
-# Plot Data, fixed-effect prediction, random-effect prediction.
-pdat <- expand.grid(gen=levels(dat$gen), pop=seq(2,8,length=50))
-pdat$pred <- predict(m1, pdat)
-pdat$predf <- predict(m1, pdat, level=0)
-
-xyplot(yield~pop|gen, dat, pch=16, as.table=TRUE,
-       main="carmer.density models",
-       key=simpleKey(text=c("Data", "Fixed effect","Random effect"),
-         col=c("blue", "red","darkgreen"), columns=3, points=FALSE)) +
-  xyplot(predf~pop|gen, pdat, type='l', as.table=TRUE, col="red") +
-  xyplot(pred~pop|gen, pdat, type='l', col="darkgreen", lwd=2)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cate.potassium-1.png b/docs/reference/cate.potassium-1.png deleted file mode 100644 index 1afe1f31..00000000 Binary files a/docs/reference/cate.potassium-1.png and /dev/null differ diff --git a/docs/reference/cate.potassium.html b/docs/reference/cate.potassium.html deleted file mode 100644 index 0b4daf22..00000000 --- a/docs/reference/cate.potassium.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - - -Relative cotton yield for different soil potassium concentrations — cate.potassium • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Relative cotton yield for different soil potassium concentrations

    - - -
    - -
    -

    Relative cotton yield for different soil potassium concentrations

    -
    - - - -

    Format

    - -

    A data frame with 24 observations on the following 2 variables.

    -
    yield

    Relative yield

    -
    potassium

    Soil potassium, ppm

    - -
    - -

    Details

    - -

    Cate & Nelson used this data to determine the minimum optimal amount - of soil potassium to achieve maximum yield.

    -

    Note, Fig 1 of Cate & Nelson does not match the data from Table 2. It - sort of appears that points with high-concentrations of potassium were - shifted left to a truncation point. Also, the calculations below do - not quite match the results in Table 1. Perhaps the published data - were rounded?

    -

    Source

    - -

    Cate, R.B. and Nelson, L.A. (1971). - A simple statistical procedure for partitioning soil test correlation - data into two classes. - Soil Science Society of America Journal, 35, 658--660. - https://doi.org/10.2136/sssaj1971.03615995003500040048x

    - -

    Examples

    - 
    
-library(agridat)
-
-data(cate.potassium)
-dat <- cate.potassium
-names(dat) <- c('y','x')
-
-CateNelson <- function(dat){
-  dat <- dat[order(dat$x),] # Sort the data by x
-  x <- dat$x
-  y <- dat$y
-
-  # Create a data.frame to store the results
-  out <- data.frame(x=NA, mean1=NA, css1=NA, mean2=NA, css2=NA, r2=NA)
-
-  css <- function(x) { var(x) * (length(x)-1) }
-  tcss <- css(y) # Total corrected sum of squares
-
-  for(i in 2:(length(y)-2)){
-    y1 <- y[1:i]
-    y2 <- y[-(1:i)]
-
-    out[i, 'x'] <- x[i]
-    out[i, 'mean1'] <- mean(y1)
-    out[i, 'mean2'] <- mean(y2)
-    out[i, 'css1'] <- css1 <- css(y1)
-    out[i, 'css2'] <- css2 <- css(y2)
-    out[i, 'r2'] <-  ( tcss - (css1+css2)) / tcss
-  }
-  return(out)
-}
-
-cn <- CateNelson(dat)
-ix <- which.max(cn$r2)
-with(dat, plot(y~x, ylim=c(0,110), xlab="Potassium", ylab="Yield"))
-
    title("cate.potassium - Cate-Nelson analysis")
-
    abline(v=dat$x[ix], col="skyblue")
-
    abline(h=(dat$y[ix] + dat$y[ix+1])/2, col="skyblue")
-
    
-# \dontrun{
-  # another approach with similar results
-  # https://joe.org/joe/2013october/tt1.php
-  libs("rcompanion")
-  cateNelson(dat$x, dat$y, plotit=0)
-
    #> ....................
-#> 
-#> Critical x that maximize sum of squares: 
-#>  
-#>   Critical.x.value Sum.of.squares
-#> 1             46.5       5374.217
-#> ........................
-#> 
-#> Critical y that minimize errors: 
-#>  
-#>   Critical.y.value Q.i Q.ii Q.iii Q.iv Q.model Q.err Cramer.V
-#> 1            82.40   0   15     1    8      23     1   0.9129
-#> 2            65.65   2   16     0    6      22     2   0.8165
-#> 3            74.90   1   15     1    7      22     2   0.8125
-#> 4            85.55   0   14     2    8      22     2   0.8367
-#> 
-#> n         = Number of observations 
-#> CLx       = Critical value of x 
-#> SS        = Sum of squares for that critical value of x 
-#> CLy       = Critical value of y 
-#> Q         = Number of observations which fall into quadrants I, II, III, IV 
-#> Q.Model   = Total observations which fall into the quadrants predicted by the model 
-#> p.Model   = Percent observations which fall into the quadrants predicted by the model 
-#> Q.Error   = Observations which do not fall into the quadrants predicted by the model 
-#> p.Error   = Percent observations which do not fall into the quadrants predicted by the model 
-#> Fisher.p  = p-value from Fisher exact test dividing data into these quadrants 
-#> Cramer.V  = Cramer's V statistic from dividing data into these quadrants 
-#> 
-#> Final model: 
-#>  
    #>    n  CLx       SS  CLy Q.I Q.II Q.III Q.IV Q.Model   p.Model Q.Error
-#> 1 24 46.5 5374.217 82.4   0   15     1    8      23 0.9583333       1
-#>      p.Error Fisher.p.value Cramer.V
-#> 1 0.04166667   1.223706e-05   0.9129
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/chinloy.fractionalfactorial-1.png b/docs/reference/chinloy.fractionalfactorial-1.png deleted file mode 100644 index 9d99d670..00000000 Binary files a/docs/reference/chinloy.fractionalfactorial-1.png and /dev/null differ diff --git a/docs/reference/chinloy.fractionalfactorial.html b/docs/reference/chinloy.fractionalfactorial.html deleted file mode 100644 index c66bb5d5..00000000 --- a/docs/reference/chinloy.fractionalfactorial.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Sugarcane fractional factorial 1/3 3^5 — chinloy.fractionalfactorial • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Sugarcane fractional factorial 1/3 3^5

    - - -
    - -
    -

    Sugarcane fractional factorial 1/3 3^5.

    -
    - - 
    data("chinloy.fractionalfactorial")
    - - -

    Format

    - -

    A data frame with 81 observations on the following 10 variables.

    -
    yield

    yield

    -
    block

    block

    -
    row

    row position

    -
    col

    column position

    -
    trt

    treatment code

    -
    N

    nitrogen treatment: 0, 1, 2

    -
    P

    phosphorous treatment: 0, 1, 2

    -
    K

    potassium treatment: 0, 1, 2

    -
    B

    bagasse treatment: 0, 1, 2

    -
    F

    filter press mud treatment: 0, 1, 2

    - - - -

    Details

    - - - -

    An experiment grown in 1949 at the Worthy Park Estate in Jamaica.

    -

    Nitrogen was applied as sulphate of ammonia at 0, 3, 6 hundred-weight - per acre.

    -

    Phosphorous was applied as superphosphate at 0, 4, 8 hundred-weight - per acre.

    -

    Potassium was applied as muriate of potash at 0, 1, 2 hundred-weight - per acre.

    -

    Bagasse applied pre-plant at 0, 20, 40 tons per acre.

    -

    Filter press mud applied pre-plant at 0, 10, 20 tons per acre.

    -

    Each plot was 18 yards long by 6 yards (3 rows) wide. Plots were - arranged in nine columns of nine, a 2-yard space separating plots - along the rows and two guard rows separating plots across the rows.

    -

    Field width: 6 yards * 9 plots + 4 yards * 8 gaps = 86 yards

    -

    Field length: 18 yards * 9 plots + 2 yards * 8 gaps = 178 yards

    -

    Source

    - -

    T. Chinloy, R. F. Innes and D. J. Finney. (1953). - An example of fractional replication in an experiment on sugar cane manuring. - Journ Agricultural Science, 43, 1-11. - https://doi.org/10.1017/S0021859600044567

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(chinloy.fractionalfactorial)
-dat <- chinloy.fractionalfactorial
-
-# Treatments are coded with levels 0,1,2. Make sure they are factors
-dat <- transform(dat,
-                 N=factor(N), P=factor(P), K=factor(K), B=factor(B), F=factor(F))
-
-# Experiment layout
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        out1=block, text=trt, shorten="no", cex=0.6,
-        aspect=178/86,
-        main="chinloy.fractionalfactorial")
    
-# Main effect and some two-way interactions. These match Chinloy table 6.
-# Not sure how to code terms like P^2K=B^2F
-m1 <- aov(yield ~ block + N + P + K + B + F + N:P + N:K + N:B + N:F, dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> block      8 10.6223  1.3278  2.9797  0.008942 ** 
-#> N          2  4.5399  2.2699  5.0940  0.010037 *  
-#> P          2 11.9864  5.9932 13.4496 2.517e-05 ***
-#> K          2  2.5091  1.2546  2.8154  0.070230 .  
-#> B          2  5.2851  2.6426  5.9303  0.005112 ** 
-#> F          2 13.9405  6.9702 15.6422 6.566e-06 ***
-#> N:P        4  5.0740  1.2685  2.8467  0.034366 *  
-#> N:K        4  1.2432  0.3108  0.6975  0.597636    
-#> N:B        4  1.9243  0.4811  1.0796  0.377618    
-#> N:F        4  2.1110  0.5277  1.1843  0.330265    
-#> Residuals 46 20.4979  0.4456                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/christidis.competition-1.png b/docs/reference/christidis.competition-1.png deleted file mode 100644 index e2c0ec44..00000000 Binary files a/docs/reference/christidis.competition-1.png and /dev/null differ diff --git a/docs/reference/christidis.competition-2.png b/docs/reference/christidis.competition-2.png deleted file mode 100644 index 189ab2bc..00000000 Binary files a/docs/reference/christidis.competition-2.png and /dev/null differ diff --git a/docs/reference/christidis.competition.html b/docs/reference/christidis.competition.html deleted file mode 100644 index c1792ed6..00000000 --- a/docs/reference/christidis.competition.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - - -Competition between varieties in cotton — christidis.competition • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Competition between varieties in cotton

    - - -
    - -
    -

    Competition between varieties in cotton, measurements taken for each row.

    -
    - - 
    data("christidis.competition")
    - - -

    Format

    - -

    A data frame with 270 observations on the following 8 variables.

    -
    plot

    plot

    -
    plotrow

    row within plot

    -
    block

    block

    -
    row

    row, only 1 row

    -
    col

    column

    -
    gen

    genotype

    -
    yield

    yield, kg

    -
    height

    height, cm

    - - - -

    Details

    - - - -

    Nine genotypes/varieties of cotton were used in a variety test. - The plots were 100 meters long and 2.40 meters wide, each plot having - 3 rows 0.80 meters apart.

    -

    The layout was an RCB of 5 blocks, each block having 2 replicates of - every variety (with the original intention of trying 2 seed - treatments). Each row was harvested/weighed separately. - After the leaves of the plants had dried up and fallen, the mean - height of each row was measured.

    -

    Christidis found significant competition between varieties, but not - due to height differences. Crude analysis.

    -

    TODO: Find a better analysis of this data which incorporates field - trends AND competition effects, maybe including a random effect for - border rows of all genotype pairs (as neighbors)?

    -

    Source

    - -

    Christidis, Basil G (1935). - Intervarietal competition in yield trials with cotton. - The Journal of Agricultural Science, 25, 231-237. Table 1. - https://doi.org/10.1017/S0021859600009710

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(christidis.competition)
-dat <- christidis.competition
-
-# Match Christidis Table 2 means
-# aggregate(yield ~ gen, aggregate(yield ~ gen+plot, dat, sum), mean)
-
-# Each RCB block has 2 replicates of each genotype
-# with(dat, table(block,gen))
-
-libs(lattice)
-
-# Tall plants yield more
-# xyplot(yield ~ height|gen, data=dat)
-
-# Huge yield variation across field. Also heterogeneous variance.
-xyplot(yield ~ col, dat, group=gen, auto.key=list(columns=5),
-       main="christidis.competition")
    
-
-libs(mgcv)
-if(is.element("package:gam", search())) detach("package:gam")
-# Simple non-competition model to remove main effects
-m1 <- gam(yield ~ gen + s(col), data=dat)
-p1 <- as.data.frame(predict(m1, type="terms"))
-names(p1) <- c('geneff','coleff')
-dat2 <- cbind(dat, p1)
-dat2 <- transform(dat2, res=yield-geneff-coleff)
-libs(lattice)
-xyplot(res ~  col, data=dat2, group=gen,
-       main="christidis.competition - residuals")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/christidis.cotton.uniformity-1.png b/docs/reference/christidis.cotton.uniformity-1.png deleted file mode 100644 index d782cd1f..00000000 Binary files a/docs/reference/christidis.cotton.uniformity-1.png and /dev/null differ diff --git a/docs/reference/christidis.cotton.uniformity.html b/docs/reference/christidis.cotton.uniformity.html deleted file mode 100644 index 643ee0a0..00000000 --- a/docs/reference/christidis.cotton.uniformity.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - - - -Uniformity trial of cotton — christidis.cotton.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of cotton

    - - -
    - -
    -

    Uniformity trial of cotton in Greece, 1938

    -
    - - 
    data("christidis.cotton.uniformity")
    - - -

    Format

    - -

    A data frame with 1024 observations on the following 4 variables.

    -
    col

    column

    -
    row

    row

    -
    yield

    yield, kg/unit

    -
    block

    block factor

    - -
    - -

    Details

    - -

    The experiment was conducted in 1938 at Sindos by the Greek Cotton Research Institute.

    -

    Each block consisted of 20 rows, 1 meter apart and 66 meters long. Two - rows on each side and 1 meter on each end were removed for - borderr Each row was divided into 4 meter-lengths and harvested - separately. There were 4 blocks, oriented at 0, 30, 60, 90 degrees.

    -

    Each block contained 16 rows, each 64 meters long.

    -

    Field width: 16 units * 4 m = 64 m

    -

    Field depth: 16 rows * 1 m = 16 m

    -

    Source

    - -

    Christidis, B. G. (1939). - Variability of Plots of Various Shapes as Affected by Plot Orientation. - Empire Journal of Experimental Agriculture 7: 330-342. Table 1.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(christidis.cotton.uniformity)
-dat <- christidis.cotton.uniformity
-  
-# Match the mean yields in table 2. Not sure why '16' is needed
-# sapply(split(dat$yield, dat$block), mean)*16
-  
-libs(desplot)
-dat$yld <- dat$yield/4*1000 # re-scale to match Christidis fig 1
-desplot(dat, yld ~ col*row|block,
-        flip=TRUE, aspect=(16)/(64),
-        main="christidis.cotton.uniformity")
-
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/christidis.wheat.uniformity-1.png b/docs/reference/christidis.wheat.uniformity-1.png deleted file mode 100644 index 51af4810..00000000 Binary files a/docs/reference/christidis.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/christidis.wheat.uniformity.html b/docs/reference/christidis.wheat.uniformity.html deleted file mode 100644 index effe7c6d..00000000 --- a/docs/reference/christidis.wheat.uniformity.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — christidis.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat at Cambridge, UK in 1931.

    -
    - - 
    data("christidis.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 288 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - - - -

    Two blocks, 24 rows each. In block A, each 90-foot row was divided into -12 units, each unit 7.5 feet long. Rows were 8 inches wide.

    -

    Field width: 12 units * 7.5 feet = 90 feet

    -

    Field length: 24 rows * 8 inches = 16 feet

    -

    Source

    - -

    Christidis, Basil G (1931). - The importance of the shape of plots in field experimentation. - The Journal of Agricultural Science, 21, 14-37. Table VI, p. 28. - https://dx.doi.org/10.1017/S0021859600007942

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(christidis.wheat.uniformity)
-dat <- christidis.wheat.uniformity
-  
-# sum(dat$yield) # Matches Christidis
-  
- libs(desplot)
- desplot(dat, yield ~  col*row,
-         flip=TRUE, aspect=16/90, # true aspect
-         main="christidis.wheat.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cleveland.soil-1.png b/docs/reference/cleveland.soil-1.png deleted file mode 100644 index 396e2f9b..00000000 Binary files a/docs/reference/cleveland.soil-1.png and /dev/null differ diff --git a/docs/reference/cleveland.soil.html b/docs/reference/cleveland.soil.html deleted file mode 100644 index bcc11d71..00000000 --- a/docs/reference/cleveland.soil.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - -Soil resistivity in a field — cleveland.soil • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Soil resistivity in a field

    - - -
    - -
    -

    Soil resistivity in a field

    -
    - - - -

    Format

    - -

    A data frame with 8641 observations on the following 5 variables.

    -
    northing

    y ordinate

    -
    easting

    x ordinate

    -
    resistivity

    Soil resistivity, ohms

    -
    is.ns

    Indicator of north/south track

    -
    track

    Track number

    - -
    - -

    Details

    - -

    Resistivity is related to soil salinity.

    -

    Source

    - -

    William Cleveland, (1993), Visualizing Data. - Electronic version from StatLib: https://lib.stat.cmu.edu/datasets/

    -

    Cleaned version from Luke Tierney - https://homepage.stat.uiowa.edu/~luke/classes/248/examples/soil

    - -

    Examples

    - 
    
-library(agridat)
-
-data(cleveland.soil)
-dat <- cleveland.soil
-
-# Similar to Cleveland fig 4.64
-## libs(latticeExtra)
-## redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-## levelplot(resistivity ~ easting + northing, data = dat,
-##           col.regions=redblue,
-##           panel=panel.levelplot.points,
-##           aspect=2.4, xlab= "Easting (km)", ylab= "Northing (km)",
-##           main="cleveland")
-
-# 2D loess plot. Cleveland fig 4.68
-sg1 <- expand.grid(easting = seq(.15, 1.410, by = .02),
-                   northing = seq(.150, 3.645, by = .02))
-lo1 <- loess(resistivity~easting*northing, data=dat, span = 0.1, degree = 2)
-fit1 <- predict(lo1, sg1)
-libs(lattice)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(fit1 ~ sg1$easting * sg1$northing,
-          col.regions=redblue,
-          cuts = 9,
-          aspect=2.4, xlab = "Easting (km)", ylab = "Northing (km)",
-          main="cleveland.soil - 2D smooth of Resistivity")
-
    
-# ----------------------------------------------------------------------------
-
-# \dontrun{
-  # 3D loess plot with data overlaid
-  libs(rgl)
-
    #> 
-#> Attaching package: 'rgl'
    #> The following object is masked from 'package:plotrix':
-#> 
-#>     mtext3d
      bg3d(color = "white")
-  clear3d()
-  points3d(dat$easting, dat$northing, dat$resistivity / 100,
-           col = rep("gray50", nrow(dat)))
-  rgl::surface3d(seq(.15, 1.410, by = .02),
-                 seq(.150, 3.645, by = .02),
-                 fit1/100, alpha=0.9, col=rep("wheat", length(fit1)),
-                 front="fill", back="fill")
-  rgl.close()
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.beets-1.png b/docs/reference/cochran.beets-1.png deleted file mode 100644 index 3f2ce89e..00000000 Binary files a/docs/reference/cochran.beets-1.png and /dev/null differ diff --git a/docs/reference/cochran.beets.html b/docs/reference/cochran.beets.html deleted file mode 100644 index 773d68a6..00000000 --- a/docs/reference/cochran.beets.html +++ /dev/null @@ -1,217 +0,0 @@ - - - - - - - - -Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Yield and number of plants in a sugarbeet fertilizer experiment

    - - -
    - -
    -

    Yield and number of plants in a sugarbeet fertilizer experiment.

    -
    - - 
    data("cochran.beets")
    - - -

    Format

    - -

    A data frame with 42 observations on the following 4 variables.

    -
    fert

    fertilizer treatment

    -
    block

    block

    -
    yield

    yield, tons/acres

    -
    plants

    number of plants per plot

    - -
    - -

    Details

    - -

    Yield (tons/acre) and number of beets per plot. Fertilizer - treatments combine superphosphate (P), muriate of potash (K), - and sodium nitrate (N).

    -

    Source

    - -

    George Snedecor (1946). - Statisitcal Methods, 4th ed. Table 12.13, p. 332.

    -

    References

    - -

    H. Fairfield Smith (1957). - Interpretation of Adjusted Treatment Means and Regressions in Analysis - of Covariance. - Biometrics, 13, 282-308. - https://doi.org/10.2307/2527917

    - -

    Examples

    - 
    
-library(agridat)
-data(cochran.beets)
-dat = cochran.beets
-
-# P has strong effect
-libs(lattice)
-xyplot(yield ~ plants|fert, dat, main="cochran.beets") 
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.bib-1.png b/docs/reference/cochran.bib-1.png deleted file mode 100644 index 973a0224..00000000 Binary files a/docs/reference/cochran.bib-1.png and /dev/null differ diff --git a/docs/reference/cochran.bib.html b/docs/reference/cochran.bib.html deleted file mode 100644 index 95c0af98..00000000 --- a/docs/reference/cochran.bib.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Multi-environment trial of corn, balanced incomplete block design — cochran.bib • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn, balanced incomplete block design

    - - -
    - -
    -

    Balanced incomplete block design in corn

    -
    - - - -

    Format

    - -

    A data frame with 52 observations on the following 3 variables.

    -
    loc

    location/block, 13 levels

    -
    gen

    genotype/line, 13 levels

    -
    yield

    yield, pounds/plot

    - - - -

    Details

    - -

    Incomplete block design. Each loc/block has 4 genotypes/lines. - The blocks are planted at different locations.

    -

    Conducted in 1943 in North Carolina.

    -

    Source

    - -

    North Carolina Agricultural Experiment Station, - United States Department of Agriculture.

    -

    References

    - -

    Cochran, W.G. and Cox, G.M. (1957), - Experimental Designs, 2nd ed., Wiley and Sons, New York, p. 448.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(cochran.bib)
-dat <- cochran.bib
-
-# Show the incomplete-block structure
-libs(lattice)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(yield~loc*gen, dat,
-          col.regions=redblue,
-          xlab="loc (block)", main="cochran.bib - incomplete blocks")
    
-with(dat, table(gen,loc))
    #>      loc
-#> gen   B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 B13
-#>   G01   0   0   0   0   0   0   1   0   1   0   1   1   0
-#>   G02   0   0   0   1   0   0   0   0   1   1   0   0   1
-#>   G03   1   1   0   0   0   0   0   1   1   0   0   0   0
-#>   G04   0   1   0   0   0   1   0   0   0   1   1   0   0
-#>   G05   0   0   0   1   0   1   1   1   0   0   0   0   0
-#>   G06   1   0   0   0   0   1   0   0   0   0   0   1   1
-#>   G07   0   0   0   0   1   0   0   1   0   0   1   0   1
-#>   G08   0   1   0   1   1   0   0   0   0   0   0   1   0
-#>   G09   1   0   0   0   1   0   1   0   0   1   0   0   0
-#>   G10   0   0   1   0   1   1   0   0   1   0   0   0   0
-#>   G11   1   0   1   1   0   0   0   0   0   0   1   0   0
-#>   G12   0   1   1   0   0   0   1   0   0   0   0   0   1
-#>   G13   0   0   1   0   0   0   0   1   0   1   0   1   0
    rowSums(as.matrix(with(dat, table(gen,loc))))
    #> G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 G13 
-#>   4   4   4   4   4   4   4   4   4   4   4   4   4 
    colSums(as.matrix(with(dat, table(gen,loc))))
    #> B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 B13 
-#>   4   4   4   4   4   4   4   4   4   4   4   4   4 
    
-m1 = aov(yield ~ gen + Error(loc), data=dat)
-summary(m1)
    #> 
-#> Error: loc
-#>     Df Sum Sq Mean Sq
-#> gen 12  689.4   57.45
-#> 
-#> Error: Within
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> gen       12  328.5   27.38   1.373  0.238
-#> Residuals 27  538.2   19.93               
    
-libs(nlme)
-m2 = lme(yield ~ -1 + gen, data=dat, random=~1|loc)
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.crd-1.png b/docs/reference/cochran.crd-1.png deleted file mode 100644 index e1ebc0dc..00000000 Binary files a/docs/reference/cochran.crd-1.png and /dev/null differ diff --git a/docs/reference/cochran.crd.html b/docs/reference/cochran.crd.html deleted file mode 100644 index e7ab00fe..00000000 --- a/docs/reference/cochran.crd.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Potato scab infection with sulfur treatments — cochran.crd • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Potato scab infection with sulfur treatments

    - - -
    - -
    -

    Potato scab infection with sulfur treatments

    -
    - - - -

    Format

    - -

    A data frame with 32 observations on the following 5 variables.

    -
    inf

    infection percent

    -
    trt

    treatment factor

    -
    row

    row

    -
    col

    column

    - -
    - -

    Details

    - -

    The experiment was conducted to investigate the effect of sulfur on - controlling scab disease in potatoes. There were seven treatments. - Control, plus spring and fall application of 300, 600, 1200 pounds/acre of - sulfur. The response variable was infection as a percent of the surface - area covered with scab. A completely randomized design was used with 8 - replications of the control and 4 replications of the other treatments.

    -

    Although the original analysis did not show significant differences in - the sulfur treatments, including a polynomial trend in the model - uncovered significant differences (Tamura, 1988).

    -

    Source

    - -

    W.G. Cochran and G. Cox, 1957. Experimental Designs, 2nd - ed. John Wiley, New York.

    -

    References

    - -

    Tamura, R.N. and Nelson, L.A. and Naderman, G.C., (1988). - An investigation of the validity and usefulness of - trend analysis for field plot data. - Agronomy Journal, 80, 712-718.

    -

    https://doi.org/10.2134/agronj1988.00021962008000050003x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cochran.crd)
-dat <- cochran.crd
-
-# Field plan
-libs(desplot)
-desplot(dat, inf~col*row,
-        text=trt, cex=1, # aspect unknown
-        main="cochran.crd")
-
    
-# CRD anova.  Table 6 of Tamura 1988
-contrasts(dat$trt) <- cbind(c1=c(1,1,1,-6,1,1,1),   # Control vs Sulf
-                            c2=c(-1,-1,-1,0,1,1,1)) # Fall vs Sp
-m1 <- aov(inf ~ trt, data=dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: inf
-#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
-#> trt        6  972.34 162.057  3.6081 0.01026 *
-#> Residuals 25 1122.88  44.915                  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    summary(m1, split=list(trt=list("Control vs Sulf"=1, "Fall vs Spring"=2)))
-
    #>                        Df Sum Sq Mean Sq F value  Pr(>F)   
-#> trt                     6  972.3   162.1   3.608 0.01026 * 
-#>   trt: Control vs Sulf  1  518.0   518.0  11.533 0.00229 **
-#>   trt: Fall vs Spring   1  228.2   228.2   5.080 0.03322 * 
-#> Residuals              25 1122.9    44.9                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# Quadratic polynomial for columns...slightly different than Tamura 1988
-m2 <- aov(inf ~ trt + poly(col,2), data=dat)
-anova(m2)
-
    #> Analysis of Variance Table
-#> 
-#> Response: inf
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> trt           6 972.34 162.057  7.1253 0.0002205 ***
-#> poly(col, 2)  2 599.76 299.881 13.1850 0.0001531 ***
-#> Residuals    23 523.11  22.744                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    summary(m2, split=list(trt=list("Control vs Sulf"=1, "Fall vs Spring"=2)))
-
    #>                        Df Sum Sq Mean Sq F value   Pr(>F)    
-#> trt                     6  972.3   162.1   7.125 0.000221 ***
-#>   trt: Control vs Sulf  1  518.0   518.0  22.776 8.21e-05 ***
-#>   trt: Fall vs Spring   1  228.2   228.2  10.032 0.004301 ** 
-#> poly(col, 2)            2  599.8   299.9  13.185 0.000153 ***
-#> Residuals              23  523.1    22.7                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.eelworms-1.png b/docs/reference/cochran.eelworms-1.png deleted file mode 100644 index 41f12939..00000000 Binary files a/docs/reference/cochran.eelworms-1.png and /dev/null differ diff --git a/docs/reference/cochran.eelworms-2.png b/docs/reference/cochran.eelworms-2.png deleted file mode 100644 index ebfabde0..00000000 Binary files a/docs/reference/cochran.eelworms-2.png and /dev/null differ diff --git a/docs/reference/cochran.eelworms.html b/docs/reference/cochran.eelworms.html deleted file mode 100644 index 09b0c35b..00000000 --- a/docs/reference/cochran.eelworms.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Counts of eelworms before and after fumigant treatments — cochran.eelworms • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Counts of eelworms before and after fumigant treatments

    - - -
    - -
    -

    Counts of eelworms before and after fumigant treatments

    -
    - - - -

    Format

    - -

    A data frame with 48 observations on the following 7 variables.

    -
    block

    block factor, 4 levels

    -
    row

    row

    -
    col

    column

    -
    fumigant

    fumigant factor

    -
    dose

    dose, Numeric 0,1,2. Maybe should be a factor?

    -
    initial

    count of eelworms pre-treatment

    -
    final

    count of eelworms post-treatment

    - - - -

    Details

    - - - -

    In the original experiment plan (as shown in Bailey 2008), columns 9, 10, 11 are - shifted up slightly.

    -

    Treatment codes: Con = Control, Chl = Chlorodinitrobenzen, Cym = - Cymag, Car = Carbon Disulphide jelly, See = Seekay.

    -

    Experiment was conducted in 1935 at Rothamsted Experiment Station. In - early March 400 grams of soil were sampled and the number of eelworm - cysts were counted. Fumigants were added to the soil, oats were sown - and later harvested. In October, the plots were again sampled and the - final count of cysts recorded.

    -

    Source

    - -

    Cochran and Cox, 1950. - Experimental Designs. Table 3.1.

    -

    References

    - -

    R. A. Bailey, 2008. Design of Comparative Experiments. Cambridge.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cochran.eelworms)
-dat <- cochran.eelworms
-
-# Very strong spatial trends
-libs(desplot)
-desplot(dat, initial ~ col*row,
-        flip=TRUE, # aspect unknown
-        main="cochran.eelworms")
    
-
-# final counts are strongly related to initial counts
-libs(lattice)
-xyplot(final~initial|factor(dose), data=dat, group=fumigant,
-       main="cochran.eelworms - by dose (panel) & fumigant",
-       xlab="Initial worm count",
-       ylab="Final worm count", auto.key=list(columns=5))
    
-# One approach...log transform, use 'initial' as covariate, create 9 treatments
-dat <- transform(dat, trt=factor(paste0(fumigant, dose)))
-m1 <- aov(log(final) ~ block + trt + log(initial), data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: log(final)
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> block         3 5.5727  1.8576 23.5588 1.973e-08 ***
-#> trt           9 3.4114  0.3790  4.8073  0.000358 ***
-#> log(initial)  1 5.2603  5.2603 66.7142 1.582e-09 ***
-#> Residuals    34 2.6808  0.0788                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.factorial-1.png b/docs/reference/cochran.factorial-1.png deleted file mode 100644 index a4d1ba2b..00000000 Binary files a/docs/reference/cochran.factorial-1.png and /dev/null differ diff --git a/docs/reference/cochran.factorial.html b/docs/reference/cochran.factorial.html deleted file mode 100644 index 6072bd15..00000000 --- a/docs/reference/cochran.factorial.html +++ /dev/null @@ -1,261 +0,0 @@ - - - - - - - - -Factorial experiment in beans — cochran.factorial • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Factorial experiment in beans

    - - -
    - -
    -

    Factorial experiment in beans

    -
    - - 
    data("cochran.factorial")
    - - -

    Format

    - -

    A data frame with 32 observations on the following 4 variables.

    -
    rep

    rep factor

    -
    block

    block factor

    -
    trt

    treatment factor, 16 levels

    -
    yield

    yield (pounds)

    - - - -

    Details

    - -

    Conducted by Rothamsted Experiment Station in 1936. The treatments - are listed below.

    -

    d = dung: None, 10 tons/acre.

    -

    n = nitrochalk: None, 0.4 hundredweight nitrogen per acre.

    -

    p = Superphosphate: None, 0.6 hundredweight per acre

    -

    k = muriate of potash: None, 1 hundredweight K20 per acres.

    -

    The response variable is the yield of beans.

    -

    Source

    - -

    Cochran, W.G. and Cox, G.M. (1957), - Experimental Designs, 2nd ed., Wiley and Sons, New York, p. 160.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cochran.factorial)
-dat <- cochran.factorial
-
-# Split treatment into individual factors
-dat <- transform(dat,
-                 d = -1 + 2 * grepl('d',trt),
-                 n = -1 + 2 * grepl('n',trt),
-                 p = -1 + 2 * grepl('p',trt),
-                 k = -1 + 2 * grepl('k',trt))
-dat <- transform(dat, d=factor(d), n=factor(n), p=factor(p), k=factor(k))
-
-# Cochran table 6.5.
-m1 <- lm(yield ~ rep * block + (d+n+p+k)^3, data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq F value   Pr(>F)   
-#> rep        1   3.12    3.12  0.1288 0.725064   
-#> block      1  78.13   78.13  3.2193 0.094393 . 
-#> d          1   2.00    2.00  0.0824 0.778258   
-#> n          1 325.12  325.12 13.3974 0.002572 **
-#> p          1   6.12    6.12  0.2524 0.623205   
-#> k          1   4.50    4.50  0.1854 0.673303   
-#> rep:block  1  45.12   45.12  1.8595 0.194213   
-#> d:n        1  32.00   32.00  1.3186 0.270083   
-#> d:p        1 242.00  242.00  9.9720 0.006982 **
-#> d:k        1   6.13    6.13  0.2524 0.623205   
-#> n:p        1  78.13   78.13  3.2193 0.094393 . 
-#> n:k        1  32.00   32.00  1.3186 0.270083   
-#> p:k        1  24.50   24.50  1.0096 0.332058   
-#> d:n:p      1   2.00    2.00  0.0824 0.778258   
-#> d:n:k      1  10.13   10.13  0.4172 0.528774   
-#> d:p:k      1  15.13   15.13  0.6233 0.443007   
-#> n:p:k      1  32.00   32.00  1.3186 0.270083   
-#> Residuals 14 339.75   24.27                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-
-libs(FrF2)
    #> Loading required package: DoE.base
    #> Loading required package: conf.design
    #> 
-#> Attaching package: 'conf.design'
    #> The following object is masked from 'package:lme4':
-#> 
-#>     factorize
    #> Registered S3 method overwritten by 'DoE.base':
-#>   method           from       
-#>   factorize.factor conf.design
    #> 
-#> Attaching package: 'DoE.base'
    #> The following objects are masked from 'package:stats':
-#> 
-#>     aov, lm
    #> The following object is masked from 'package:graphics':
-#> 
-#>     plot.design
    #> The following object is masked from 'package:base':
-#> 
-#>     lengths
    aliases(m1)
    #> [1] no aliasing in the model
    MEPlot(m1, select=3:6,
-       main="cochran.factorial - main effects plot")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.latin-1.png b/docs/reference/cochran.latin-1.png deleted file mode 100644 index c0a9f1ea..00000000 Binary files a/docs/reference/cochran.latin-1.png and /dev/null differ diff --git a/docs/reference/cochran.latin.html b/docs/reference/cochran.latin.html deleted file mode 100644 index ac107b56..00000000 --- a/docs/reference/cochran.latin.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Latin square design in wheat — cochran.latin • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Latin square design in wheat

    - - -
    - -
    -

    Six wheat plots were sampled by six operators and shoot heights - measured. The operators sampled plots in six ordered sequences. - The dependent variate was the difference between measured - height and true height of the plot.

    -
    - - - -

    Format

    - -

    A data frame with 36 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    operator

    operator factor

    -
    diff

    difference between measured height and true height

    - - - -

    Source

    - -

    Cochran, W.G. and Cox, G.M. (1957), - Experimental Designs, 2nd ed., Wiley and Sons, New York.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(cochran.latin)
-dat <- cochran.latin
-
-libs(desplot)
-desplot(dat, diff~col*row,
-        text=operator, cex=1, # aspect unknown
-        main="cochran.latin")
    
-
-dat <- transform(dat, rf=factor(row), cf=factor(col))
-aov.dat <- aov(diff ~ operator + Error(rf*cf), dat)
-summary(aov.dat)
    #> 
-#> Error: rf
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  5   28.6    5.72               
-#> 
-#> Error: cf
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  5  78.87   15.77               
-#> 
-#> Error: rf:cf
-#>           Df Sum Sq Mean Sq F value   Pr(>F)    
-#> operator   5 155.60  31.119    9.35 0.000103 ***
-#> Residuals 20  66.56   3.328                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    model.tables(aov.dat, type="means")
    #> Tables of means
-#> Grand mean
-#>          
-#> 4.758333 
-#> 
-#>  operator 
-#> operator
-#>     a     b     c     d     e     f 
-#> 6.067 5.583 6.117 6.917 2.667 1.200
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.lattice-1.png b/docs/reference/cochran.lattice-1.png deleted file mode 100644 index 8519fbe8..00000000 Binary files a/docs/reference/cochran.lattice-1.png and /dev/null differ diff --git a/docs/reference/cochran.lattice.html b/docs/reference/cochran.lattice.html deleted file mode 100644 index df26303e..00000000 --- a/docs/reference/cochran.lattice.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - - -Balanced lattice experiment in cotton — cochran.lattice • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - -
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    Balanced lattice experiment in cotton

    - - -
    - -
    -

    Balanced lattice experiment in cotton

    -
    - - 
    data("cochran.lattice")
    - - -

    Format

    - -

    A data frame with 80 observations on the following 5 variables.

    -
    y

    percent of affected flower buds

    -
    rep

    replicate

    -
    row

    row

    -
    col

    column

    -
    trt

    treatment factor

    - -
    - -

    Details

    - - - -

    The experiment is a balanced lattice square with 16 treatments in a 4x4 - layout in each of 5 replicates. The treatments were applied to cotton - plants. Each plot was ten rows wide by 70 feet long - (about 1/18 of an acre). (Estimated plot width is 34.5 feet.) - Data were collected from the middle 4 rows. - The data are the percentages of squares showing attack by boll weevils. - A 'square' is the name given to a young flower bud.

    -

    The plot orientation is not clear.

    -

    Source

    - - - -

    William G. Cochran, Gertrude M. Cox. Experimental Designs, 2nd - Edition. Page 490.

    -

    Originally from: F. M. Wadley (1946). - Incomplete block designs in insect population problems. - J. Economic Entomology, 38, 651--654.

    -

    References

    - - - -

    Walter Federer. - Combining Standard Block Analyses With Spatial Analyses Under a Random - Effects Model. Cornell Univ Tech Report BU-1373-MA. - https://hdl.handle.net/1813/31971

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cochran.lattice)
-dat <- cochran.lattice
-
-libs(desplot)
-desplot(dat, y~row*col|rep,
-        text=trt, # aspect unknown, should be 2 or .5
-         main="cochran.lattice")
-
    
-
-# Random rep,row,column model often used by Federer
-libs(lme4)
-dat <- transform(dat, rowf=factor(row), colf=factor(col))
-m1 <-  lmer(y ~ trt + (1|rep) + (1|rep:row) + (1|rep:col), data=dat)
-
    #> boundary (singular) fit: see ?isSingular
    summary(m1)
-
    #> Linear mixed model fit by REML ['lmerMod']
-#> Formula: y ~ trt + (1 | rep) + (1 | rep:row) + (1 | rep:col)
-#>    Data: dat
-#> 
-#> REML criterion at convergence: 433.9
-#> 
-#> Scaled residuals: 
-#>     Min      1Q  Median      3Q     Max 
-#> -2.1384 -0.5057 -0.0117  0.4584  2.2629 
-#> 
-#> Random effects:
-#>  Groups   Name        Variance Std.Dev.
-#>  rep:col  (Intercept)  3.17    1.781   
-#>  rep:row  (Intercept) 10.92    3.304   
-#>  rep      (Intercept)  0.00    0.000   
-#>  Residual             23.86    4.885   
-#> Number of obs: 80, groups:  rep:col, 20; rep:row, 20; rep, 5
-#> 
-#> Fixed effects:
-#>             Estimate Std. Error t value
-#> (Intercept)    6.090      2.566   2.373
-#> trtT02         7.641      3.451   2.214
-#> trtT03         2.403      3.451   0.696
-#> trtT04         5.254      3.451   1.522
-#> trtT05         3.472      3.451   1.006
-#> trtT06         1.206      3.451   0.350
-#> trtT07         1.208      3.451   0.350
-#> trtT08         3.323      3.451   0.963
-#> trtT09         4.024      3.451   1.166
-#> trtT10         9.094      3.451   2.635
-#> trtT11        11.815      3.451   3.423
-#> trtT12         6.756      3.451   1.958
-#> trtT13         4.942      3.451   1.432
-#> trtT14         8.160      3.451   2.364
-#> trtT15         3.205      3.451   0.929
-#> trtT16         4.537      3.451   1.315
    #> 
-#> Correlation matrix not shown by default, as p = 16 > 12.
-#> Use print(x, correlation=TRUE)  or
-#>     vcov(x)        if you need it
    #> convergence code: 0
-#> boundary (singular) fit: see ?isSingular
-#> 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cochran.wireworms-1.png b/docs/reference/cochran.wireworms-1.png deleted file mode 100644 index ce1f5a67..00000000 Binary files a/docs/reference/cochran.wireworms-1.png and /dev/null differ diff --git a/docs/reference/cochran.wireworms-2.png b/docs/reference/cochran.wireworms-2.png deleted file mode 100644 index 7f7430b5..00000000 Binary files a/docs/reference/cochran.wireworms-2.png and /dev/null differ diff --git a/docs/reference/cochran.wireworms.html b/docs/reference/cochran.wireworms.html deleted file mode 100644 index b855e0ca..00000000 --- a/docs/reference/cochran.wireworms.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - - -Wireworms controlled by fumigants in a latin square — cochran.wireworms • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Wireworms controlled by fumigants in a latin square

    - - -
    - -
    -

    Wireworms controlled by fumigants in a latin square

    -
    - - - -

    Format

    - -

    A data frame with 25 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    trt

    fumigant treatment, 5 levels

    -
    worms

    count of wireworms per plot

    - -
    - -

    Details

    - -

    Plots were approximately 22 cm by 13 cm. Layout of the experiment was a - latin square. The number of wireworms in each plot was counted, - following soil fumigation the previous year.

    -

    Source

    - -

    W. G. Cochran (1938). - Some difficulties in the statistical analysis of replicated experiments. - Empire Journal of Experimental Agriculture, 6, 157--175.

    -

    References

    - -

    Ron Snee (1980). Graphical Display of Means. - The American Statistician, 34, 195-199. - https://www.jstor.org/stable/2684060 - https://doi.org/10.1080/00031305.1980.10483028

    -

    W. Cochran (1940). The analysis of variance when experimental errors - follow the Poisson or binomial laws. - The Annals of Mathematical Statistics, 11, 335-347. - https://www.jstor.org/stable/2235680

    -

    G W Snedecor and W G Cochran, 1980. Statistical Methods, - Iowa State University Press. Page 288.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cochran.wireworms)
-dat <- cochran.wireworms
-
-libs(desplot)
-desplot(dat, worms ~ col*row,
-        text=trt, cex=1, # aspect unknown
-        main="cochran.wireworms")
-
    
-# Trt K is effective, but not the others.  Really, this says it all.
-libs(lattice)
-bwplot(worms ~ trt, dat, main="cochran.wireworms", xlab="Treatment")
-
    
-# Snedecor and Cochran do ANOVA on sqrt(x+1).
-dat <- transform(dat, rowf=factor(row), colf=factor(col))
-m1 <- aov(sqrt(worms+1) ~ rowf + colf + trt, data=dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: sqrt(worms + 1)
-#>           Df Sum Sq Mean Sq F value  Pr(>F)  
-#> rowf       4 2.3579 0.58947  1.8044 0.19285  
-#> colf       4 0.7787 0.19467  0.5959 0.67248  
-#> trt        4 5.7616 1.44041  4.4092 0.02014 *
-#> Residuals 12 3.9202 0.32668                  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# Instead of transforming, use glm
-m2 <- glm(worms ~ trt + rowf + colf, data=dat, family="poisson")
-anova(m2)
-
    #> Analysis of Deviance Table
-#> 
-#> Model: poisson, link: log
-#> 
-#> Response: worms
-#> 
-#> Terms added sequentially (first to last)
-#> 
-#> 
-#>      Df Deviance Resid. Df Resid. Dev
-#> NULL                    24     64.555
-#> trt   4  26.5294        20     38.026
-#> rowf  4  15.6955        16     22.331
-#> colf  4   2.8225        12     19.508
    
-# GLM with random blocking.
-libs(lme4)
-m3 <- glmer(worms ~ -1 +trt +(1|rowf) +(1|colf), data=dat, family="poisson")
-summary(m3)
-
    #> Generalized linear mixed model fit by maximum likelihood (Laplace
-#>   Approximation) [glmerMod]
-#>  Family: poisson  ( log )
-#> Formula: worms ~ -1 + trt + (1 | rowf) + (1 | colf)
-#>    Data: dat
-#> 
-#>      AIC      BIC   logLik deviance df.resid 
-#>    124.2    132.7    -55.1    110.2       18 
-#> 
-#> Scaled residuals: 
-#>     Min      1Q  Median      3Q     Max 
-#> -1.3342 -0.7364 -0.1740  0.4505  2.7797 
-#> 
-#> Random effects:
-#>  Groups Name        Variance  Std.Dev. 
-#>  rowf   (Intercept) 8.389e-02 0.2896419
-#>  colf   (Intercept) 1.451e-08 0.0001205
-#> Number of obs: 25, groups:  rowf, 5; colf, 5
-#> 
-#> Fixed effects:
-#>      Estimate Std. Error z value Pr(>|z|)    
-#> trtK   0.1393     0.4275   0.326    0.745    
-#> trtM   1.7814     0.2226   8.002 1.22e-15 ***
-#> trtN   1.9028     0.2142   8.881  < 2e-16 ***
-#> trtO   1.7147     0.2275   7.537 4.80e-14 ***
-#> trtP   1.4829     0.2463   6.020 1.74e-09 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Correlation of Fixed Effects:
-#>      trtK  trtM  trtN  trtO 
-#> trtM 0.185                  
-#> trtN 0.192 0.369            
-#> trtO 0.181 0.347 0.361      
-#> trtP 0.167 0.321 0.333 0.314
    ## Fixed effects:
-##      Estimate Std. Error z value Pr(>|z|)    
-## trtK   0.1393     0.4275   0.326    0.745    
-## trtM   1.7814     0.2226   8.002 1.22e-15 ***
-## trtN   1.9028     0.2142   8.881  < 2e-16 ***
-## trtO   1.7147     0.2275   7.537 4.80e-14 ***
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/connolly.potato-1.png b/docs/reference/connolly.potato-1.png deleted file mode 100644 index a4a2a9ba..00000000 Binary files a/docs/reference/connolly.potato-1.png and /dev/null differ diff --git a/docs/reference/connolly.potato-2.png b/docs/reference/connolly.potato-2.png deleted file mode 100644 index 9b5a9367..00000000 Binary files a/docs/reference/connolly.potato-2.png and /dev/null differ diff --git a/docs/reference/connolly.potato-3.png b/docs/reference/connolly.potato-3.png deleted file mode 100644 index 90703056..00000000 Binary files a/docs/reference/connolly.potato-3.png and /dev/null differ diff --git a/docs/reference/connolly.potato.html b/docs/reference/connolly.potato.html deleted file mode 100644 index ac301eda..00000000 --- a/docs/reference/connolly.potato.html +++ /dev/null @@ -1,296 +0,0 @@ - - - - - - - - -Potato yields in single-drill plots — connolly.potato • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Potato yields in single-drill plots

    - - -
    - -
    -

    Potato yields in single-drill plots

    -
    - - 
    data("connolly.potato")
    - - -

    Format

    - -

    A data frame with 80 observations on the following 6 variables.

    -
    rep

    block

    -
    gen

    variety

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, kg/ha

    -
    matur

    maturity group

    - -
    - -

    Details

    - - - -

    Connolly et el use this data to illustrate how yield can be affected - by competition from neighboring plots.

    -

    This data uses M1, M2, M3 for maturity, while Connolly et al use - FE (first early), SE (second early) and M (maincrop).

    -

    The trial was 20 sections, each of which was an independent row of 20 - drills. The data here are four reps of single-drill plots from - sections 1, 6, 11, and 16.

    -

    The neighbor covariate for a plot is defined as the average of the - plots to the left and right. For drills at the edge of the trial, the - covariate was the average of the one neighboring plot yield and the - section (i.e. rep) mean.

    -

    It would be interesting to fit a model that uses differences in - maturity between a plot and its neighbor as the actual covariate.

    -

    https://doi.org/10.1111/j.1744-7348.1993.tb04099.x

    -

    Used with permission of Iain Currie.

    -

    Source

    - -

    Connolly, T and Currie, ID and Bradshaw, JE and McNicol, JW. (1993). - Inter-plot competition in yield trials of potatoes - Solanum tuberosum L. with single-drill plots. - Annals of Applied Biology, 123, 367-377.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(connolly.potato)
-dat <- connolly.potato
-
-# Field plan
-libs(desplot)
-desplot(dat, yield~col*row,
-        out1=rep, # aspect unknown
-        main="connolly.potato yields (reps not contiguous)")
-
    
-
-# Later maturities are higher yielding
-libs(lattice)
-bwplot(yield~matur, dat, main="connolly.potato yield by maturity")
-
    
-# Observed raw means. Matches Connolly table 2.
-mn <- aggregate(yield~gen, data=dat, FUN=mean)
-mn[rev(order(mn$yield)),]
-
    #>    gen  yield
-#> 8  V08 16.200
-#> 19 V19 14.450
-#> 10 V10 13.925
-#> 12 V12 13.500
-#> 7  V07 13.300
-#> 20 V20 12.975
-#> 14 V14 12.975
-#> 6  V06 12.625
-#> 11 V11 12.575
-#> 16 V16 11.900
-#> 3  V03 11.650
-#> 9  V09 11.500
-#> 1  V01 11.275
-#> 18 V18 10.650
-#> 2  V02 10.325
-#> 17 V17 10.200
-#> 15 V15 10.125
-#> 13 V13 10.050
-#> 4  V04  9.425
-#> 5  V05  9.275
    
-# Create a covariate which is the average of neighboring plot yields
-libs(reshape2)
-mat <- acast(dat, row~col, value.var='yield')
-mat2 <- matrix(NA, nrow=4, ncol=20)
-mat2[,2:19] <- (mat[ , 1:18] + mat[ , 3:20])/2
-mat2[ , 1] <- (mat[ , 1] + apply(mat, 1, mean))/2
-mat2[ , 20] <- (mat[ , 20] + apply(mat, 1, mean))/2
-dat2 <- melt(mat2)
-colnames(dat2) <- c('row','col','cov')
-dat <- merge(dat, dat2)
-# xyplot(yield ~ cov, data=dat, type=c('p','r'))
-
-# Connolly et al fit a model with avg neighbor yield as a covariate
-m1 <- lm(yield ~ 0 + gen + rep + cov, data=dat)
-coef(m1)['cov'] # = -.303  (Connolly obtained -.31)
-
    #>        cov 
-#> -0.3030545 
    
-# Block names and effects
-bnm <- c("R1","R2","R3","R4")
-beff <- c(0, coef(m1)[c('repR2','repR3','repR4')])
-# Variety names and effects
-vnm <- paste0("V", formatC(1:20, width=2, flag='0'))
-veff <- coef(m1)[1:20]
-
-# Adjust yield for variety and block effects
-dat <- transform(dat, yadj = yield - beff[match(rep,bnm)]
-                - veff[match(gen,vnm)])
-
-# Similar to Connolly Fig 1.  Point pattern doesn't quite match
-xyplot(yadj~cov, data=dat, type=c('p','r'),
-       main="connolly.potato",
-       xlab="Avg yield of nearest neighbors",
-       ylab="Yield, adjusted for variety and block effects")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cornelius.maize-1.png b/docs/reference/cornelius.maize-1.png deleted file mode 100644 index f08e3820..00000000 Binary files a/docs/reference/cornelius.maize-1.png and /dev/null differ diff --git a/docs/reference/cornelius.maize.html b/docs/reference/cornelius.maize.html deleted file mode 100644 index 6d4cb810..00000000 --- a/docs/reference/cornelius.maize.html +++ /dev/null @@ -1,279 +0,0 @@ - - - - - - - - -Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize for 9 cultivars at 20 locations.

    - - -
    - -
    -

    Maize yields for 9 cultivars at 20 locations.

    -
    - - 
    data("cornelius.maize")
    - - -

    Format

    - -

    A data frame with 180 observations on the following 3 variables.

    -
    env

    environment factor, 20 levels

    -
    gen

    genotype/cultivar, 9 levels

    -
    yield

    yield, kg/ha

    - -
    - -

    Details

    - -

    Cell means (kg/hectare) for the CIMMYT EVT16B maize yield trial.

    -

    Source

    - -

    P L Cornelius and J Crossa and M S Seyedsadr. (1996). - Statistical Tests and Estimators of Multiplicative Models for - Genotype-by-Environment Interaction. - Book: Genotype-by-Environment Interaction. Pages 199-234.

    -

    References

    - -

    Forkman, Johannes and Piepho, Hans-Peter. (2014). - Parametric bootstrap methods for testing multiplicative terms in GGE - and AMMI models. Biometrics, 70(3), 639-647. - https://doi.org/10.1111/biom.12162

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cornelius.maize)
-dat <- cornelius.maize
-
-# dotplot(gen~yield|env,dat) # We cannot compare genotype yields easily
-# Subtract environment mean from each observation
-libs(reshape2)
-mat <- acast(dat, gen~env)
-
    #> Using yield as value column: use value.var to override.
    mat <- scale(mat, scale=FALSE)
-dat2 <- melt(mat)
-names(dat2) <- c('gen','env','yield')
-libs(lattice)
-bwplot(yield ~ gen, dat2,
-       main="cornelius.maize - environment centered yields")
-
    
-if(0){
-# This reproduces the analysis of Forkman and Piepho.
-
-test.pc <- function(Y0, type="AMMI", n.boot=10000, maxpc=6) {
-
-  # Test the significance of Principal Components in GGE/AMMI
-
-  # Singular value decomposition of centered/double-centered Y
-  Y <- sweep(Y0, 1, rowMeans(Y0)) # subtract environment means
-  if(type=="AMMI") {
-    Y <- sweep(Y, 2, colMeans(Y0)) # subtract genotype means
-    Y <- Y + mean(Y0)
-  }
-  lam <- svd(Y)$d
-
-  # Observed value of test statistic.
-  # t.obs[k] is the proportion of variance explained by the kth term out of
-  # the k...M terms, e.g. t.obs[2] is lam[2]^2 / sum(lam[2:M]^2)
-  t.obs <- { lam^2/rev(cumsum(rev(lam^2))) } [1:(M-1)]
-  t.boot <- matrix(NA, nrow=n.boot, ncol=M-1)
-
-  # Centering rows/columns reduces the rank by 1 in each direction.
-  I <- if(type=="AMMI") nrow(Y0)-1 else nrow(Y0)
-  J <- ncol(Y0)-1
-  M <- min(I, J) # rank of Y, maximum number of components
-  M <- min(M, maxpc) # Optional step: No more than 5 components
-
-  for(K in 0:(M-2)){ # 'K' multiplicative components in the svd
-
-    for(bb in 1:n.boot){
-      E.b <- matrix(rnorm((I-K) * (J-K)), nrow = I-K, ncol = J-K)
-      lam.b <- svd(E.b)$d
-      t.boot[bb, K+1] <- lam.b[1]^2 / sum(lam.b^2)
-    }
-
-  }
-
-  # P-value for each additional multiplicative term in the SVD.
-  # P-value is the proportion of time bootstrap values exceed t.obs
-  colMeans(t.boot > matrix(rep(t.obs, n.boot), nrow=n.boot, byrow=TRUE))
-}
-
-dat <- cornelius.maize
-
-# Convert to matrix format
-libs(reshape2)
-dat <- acast(dat, env~gen, value.var='yield')
-
-## R> test.pc(dat,"AMMI")
-## [1] 0.0000 0.1505 0.2659 0.0456 0.1086 # Forkman: .00 .156 .272 .046 .111
-
-## R> test.pc(dat,"GGE")
-## [1] 0.0000 0.2934 0.1513 0.0461 0.2817 # Forkman: .00 .296 .148 .047 .285
-
-}
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/correa.soybean.uniformity-1.png b/docs/reference/correa.soybean.uniformity-1.png deleted file mode 100644 index 407871d7..00000000 Binary files a/docs/reference/correa.soybean.uniformity-1.png and /dev/null differ diff --git a/docs/reference/correa.soybean.uniformity.html b/docs/reference/correa.soybean.uniformity.html deleted file mode 100644 index 03d5b4ba..00000000 --- a/docs/reference/correa.soybean.uniformity.html +++ /dev/null @@ -1,219 +0,0 @@ - - - - - - - - -Uniformity trial of soybean — correa.soybean.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of soybean

    - - -
    - -
    -

    Uniformity trial of soybean in Brazil, 1970.

    -
    - - 
    data("correa.soybean.uniformity")
    - - -

    Format

    - -

    A data frame with 1152 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, grams/plot

    - -
    - -

    Details

    - - - -

    Field length: 48 rows * .6 m = 28.8 m

    -

    Field width: 24 columns * .6 m = 14.4 m

    -

    Source

    - -

    Enedino Correa da Silva. (1974). - Estudo do tamanho e forma de parcelas para experimentos de soja (Plot size and shape for soybean yield trials). - Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, - page 52-53. - https://seer.sct.embrapa.br/index.php/pab/article/view/17250

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(correa.soybean.uniformity)
-dat <- correa.soybean.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, aspect=28.8/14.4, 
-        main="correa.soybean.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/corsten.interaction-1.png b/docs/reference/corsten.interaction-1.png deleted file mode 100644 index d4d423ca..00000000 Binary files a/docs/reference/corsten.interaction-1.png and /dev/null differ diff --git a/docs/reference/corsten.interaction.html b/docs/reference/corsten.interaction.html deleted file mode 100644 index 12be1481..00000000 --- a/docs/reference/corsten.interaction.html +++ /dev/null @@ -1,225 +0,0 @@ - - - - - - - - -Multi-environment trial of corn — corsten.interaction • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -

    Multi-environment trial of corn

    - - -
    - -
    -

    The data is the yield (kg/acre) of 20 genotypes of corn at 7 - locations.

    -
    - - - -

    Format

    - -

    A data frame with 140 observations on the following 3 variables.

    -
    gen

    genotype, 20 levels

    -
    loc

    location, 7 levels

    -
    yield

    yield, kg/acre

    - -
    - -

    Details

    - -

    The data is used by Corsten & Denis (1990) to illustrate two-way - clustering by minimizing the interaction sum of squares.

    -

    In their paper, the labels on the location dendrogram have a slight - typo. The order of the loc labels shown is 1 2 3 4 5 6 7. - The correct order of the loc labels is 1 2 4 5 6 7 3.

    -

    Used with permission of Jean-Baptiste Denis.

    -

    Source

    - -

    L C A Corsten and J B Denis, (1990). - Structuring Interaction in Two-Way Tables By Clustering. - Biometrics, 46, 207--215. Table 1. - https://doi.org/10.2307/2531644

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(corsten.interaction)
-dat <- corsten.interaction
-
-libs(reshape2)
-m1 <- melt(dat, measure.var='yield')
-dmat <- acast(m1, loc~gen)
-
-# Corsten (1990) uses this data to illustrate simultaneous row and
-# column clustering based on interaction sums-of-squares.
-# There is no (known) function in R to reproduce this analysis
-# (please contact the package maintainer if this is not true).
-# For comparison, the 'heatmap' function clusters the rows and
-# columns _independently_ of each other.
-heatmap(dmat, main="corsten.interaction")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cox.stripsplit-1.png b/docs/reference/cox.stripsplit-1.png deleted file mode 100644 index b2d991bc..00000000 Binary files a/docs/reference/cox.stripsplit-1.png and /dev/null differ diff --git a/docs/reference/cox.stripsplit.html b/docs/reference/cox.stripsplit.html deleted file mode 100644 index 09fb0b60..00000000 --- a/docs/reference/cox.stripsplit.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - - -Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Strip-split-plot of barley with fertilizer, calcium, and soil factors.

    - - -
    - -
    -

    Strip-split-plot of barley with fertilizer, calcium, and soil factors.

    -
    - - - -

    Format

    - -

    A data frame with 96 observations on the following 5 variables.

    -
    rep

    replicate, 4 levels

    -
    soil

    soil, 3 levels

    -
    fert

    fertilizer, 4 levels

    -
    calcium

    calcium, 2 levels

    -
    yield

    yield of winter barley

    - -
    - -

    Details

    - -

    Four different fertilizer treatments are laid out in vertical - strips, which are then split into subplots with different levels of - calcium. Soil type is stripped across the split-plot experiment, and the - entire experiment is then replicated three times.

    -

    Sometimes called a split-block design.

    -

    Source

    - -

    Comes from the notes of Gertrude Cox and A. Rotti.

    -

    References

    - -

    SAS/STAT(R) 9.2 User's Guide, Second Edition. - Example 23.5 Strip-Split Plot. - https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(cox.stripsplit)
-dat <- cox.stripsplit
-
-# Raw means
-# aggregate(yield ~ calcium, data=dat, mean)
-# aggregate(yield ~ soil, data=dat, mean)
-# aggregate(yield ~ calcium, data=dat, mean)
-
-libs(HH)
-
    #> Loading required package: multcomp
    #> Loading required package: mvtnorm
    #> Loading required package: TH.data
    #> 
-#> Attaching package: 'TH.data'
    #> The following object is masked from 'package:MASS':
-#> 
-#>     geyser
    #> Loading required package: gridExtra
    #> 
-#> Attaching package: 'gridExtra'
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     combine
    #> 
-#> Attaching package: 'HH'
    #> The following object is masked from 'package:vcd':
-#> 
-#>     odds
    #> The following objects are masked from 'package:car':
-#> 
-#>     logit, vif
    interaction2wt(yield ~ rep + soil + fert + calcium, dat,
-               x.between=0, y.between=0,
-               main="cox.stripsplit")
-
    
-# Traditional AOV
-m1 <- aov(yield~ fert*calcium*soil +
-          Error(rep/(fert+soil+calcium:fert+soil:fert)),
-          data=dat)
-summary(m1)
-
    #> 
-#> Error: rep
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  3   6.28   2.093               
-#> 
-#> Error: rep:fert
-#>           Df Sum Sq Mean Sq F value Pr(>F)  
-#> fert       3  7.221  2.4071   3.562 0.0604 .
-#> Residuals  9  6.082  0.6758                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:soil
-#>           Df Sum Sq Mean Sq F value Pr(>F)  
-#> soil       2  1.927  0.9633   3.466 0.0999 .
-#> Residuals  6  1.668  0.2779                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:fert:calcium
-#>              Df Sum Sq Mean Sq F value Pr(>F)  
-#> calcium       1 0.2773  0.2773   1.883 0.1950  
-#> fert:calcium  3 1.9640  0.6547   4.446 0.0255 *
-#> Residuals    12 1.7671  0.1473                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:fert:soil
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> fert:soil  6 0.6883 0.11471   1.301  0.306
-#> Residuals 18 1.5870 0.08817               
-#> 
-#> Error: Within
-#>                   Df Sum Sq Mean Sq F value Pr(>F)
-#> calcium:soil       2 0.0449 0.02247   0.245  0.784
-#> fert:calcium:soil  6 0.1894 0.03156   0.345  0.906
-#> Residuals         24 2.1962 0.09151               
    
-# With balanced data, the following are all basically identical
-
-libs(lme4)
-# The 'rep:soil:fert' term causes problems...so we drop it.
-m2 <- lmer(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) +
-             (1|rep:soil) + (1|rep:fert:calcium), data=dat)
-
-
-if(0){
-  # afex uses Kenword-Rogers approach for denominator d.f.
-  libs(afex)
-  mixed(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) +
-          (1|rep:soil) + (1|rep:fert:calcium) + (1|rep:soil:fert), data=dat,
-        control=lmerControl(check.nobs.vs.rankZ="ignore"))
-  ##              Effect      stat ndf     ddf F.scaling p.value
-  ## 1       (Intercept) 1350.8113   1  3.0009         1  0.0000
-  ## 2              fert    3.5619   3  9.0000         1  0.0604
-  ## 3              soil    3.4659   2  6.0000         1  0.0999
-  ## 4           calcium    1.8835   1 12.0000         1  0.1950
-  ## 5         fert:soil    1.2735   6 18.0000         1  0.3179
-  ## 6      fert:calcium    4.4457   3 12.0000         1  0.0255
-  ## 7      soil:calcium    0.2494   2 24.0000         1  0.7813
-  ## 8 fert:soil:calcium    0.3504   6 24.0000         1  0.9027
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cramer.cucumber.html b/docs/reference/cramer.cucumber.html deleted file mode 100644 index 784c2e47..00000000 --- a/docs/reference/cramer.cucumber.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - - - - -Cucumber yields and quantitative traits — cramer.cucumber • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Cucumber yields and quantitative traits

    - - -
    - -
    -

    Cucumber yields and quantitative traits

    -
    - - 
    data("cramer.cucumber")
    - - -

    Format

    - -

    A data frame with 24 observations on the following 9 variables.

    -
    cycle

    cycle

    -
    rep

    replicate

    -
    plants

    plants per plot

    -
    flowers

    number of pistillate flowers

    -
    branches

    number of branches

    -
    leaves

    number of leaves

    -
    totalfruit

    total fruit number

    -
    culledfruit

    culled fruit number

    -
    earlyfruit

    early fruit number

    - - - -

    Details

    - -

    The data are used to illustrate path analysis of the correlations - between phenotypic traits.

    -

    Used with permission of Christopher Cramer.

    -

    Source

    - -

    Christopher S. Cramer, Todd C. Wehner, and Sandra B. Donaghy. 1999. - Path Coefficient Analysis of Quantitative Traits. - In: Handbook of Formulas and Software for Plant Geneticists and - Breeders, page 89.

    -

    References

    - -

    Cramer, C. S., T. C. Wehner, and S. B. Donaghy. 1999. - PATHSAS: a SAS computer program for path coefficient analysis of quantitative data. - J. Hered, 90, 260-262 - https://doi.org/10.1093/jhered/90.1.260

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(cramer.cucumber)
-  dat <- cramer.cucumber
-
-  libs(lattice)
-  splom(dat[3:9], group=dat$cycle,
-        main="cramer.cucumber - traits by cycle",
-        auto.key=list(columns=3))
    #> Error in eval(substitute(groups), data, environment(formula)): object 'dat' not found
    
-
-  # derived traits
-  dat <- transform(dat,
-                   marketable = totalfruit-culledfruit,
-                   branchesperplant = branches/plants,
-                   nodesperbranch = leaves/(branches+plants),
-                   femalenodes = flowers+totalfruit)
-  dat <- transform(dat,
-                   perfenod = (femalenodes/leaves),
-                   fruitset = totalfruit/flowers,
-                   fruitperplant = totalfruit / plants,
-                   marketableperplant = marketable/plants,
-                   earlyperplant=earlyfruit/plants)
-  # just use cycle 1
-  dat1 <- subset(dat, cycle==1)
-
-  # define independent and dependent variables
-  indep <- c("branchesperplant", "nodesperbranch", "perfenod", "fruitset")
-  dep0 <- "fruitperplant"
-  dep <- c("marketable","earlyperplant")
-
-  # standardize trait data for cycle 1
-  sdat <- data.frame(scale(dat1[1:8, c(indep,dep0,dep)]))
-
-  # slopes for dep0 ~ indep
-  X <- as.matrix(sdat[,indep])
-  Y <- as.matrix(sdat[,c(dep0)])
-  # estdep <- solve(t(X) 
-  estdep <- solve(crossprod(X), crossprod(X,Y))
-  estdep
    #>                       [,1]
-#> branchesperplant 0.7160269
-#> nodesperbranch   0.3415537
-#> perfenod         0.2316693
-#> fruitset         0.2985557
      ## branchesperplant 0.7160269
-  ## nodesperbranch   0.3415537
-  ## perfenod         0.2316693
-  ## fruitset         0.2985557
-
-  # slopes for dep ~ dep0
-  X <- as.matrix(sdat[,dep0])
-  Y <- as.matrix(sdat[,c(dep)])
-  # estind2 <- solve(t(X) 
-  estind2 <- solve(crossprod(X), crossprod(X,Y))
-  estind2
    #>      marketable earlyperplant
-#> [1,]    0.97196     0.8828393
      ##  marketable earlyperplant
-  ##     0.97196     0.8828393
-
-  # correlation coefficients for indep variables
-  corrind=cor(sdat[,indep])
-  round(corrind,2)
    #>                  branchesperplant nodesperbranch perfenod fruitset
-#> branchesperplant             1.00           0.52    -0.24     0.09
-#> nodesperbranch               0.52           1.00    -0.44     0.14
-#> perfenod                    -0.24          -0.44     1.00     0.04
-#> fruitset                     0.09           0.14     0.04     1.00
      ##                  branchesperplant nodesperbranch perfenod fruitset
-  ## branchesperplant             1.00           0.52    -0.24     0.09
-  ## nodesperbranch               0.52           1.00    -0.44     0.14
-  ## perfenod                    -0.24          -0.44     1.00     0.04
-  ## fruitset                     0.09           0.14     0.04     1.00
-
-  # Correlation coefficients for dependent variables
-  corrdep=cor(sdat[,c(dep0, dep)])
-  round(corrdep,2)
    #>               fruitperplant marketable earlyperplant
-#> fruitperplant          1.00       0.97          0.88
-#> marketable             0.97       1.00          0.96
-#> earlyperplant          0.88       0.96          1.00
      ##               fruitperplant marketable earlyperplant
-  ## fruitperplant          1.00       0.97          0.88
-  ## marketable             0.97       1.00          0.96
-  ## earlyperplant          0.88       0.96          1.00
-
-  result = corrind
-  result = result*matrix(estdep,ncol=4,nrow=4,byrow=TRUE)
-  round(result,2) # match SAS output columns 1-4
    #>                  branchesperplant nodesperbranch perfenod fruitset
-#> branchesperplant             0.72           0.18    -0.06     0.03
-#> nodesperbranch               0.37           0.34    -0.10     0.04
-#> perfenod                    -0.17          -0.15     0.23     0.01
-#> fruitset                     0.07           0.05     0.01     0.30
      ##                  branchesperplant nodesperbranch perfenod fruitset
-  ## branchesperplant             0.72           0.18    -0.06     0.03
-  ## nodesperbranch               0.37           0.34    -0.10     0.04
-  ## perfenod                    -0.17          -0.15     0.23     0.01
-  ## fruitset                     0.07           0.05     0.01     0.30
-
-  resdep0 = rowSums(result)
-  resdep <- cbind(resdep0,resdep0)*matrix(estind2, nrow=4,ncol=2,byrow=TRUE)
-  colnames(resdep) <- dep
-  # slightly different from SAS output last 2 columns
-  round(cbind(fruitperplant=resdep0, round(resdep,2)),2)
    #>                  fruitperplant marketable earlyperplant
-#> branchesperplant          0.87       0.84          0.76
-#> nodesperbranch            0.65       0.63          0.58
-#> perfenod                 -0.08      -0.08         -0.07
-#> fruitset                  0.42       0.41          0.37
      ##                  fruitperplant marketable earlyperplant
-  ## branchesperplant          0.87       0.84          0.76
-  ## nodesperbranch            0.65       0.63          0.58
-  ## perfenod                 -0.08      -0.08         -0.07
-  ## fruitset                  0.42       0.41          0.37
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/crampton.pig-1.png b/docs/reference/crampton.pig-1.png deleted file mode 100644 index 670b3b86..00000000 Binary files a/docs/reference/crampton.pig-1.png and /dev/null differ diff --git a/docs/reference/crampton.pig.html b/docs/reference/crampton.pig.html deleted file mode 100644 index f369a928..00000000 --- a/docs/reference/crampton.pig.html +++ /dev/null @@ -1,205 +0,0 @@ - -Weight gain in pigs for different treatments — crampton.pig • agridat - - -
    -
    - - - -
    -
    -
    -

    Weight gain in pigs for different treatments

    - - -
    - -
    -

    Weight gain in pigs for different treatments, with initial weight and - feed eaten as covariates.

    -
    - -
    - 
    data("crampton.pig")
    -
    - -
    -

    Format

    -

    A data frame with 50 observations on the following 5 variables.

    treatment
    -

    feed treatment

    - -
    rep
    -

    replicate

    - -
    weight1
    -

    initial weight

    - -
    feed
    -

    feed eaten

    - -
    weight2
    -

    final weight

    - - -
    -
    -

    Details

    - - -

    A study of the effect of initial weight and feed eaten on the weight - gaining ability of pigs with different feed treatments.

    -

    The data are extracted from Ostle. It is not clear that 'replicate' is - actually a blocking replicate as opposed to a repeated measurement. - The original source document needs to be consulted.

    -
    -
    -

    Source

    -

    Crampton, EW and Hopkins, JW. (1934). - The Use of the Method of Partial Regression in the Analysis of Comparative Feeding Trial Data, Part II. - The Journal of Nutrition, 8, 113-123. - https://doi.org/10.1093/jn/8.3.329

    -
    -
    -

    References

    -

    Bernard Ostle. Statistics in Research, Page 458. - https://archive.org/details/secondeditionsta001000mbp

    -

    Goulden (1939). Methods of Statistical Analysis, 1st ed. Page 256-259. - https://archive.org/details/methodsofstatist031744mbp

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-
-  data(crampton.pig)
-  dat <- crampton.pig
-
-  dat <- transform(dat, gain=weight2-weight1)
-  libs(lattice)
-  # Trt 4 looks best
-  xyplot(gain ~ feed, dat, group=treatment, type=c('p','r'),
-         auto.key=list(columns=5),
-         xlab="Feed eaten", ylab="Weight gain", main="crampton.pig")
-
-  
-  # Basic Anova without covariates
-  m1 <- lm(weight2 ~ treatment + rep, data=dat)
-  anova(m1)
-#> Analysis of Variance Table
-#> 
-#> Response: weight2
-#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
-#> treatment  4  5741.7 1435.43  4.9662 0.002718 **
-#> rep        9  2487.2  276.36  0.9561 0.490957   
-#> Residuals 36 10405.5  289.04                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-  # Add covariates
-  m2 <- lm(weight2 ~ treatment + rep + weight1 + feed, data=dat)
-  anova(m2)
-#> Analysis of Variance Table
-#> 
-#> Response: weight2
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> treatment  4 5741.7  1435.4 13.9211 7.975e-07 ***
-#> rep        9 2487.2   276.4  2.6802   0.01805 *  
-#> weight1    1 2724.2  2724.2 26.4201 1.131e-05 ***
-#> feed       1 4175.5  4175.5 40.4945 2.916e-07 ***
-#> Residuals 34 3505.8   103.1                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-  # Remove treatment, test this nested model for significant treatments
-  m3 <- lm(weight2 ~ rep + weight1 + feed, data=dat)
-  anova(m2,m3) # p-value .07. F=2.34 matches Ostle
-#> Analysis of Variance Table
-#> 
-#> Model 1: weight2 ~ treatment + rep + weight1 + feed
-#> Model 2: weight2 ~ rep + weight1 + feed
-#>   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
-#> 1     34 3505.8                              
-#> 2     38 4471.7 -4   -965.88 2.3418 0.07462 .
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/crossa.wheat-1.png b/docs/reference/crossa.wheat-1.png deleted file mode 100644 index 8028b912..00000000 Binary files a/docs/reference/crossa.wheat-1.png and /dev/null differ diff --git a/docs/reference/crossa.wheat-2.png b/docs/reference/crossa.wheat-2.png deleted file mode 100644 index e7491af6..00000000 Binary files a/docs/reference/crossa.wheat-2.png and /dev/null differ diff --git a/docs/reference/crossa.wheat.html b/docs/reference/crossa.wheat.html deleted file mode 100644 index dd06d99c..00000000 --- a/docs/reference/crossa.wheat.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat for 18 genotypes at 25 locations

    - - -
    - -
    -

    Wheat yields for 18 genotypes at 25 locations

    -
    - - - -

    Format

    - -

    A data frame with 450 observations on the following 3 variables.

    -
    loc

    location

    -
    locgroup

    location group: Grp1-Grp2

    -
    gen

    genotype

    -
    gengroup

    genotype group: W1, W2, W3

    -
    yield

    grain yield, tons/ha

    - -
    - -

    Details

    - -

    Grain yield from the 8th Elite Selection Wheat Yield Trial to evaluate - 18 bread wheat genotypes at 25 locations in 15 countries.

    -

    Cross et al. used this data to cluster loctions into 2 - mega-environments and clustered genotypes into 3 wheat clusters.

    -

    Locations

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CodeCountryLocationLatitude (N)Elevation (m)
    AKAlgeriaEl Khroub36640
    ALAlgeriaSetif361,023
    BJBangladeshJoydebpur248
    CACyprusAthalassa35142
    EGEgyptE1 Gemmeiza318
    ESEgyptSakha316
    EBEgyptBeni-Suef2928
    ILIndiaLudhiana31247
    IDIndiaDelhi29228
    JMJordanMadaba36785
    KNKenyaNjoro02,165
    MGMexicoGuanajuato211,765
    MSMexicoSonora2738
    MMMexicoMichoacfin201,517
    NBNepalBhairahwa27105
    PIPakistanIslamabad34683
    PAPakistanAyub32213
    SRSaudi ArabiaRiyadh24600
    SGSudanGezira14411
    SESpainEncinar3820
    SJSpainJerez37180
    SCSpainCordoba38110
    SSSpainSevilla3820
    TBTunisiaBeja37150
    TCThailandChiang Mai18 820
    - - -

    Used with permission of Jose' Crossa.

    -

    Source

    - -

    Crossa, J and Fox, PN and Pfeiffer, WH and Rajaram, S and Gauch Jr, - HG. (1991). - AMMI adjustment for statistical analysis of an - international wheat yield trial. - Theoretical and Applied Genetics, 81, 27--37. - https://doi.org/10.1007/BF00226108

    -

    References

    - -

    Jean-Louis Laffont, Kevin Wright and Mohamed Hanafi (2013). - Genotype + Genotype x Block of Environments (GGB) Biplots. - Crop Science, 53, 2332-2341. - https://doi.org/10.2135/cropsci2013.03.0178

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(crossa.wheat)
-  dat <- crossa.wheat
-  
-  # AMMI biplot.  Fig 3 of Crossa et al.
-  libs(agricolae)
-  m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield))
-  b1 <- m1$biplot[,1:4]
-  b1$PC1 <- -1 * b1$PC1 # Flip vertical
-  plot(b1$yield, b1$PC1, cex=0.0,
-       text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col="brown"),
-       main="crossa.wheat AMMI biplot",
-       xlab="Average yield", ylab="PC1", frame=TRUE)
-
      mn <- mean(b1$yield)
-  abline(h=0, v=mn, col='wheat')
-
    
-  g1 <- subset(b1,type=="GEN")
-  text(g1$yield, g1$PC1, rownames(g1), col="darkgreen", cex=.5)
-
      
-  e1 <- subset(b1,type=="ENV")
-  arrows(mn, 0,
-         0.95*(e1$yield - mn) + mn, 0.95*e1$PC1,
-         col= "brown", lwd=1.8,length=0.1)
-
      
-  # GGB example
-  library(agridat)
-  data(crossa.wheat)
-  dat2 <- crossa.wheat
-  libs(gge)
-  # Specify env.group as column in data frame
-  m2 <- gge(dat2, yield~gen*loc,
-            env.group=locgroup, gen.group=gengroup,
-            scale=FALSE)
-  biplot(m2, main="crossa.wheat - GGB biplot")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/crowder.seeds-1.png b/docs/reference/crowder.seeds-1.png deleted file mode 100644 index 39dd5c81..00000000 Binary files a/docs/reference/crowder.seeds-1.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-2.png b/docs/reference/crowder.seeds-2.png deleted file mode 100644 index 5f6524ba..00000000 Binary files a/docs/reference/crowder.seeds-2.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-3.png b/docs/reference/crowder.seeds-3.png deleted file mode 100644 index 6e5ed1a5..00000000 Binary files a/docs/reference/crowder.seeds-3.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-4.png b/docs/reference/crowder.seeds-4.png deleted file mode 100644 index bcd230d7..00000000 Binary files a/docs/reference/crowder.seeds-4.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-5.png b/docs/reference/crowder.seeds-5.png deleted file mode 100644 index 2296e052..00000000 Binary files a/docs/reference/crowder.seeds-5.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-6.png b/docs/reference/crowder.seeds-6.png deleted file mode 100644 index 83c85a6d..00000000 Binary files a/docs/reference/crowder.seeds-6.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-7.png b/docs/reference/crowder.seeds-7.png deleted file mode 100644 index 5f3e2c54..00000000 Binary files a/docs/reference/crowder.seeds-7.png and /dev/null differ diff --git a/docs/reference/crowder.seeds-8.png b/docs/reference/crowder.seeds-8.png deleted file mode 100644 index 6a1b0e67..00000000 Binary files a/docs/reference/crowder.seeds-8.png and /dev/null differ diff --git a/docs/reference/crowder.seeds.html b/docs/reference/crowder.seeds.html deleted file mode 100644 index a6af3de8..00000000 --- a/docs/reference/crowder.seeds.html +++ /dev/null @@ -1,159 +0,0 @@ - -Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds • agridat - - -
    -
    - - - -
    -
    -
    -

    Germination of Orobanche seeds for two genotypes and two treatments.

    - - -
    - -
    -

    Number of Orobanche seeds tested/germinated for two genotypes and - two treatments.

    -
    - - -
    -

    Format

    -

    plate
    -

    Factor for replication

    - -
    gen
    -

    Factor for genotype with levels O73, O75

    - -
    extract
    -

    Factor for extract from bean, cucumber

    - -
    germ
    -

    Number of seeds that germinated

    - -
    n
    -

    Total number of seeds tested

    - - -
    -
    -

    Details

    - - -

    Egyptian broomrape, orobanche aegyptiaca is a parasitic plant - family. The plants have no chlorophyll and grow on the roots of other - plants. The seeds remain dormant in soil until certain compounds from - living plants stimulate germination.

    -

    Two genotypes were studied in the experiment, O. aegyptiaca 73 - and O. aegyptiaca 75. The seeds were brushed with one of two - extracts prepared from either a bean plant or cucmber plant.

    -

    The experimental design was a 2x2 factorial, each with 5 or 6 reps of - plates.

    -
    -
    -

    Source

    - - -

    Crowder, M.J., 1978. - Beta-binomial anova for proportions. - Appl. Statist., 27, 34-37. - https://doi.org/10.2307/2346223

    -
    -
    -

    References

    - - -

    N. E. Breslow and D. G. Clayton. 1993. - Approximate inference in generalized linear mixed models. - Journal of the American Statistical Association, 88:9-25. - https://doi.org/10.2307/2290687

    -

    Y. Lee and J. A. Nelder. 1996. - Hierarchical generalized linear models with discussion. - J. R. Statist. Soc. B, 58:619-678.

    -
    - -
    -

    Examples

    - -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/cullis.earlygen-1.png b/docs/reference/cullis.earlygen-1.png deleted file mode 100644 index 3f6d00d9..00000000 Binary files a/docs/reference/cullis.earlygen-1.png and /dev/null differ diff --git a/docs/reference/cullis.earlygen-2.png b/docs/reference/cullis.earlygen-2.png deleted file mode 100644 index ec90dd16..00000000 Binary files a/docs/reference/cullis.earlygen-2.png and /dev/null differ diff --git a/docs/reference/cullis.earlygen-3.png b/docs/reference/cullis.earlygen-3.png deleted file mode 100644 index 16c5f985..00000000 Binary files a/docs/reference/cullis.earlygen-3.png and /dev/null differ diff --git a/docs/reference/cullis.earlygen.html b/docs/reference/cullis.earlygen.html deleted file mode 100644 index ebcebc49..00000000 --- a/docs/reference/cullis.earlygen.html +++ /dev/null @@ -1,341 +0,0 @@ - - - - - - - - -Early generation variety trial in wheat — cullis.earlygen • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Early generation variety trial in wheat

    - - -
    - -
    -

    Early generation variety trial in wheat

    -
    - - - -

    Format

    - -

    A data frame with 670 observations on the following 5 variables.

    -
    gen

    genotype factor

    -
    row

    row

    -
    col

    column

    -
    entry

    entry (genotype) number

    -
    yield

    yield of each plot, kg/ha

    -
    weed

    weed score

    - -
    - -

    Details

    - -

    The data are from an unreplicated field - experiment conducted at Tullibigeal, New South Wales, Australia in - 1987-88. In each row, every 6th plot was the variety 'Kite'. Six - other standard varieties were randomly interspersed over the trial. - Each plot was 15m x 1.8m, "oriented with the longest side with rows".

    -

    The 'weed' variable is a visual score on a 0 to 10 scale, - 0 = no weeds, 10 = 100 percent weeds.

    -

    The replicated check variety was numbered 526. A further 6 replicated - commercially available varieties (numbered 527 to 532) were also - randomly assigned to plots with between 3 to 5 plots of each. The aim - of these trials is to identify and retain the top, say 20 percent of - lines for further testing. Cullis et al. (1989) presented an analysis - of early generation variety trials that included a one-dimensional - spatial analysis. Below, a two-dimensional spatial analysis is - presented.

    -

    Note: The 'row' and 'col' variables are as in the VSN link below (switched - compared to the paper by Cullis et al.)

    -

    Field width: 10 rows * 15 m = 150 m

    -

    Field length: 67 plots * 1.8 m = 121 m

    -

    The orientation is not certain, but the alternative orientation would - have a field roughly 20m x 1000m, which seems unlikely.

    -

    Source

    - -

    Brian R. Cullis, Warwick J. Lill, John A. Fisher, Barbara J. Read and - Alan C. Gleeson (1989). - A New Procedure for the Analysis of Early Generation Variety Trials. - Journal of the Royal Statistical Society. Series C (Applied - Statistics), 38, 361-375. - https://doi.org/10.2307/2348066

    -

    References

    - -

    Unreplicated early generation variety trial in Wheat. - https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(cullis.earlygen)
-  dat <- cullis.earlygen
-
-  # Show field layout of checks.  Cullis Table 1.
-  dat$check <- ifelse(dat$entry < 8, dat$entry, NA)
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          col="check", cex=0.5, flip=TRUE, aspect=121/150, # true aspect
-          main="cullis.earlygen (yield)")
-
    
-  grays <- colorRampPalette(c("white","#252525"))
-  desplot(dat, weed ~ col*row,
-          at=0:6-0.5, col.regions=grays(7)[-1],
-          flip=TRUE, aspect=121/150, # true aspect
-          main="cullis.earlygen (weed)")
-
    
-  libs(lattice)
-  bwplot(yield ~ as.character(weed), dat,
-         horizontal=FALSE,
-         xlab="Weed score", main="cullis.earlygen")
-
    
-  # Moving Grid
-  libs(mvngGrAd)
-  shape <- list(c(1),
-                c(1),
-                c(1:4),
-                c(1:4))
-  # sketchGrid(10,10,20,20,shapeCross=shape, layers=1, excludeCenter=TRUE)
-  m0 <- movingGrid(rows=dat$row, columns=dat$col, obs=dat$yield,
-                   shapeCross=shape, layers=NULL)
-  dat$mov.avg <- fitted(m0)
-
-  libs(asreml) # asreml4
-
-  # Start with the standard AR1xAR1 analysis
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf, dat$yf),]
-  m2 <- asreml(yield ~ weed, data=dat, random= ~gen,
-               resid = ~ ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:12 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -4277.990      128496.8    666 17:08:12    0.0
-#>  2     -4266.138      120973.9    666 17:08:12    0.0
-#>  3     -4253.062      107781.1    666 17:08:12    0.0
-#>  4     -4241.333       87649.8    666 17:08:12    0.0
-#>  5     -4235.061       79226.3    666 17:08:12    0.0
-#>  6     -4233.865       81681.5    666 17:08:12    0.0
-#>  7     -4233.681       82608.2    666 17:08:12    0.0
-#>  8     -4233.652       82933.6    666 17:08:12    0.0
-#>  9     -4233.648       83050.0    666 17:08:12    0.0
-#> 10     -4233.647       83094.2    666 17:08:12    0.0
      
-  # Variogram suggests a polynomial trend
-  m3 <- update(m2, fixed= yield~weed+pol(col,-1))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:12 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -4225.800      82257.80    665 17:08:12    0.0
-#>  2     -4225.712      80790.06    665 17:08:12    0.0
-#>  3     -4225.633      77826.53    665 17:08:12    0.0
-#>  4     -4225.631      77839.46    665 17:08:12    0.0
      
-  # Now add a nugget variance
-  m4 <- update(m3, random= ~ gen + units)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:12 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -4224.105      75223.42    665 17:08:12    0.0
-#>  2     -4222.648      68685.54    665 17:08:12    0.0
-#>  3     -4221.139      61207.50    665 17:08:12    0.0
-#>  4     -4220.317      55446.36    665 17:08:12    0.0
-#>  5     -4220.264      54939.35    665 17:08:12    0.0
-#>  6     -4220.261      54773.48    665 17:08:12    0.0
      
-  libs(lucid)
-  vc(m4)
-
    #>        effect component std.error z.ratio bound %ch
-#>           gen  73850    10410         7.1     P 0  
-#>         units  30450     8064         3.8     P 0.4
-#>       xf:yf!R  54770    10630         5.2     P 0  
-#>  xf:yf!xf!cor      0.38     0.115     3.3     U 0  
-#>  xf:yf!yf!cor      0.84     0.045    19       U 0.1
      ##       effect component std.error z.ratio bound 
-  ##          gen  73780    10420         7.1     P 0  
-  ##        units  30440     8073         3.8     P 0.1
-  ##     xf:yf(R)  54730    10630         5.1     P 0  
-  ## xf:yf!xf!cor      0.38     0.115     3.3     U 0  
-  ## xf:yf!yf!cor      0.84     0.045    19       U 0  
-  
-  ## # Predictions from models m3 and m4 are non-estimable.  Why?
-  ## # Use model m2 for predictions
-  ## predict(m2, classify="gen")$pvals
-  ## ##         gen predicted.value std.error    status
-  ## ## 1     Banks        2723.534  93.14719 Estimable
-  ## ## 2    Eno008        2981.056 162.85241 Estimable
-  ## ## 3    Eno009        2978.008 161.57129 Estimable
-  ## ## 4    Eno010        2821.399 153.96943 Estimable
-  ## ## 5    Eno011        2991.612 161.53507 Estimable
-  
-
-  ## # Compare AR1 with Moving Grid
-  ## dat$ar1 <- fitted(m2)
-  ## head(dat[ , c('yield','ar1','mov.avg')])
-  ## ##    yield      ar1       mg
-  ## ## 1   2652 2467.980 2531.998
-  ## ## 11  3394 3071.681 3052.160
-  ## ## 21  3148 2826.188 2807.031
-  ## ## 31  3426 3026.985 3183.649
-  ## ## 41  3555 3070.102 3195.910
-  ## ## 51  3453 3006.352 3510.511
-  ## pairs(dat[ , c('yield','ar1','mg')])
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/damesa.maize-1.png b/docs/reference/damesa.maize-1.png deleted file mode 100644 index 4fffaddb..00000000 Binary files a/docs/reference/damesa.maize-1.png and /dev/null differ diff --git a/docs/reference/damesa.maize.html b/docs/reference/damesa.maize.html deleted file mode 100644 index b27a7a28..00000000 --- a/docs/reference/damesa.maize.html +++ /dev/null @@ -1,187 +0,0 @@ - -Incomplete-block experiment of maize in Ethiopia. — damesa.maize • agridat - - -
    -
    - - - -
    -
    -
    -

    Incomplete-block experiment of maize in Ethiopia.

    - - -
    - -
    -

    Incomplete-block experiment of maize in Ethiopia.

    -
    - -
    - 
    data("damesa.maize")
    -
    - -
    -

    Format

    -

    A data frame with 264 observations on the following 8 variables.

    site
    -

    site, 4 levels

    - -
    rep
    -

    replicate, 3 levels

    - -
    block
    -

    incomplete block

    - -
    plot
    -

    plot number

    - -
    gen
    -

    genotype, 22 levels

    - -
    row
    -

    row ordinate

    - -
    col
    -

    column ordinate

    - -
    yield
    -

    yield, t/ha

    - - -
    -
    -

    Details

    -

    An experiment harvested in 2012, evaluating drought-tolerant maize - hybrids at 4 sites in Ethiopia. - At each site, an incomplete-block design was used.

    -

    Damesa et al use this data to compare single-stage and two-stage - analyses.

    -
    -
    -

    Source

    -

    Tigist Mideksa Damesa, Jens MĂśhring, Mosisa Worku, Hans-Peter Piepho (2017). - One Step at a Time: Stage-Wise Analysis of a Series of Experiments. - Agronomy J, 109, 845-857. - https://doi.org/10.2134/agronj2016.07.0395

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  libs(desplot)
-  desplot(damesa.maize,
-          yield ~ col*row|site,
-          main="damesa.maize",
-          out1=rep, out2=block, num=gen, cex=1)
-
-
-  if(0){
-    # Fit the single-stage model in Damesa
-    lib(asreml)
-    m0 <- asreml(data=damesa.maize,
-             fixed = yield ~ gen,
-             random = ~ site + gen:site + at(site):rep/block,
-             residual = ~ dsum( ~ units|site) )
-    lucid::vc(m0) # match Damesa table 1 column 3
-    ##                 effect component std.error z.ratio bound 
-    ##       at(site, S1):rep   0.08819   0.1814     0.49     P 0  
-    ##       at(site, S2):rep   1.383     1.426      0.97     P 0  
-    ##       at(site, S3):rep   0              NA      NA     B 0  
-    ##       at(site, S4):rep   0.01442   0.02602    0.55     P 0  
-    ##                   site  10.45      8.604      1.2      P 0.1
-    ##               gen:site   0.1054    0.05905    1.8      P 0.1
-    ## at(site, S1):rep:block   0.3312    0.3341     0.99     P 0  
-    ## at(site, S2):rep:block   0.4747    0.1633     2.9      P 0  
-    ## at(site, S3):rep:block   0              NA      NA     B 0  
-    ## at(site, S4):rep:block   0.06954   0.04264    1.6      P 0  
-    ##              site_S1!R   1.346     0.3768     3.6      P 0  
-    ##              site_S2!R   0.1936    0.06628    2.9      P 0  
-    ##              site_S3!R   1.153     0.2349     4.9      P 0  
-    ##              site_S4!R   0.1112    0.03665    3        P 0  
-  }
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/darwin.maize-1.png b/docs/reference/darwin.maize-1.png deleted file mode 100644 index 2a0cc40a..00000000 Binary files a/docs/reference/darwin.maize-1.png and /dev/null differ diff --git a/docs/reference/darwin.maize.html b/docs/reference/darwin.maize.html deleted file mode 100644 index 55f7fbb0..00000000 --- a/docs/reference/darwin.maize.html +++ /dev/null @@ -1,265 +0,0 @@ - - - - - - - - -Darwin's maize data of crossed/inbred plant heights — darwin.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Darwin's maize data of crossed/inbred plant heights

    - - -
    - -
    -

    Darwin's maize data of crossed/inbred plant heights.

    -
    - - - -

    Format

    - -

    A data frame with 30 observations on the following 4 variables.

    -
    pot

    Pot factor, 4 levels

    -
    pair

    Pair factor, 12 levels

    -
    type

    Type factor, self-pollinated, cross-pollinated

    -
    height

    Height, in inches (measured to 1/8 inch)

    - -
    - -

    Details

    - -

    Charles Darwin, in 1876, reported data from an experiment that he had - conducted on the heights of corn plants. The - seeds came from the same parents, but some seeds were produced from - self-fertilized parents and some seeds were produced from - cross-fertilized parents. Pairs of seeds were planted in pots. - Darwin hypothesized that cross-fertilization produced produced more - robust and vigorous offspring.

    -

    Darwin wrote, "I long doubted whether it was worth while to give the - measurements of each separate plant, but have decided to do so, in - order that it may be seen that the superiority of the crossed plants - over the self-fertilised, does not commonly depend on the presence of - two or three extra fine plants on the one side, or of a few very poor - plants on the other side. Although several observers have insisted in - general terms on the offspring from intercrossed varieties being - superior to either parent-form, no precise measurements have been - given;* and I have met with no observations on the effects of crossing - and self-fertilising the individuals of the same variety. Moreover, - experiments of this kind require so much time--mine having been - continued during eleven years--that they are not likely soon to be - repeated."

    -

    Darwin asked his cousin Francis Galton for help in understanding the - data. Galton did not have modern statistical methods to approach - the problem and said, "I doubt, after making many tests, whether it is - possible to derive useful conclusions from these few observations. We - ought to have at least 50 plants in each case, in order to be in a - position to deduce fair results".

    -

    Later, R. A. Fisher used Darwin's data in a book about design of - experiments and showed that a t-test exhibits a significant difference - between the two groups.

    -

    Source

    - -

    Darwin, C. R. 1876. The effects of cross and self fertilisation in the - vegetable kingdom. London: John Murray. Page 16. - https://darwin-online.org.uk/converted/published/1881_Worms_F1357/1876_CrossandSelfFertilisation_F1249/1876_CrossandSelfFertilisation_F1249.html

    -

    References

    - -

    R. A. Fisher, (1935) The Design of Experiments, Oliver and Boyd. Page 30.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(darwin.maize)
-dat <- darwin.maize
-
-# Compare self-pollination with cross-pollination
-libs(lattice)
-bwplot(height~type, dat, main="darwin.maize")
-
    
-# \dontrun{
-  libs(reshape2)
-  dm <- melt(dat)
-
    #> Using pot, pair, type as id variables
      d2 <- dcast(dm, pot+pair~type)
-  d2$diff <- d2$cross-d2$self
-  t.test(d2$diff)
-
    #> 
-#> 	One Sample t-test
-#> 
-#> data:  d2$diff
-#> t = 2.148, df = 14, p-value = 0.0497
-#> alternative hypothesis: true mean is not equal to 0
-#> 95 percent confidence interval:
-#>  0.003899165 5.229434169
-#> sample estimates:
-#> mean of x 
-#>  2.616667 
-#> 
      ##   One Sample t-test
-  ## t = 2.148, df = 14, p-value = 0.0497
-  ## alternative hypothesis: true mean is not equal to 0
-  ## 95 percent confidence interval:
-  ##  0.003899165 5.229434169
-# }
-
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/dasilva.maize.html b/docs/reference/dasilva.maize.html deleted file mode 100644 index 819a2a16..00000000 --- a/docs/reference/dasilva.maize.html +++ /dev/null @@ -1,256 +0,0 @@ - - - - - - - - -Multi-environment trial of maize — dasilva.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize

    - - -
    - -
    -

    Multi-environment trial of maize with 3 reps.

    -
    - - 
    data("dasilva.maize")
    - - -

    Format

    - -

    A data frame with 1485 observations on the following 4 variables.

    -
    env

    environment

    -
    rep

    replicate block, 3 per env

    -
    gen

    genotype

    -
    yield

    yield (tons/hectare)

    - -
    - -

    Details

    - -

    Each location had 3 blocks. Block numbers are unique across - environments.

    -

    NOTE! The environment codes in the supplemental data file of da Silva - 2015 do not quite match the environment codes of the paper, but are - mostly off by 1.

    -

    DaSilva Table 1 has a footnote "Machado et al 2007". This reference - appears to be:

    -

    Machado et al. - Estabilidade de producao de hibridos simples e duplos de milhooriundos - de um mesmo conjunto genico. - Bragantia, 67, no 3. www.scielo.br/pdf/brag/v67n3/a10v67n3.pdf

    -

    In DaSilva Table 1, the mean of E1 is 10.803. This appears to be a - copy of the mean from row 1 of Table 1 in Machado. Using the - supplemental data from this paper, the correct mean is 8.685448.

    -

    Source

    - -

    A Bayesian Shrinkage Approach for AMMI Models. - Carlos Pereira da Silva, Luciano Antonio de Oliveira, Joel Jorge - Nuvunga, Andrezza Kellen Alves Pamplona, Marcio Balestre. - Plos One. Supplemental material. - https://doi.org/10.1371/journal.pone.0131414

    -

    Used via Creative Commons Attribution License.

    -

    References

    - -

    J.J. Nuvunga, L.A. Oliveira, A.K.A. Pamplona, C.P. Silva, R.R. Lima - and M. Balestre. - Factor analysis using mixed models of multi-environment trials with - different levels of unbalancing. - Genet. Mol. Res. 14.

    - -

    Examples

    - 
    
-library(agridat)
-data(dasilva.maize)
-dat <- dasilva.maize
-
-# Try to match Table 1 of da Silva 2015.
-# aggregate(yield ~ env, data=dat, FUN=mean)
-##   env     yield
-## 1  E1  6.211817  # match E2 in Table 1
-## 2  E2  4.549104  # E3
-## 3  E3  5.152254  # E4
-## 4  E4  6.245904  # E5
-## 5  E5  8.084609  # E6
-## 6  E6 13.191890  # E7
-## 7  E7  8.895721  # E8
-## 8  E8  8.685448  
-## 9  E9  8.737089  # E9
-
-# Unable to match CVs in Table 2, but who knows what they used
-# for residual variance.
-# aggregate(yield ~ env, data=dat, FUN=function(x) 100*sd(x)/mean(x))
-
-# Match DaSilva supplement 2, ANOVA
-# m1 <- aov(yield ~ env + gen + rep:env + gen:env, dat)
-# anova(m1)
-## Response: yield
-##            Df Sum Sq Mean Sq  F value    Pr(>F)    
-## env         8 8994.2 1124.28 964.1083 < 2.2e-16 ***
-## gen        54  593.5   10.99   9.4247 < 2.2e-16 ***
-## env:rep    18   57.5    3.19   2.7390 0.0001274 ***
-## env:gen   432  938.1    2.17   1.8622 1.825e-15 ***
-## Residuals 972 1133.5    1.17                       
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/dasilva.soybean.uniformity-1.png b/docs/reference/dasilva.soybean.uniformity-1.png deleted file mode 100644 index 5435f128..00000000 Binary files a/docs/reference/dasilva.soybean.uniformity-1.png and /dev/null differ diff --git a/docs/reference/dasilva.soybean.uniformity.html b/docs/reference/dasilva.soybean.uniformity.html deleted file mode 100644 index 6eb420ad..00000000 --- a/docs/reference/dasilva.soybean.uniformity.html +++ /dev/null @@ -1,151 +0,0 @@ - -Uniformity trial of soybean — dasilva.soybean.uniformity • agridat - - -
    -
    - - - -
    -
    -
    -

    Uniformity trial of soybean

    - - -
    - -
    -

    Uniformity trial of soybean in Brazil, 1970.

    -
    - -
    - 
    data("dasilva.soybean.uniformity")
    -
    - -
    -

    Format

    -

    A data frame with 1152 observations on the following 3 variables.

    row
    -

    row

    - -
    col
    -

    column

    - -
    yield
    -

    yield, grams/plot

    - - -
    -
    -

    Details

    - - -

    Field length: 48 rows * .6 m = 28.8 m

    -

    Field width: 24 columns * .6 m = 14.4 m

    -
    -
    -

    Source

    -

    Enedino Correa da Silva. (1974). - Estudo do tamanho e forma de parcelas para experimentos de soja (Plot size and shape for soybean yield trials). - Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, - page 52-53. - https://seer.sct.embrapa.br/index.php/pab/article/view/17250

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(dasilva.soybean.uniformity)
-dat <- dasilva.soybean.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, aspect=28.8/14.4, 
-        main="dasilva.soybean.uniformity")
-
-  
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/davidian.soybean-1.png b/docs/reference/davidian.soybean-1.png deleted file mode 100644 index b95aefdd..00000000 Binary files a/docs/reference/davidian.soybean-1.png and /dev/null differ diff --git a/docs/reference/davidian.soybean.html b/docs/reference/davidian.soybean.html deleted file mode 100644 index 87eb900d..00000000 --- a/docs/reference/davidian.soybean.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - - - -Growth of soybean varieties in 3 years — davidian.soybean • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Growth of soybean varieties in 3 years

    - - -
    - -
    -

    Growth of soybean varieties in 3 years

    -
    - - 
    data("davidian.soybean")
    - - -

    Format

    - -

    A data frame with 412 observations on the following 5 variables.

    -
    plot

    plot code

    -
    variety

    variety, F or P

    -
    year

    1988-1990

    -
    day

    days after planting

    -
    weight

    weight of soybean leaves

    - - - -

    Details

    - -

    This experiment compared the growth patterns of two genotypes of soybean - varieties: F=Forrest (commercial variety) and P=Plant Introduction - number 416937 (experimental variety).

    -

    Data were collected in 3 consecutive years.

    -

    At the start of each growing season, 16 plots were seeded (8 for each - variety). Data were collected approximately weekly. At each timepoint, - six plants were randomly selected from each plot. The leaves from these - 6 plants were weighed, and average leaf weight per plant was - reported. (We assume that the data collection is destructive and - different plants are sampled at each date).

    -

    Note: this data is the same as the "nlme::Soybean" data.

    -

    Source

    - -

    Marie Davidian and D. M. Giltinan, (1995). - Nonlinear Models for Repeated Measurement Data. - Chapman and Hall, London.

    -

    Electronic version retrieved from - https://www4.stat.ncsu.edu/~davidian/data/soybean.dat

    -

    References

    - -

    Pinheiro, J. C. and Bates, D. M. (2000). - Mixed-Effects Models in S and S-PLUS. - Springer, New York.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(davidian.soybean)
-  dat <- davidian.soybean
-  dat$year <- factor(dat$year)
-
-  libs(lattice)
-  xyplot(weight ~ day|variety*year, dat,
-         group=plot, type='l',
-         main="davidian.soybean")
    
-
-  # The only way to keep your sanity with nlme is to use groupedData objects
-  # Well, maybe not.  When I use "devtools::run_examples",
-  # the "groupedData" function creates a dataframe with/within(?) an
-  # environment, and then "nlsList" cannot find datg, even though
-  # ls() shows datg is visible and head(datg) is fine.
-  # Also works fine in interactive mode. It is driving me insane.
-  # reid.grasses has the same problem
-  # Use if(0){} to block this code from running.
-  if(0){
-    libs(nlme)
-    datg <- groupedData(weight ~ day|plot, dat)
-    # separate fixed-effect model for each plot
-    # 1988P6 gives unusual estimates
-    m1 <- nlsList(SSlogis, data=datg,
-                  subset = plot != "1988P6")
-    # plot(m1) # seems heterogeneous
-    plot(intervals(m1), layout=c(3,1)) # clear year,variety effects in Asym
-
-    # A = maximum, B = time of half A = steepness of curve
-    # C = sharpness of curve (smaller = sharper curve)
-
-    # switch to mixed effects
-    m2 <- nlme(weight ~ A / (1+exp(-(day-B)/C)),
-               data=datg,
-               fixed=list(A ~ 1, B ~ 1, C ~ 1),
-               random = A +B +C ~ 1,
-               start=list(fixed = c(17,52,7.5))) # no list!
-
-    # add covariates for A,B,C effects, correlation, weights
-    # not necessarily best model, but it shows the syntax
-    m3 <- nlme(weight ~ A / (1+exp(-(day-B)/C)),
-               data=datg,
-               fixed=list(A ~ variety + year,
-                          B ~ year,
-                        C ~ year),
-               random = A +B +C ~ 1,
-               start=list(fixed= c(19,0,0,0,
-                                   55,0,0,
-                                   8,0,0)),
-               correlation = corAR1(form = ~ 1|plot),
-               weights=varPower(), # really helps
-               control=list(mxMaxIter=200))
-
-    plot(augPred(m3), layout=c(8,6),
-      main="davidian.soybean - model 3")
-  } # end if(0)
-# }
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/davies.pasture.uniformity-1.png b/docs/reference/davies.pasture.uniformity-1.png deleted file mode 100644 index e01fa07a..00000000 Binary files a/docs/reference/davies.pasture.uniformity-1.png and /dev/null differ diff --git a/docs/reference/davies.pasture.uniformity.html b/docs/reference/davies.pasture.uniformity.html deleted file mode 100644 index fc9ddf3b..00000000 --- a/docs/reference/davies.pasture.uniformity.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - -Uniformity trial of pasture. — davies.pasture.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of pasture.

    - - -
    - -
    -

    Uniformity trial of pasture in Australia.

    -
    - - 
    data("davies.pasture.uniformity")
    - - -

    Format

    - -

    A data frame with 760 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per plot, grams

    - - - -

    Details

    - -

    Conducted at the Waite Agricultural Research Institute in 1928. A rectangle 250 x 200 links was selected, divided into 1000 plots measuring 10 x 5 links, that is 1/2000th acre. - Plots were hand harvested for herbage and air-dried. Cutting began Tue, 25 Sep and ended Sat, 29 Sep, by which time 760 plots had been harvested. Rain fell, harvesting ceased.

    -

    The minimum recommended plot size is 150 square links. The optimum recommended plot size is 450 square links, 5 x 90 links in size.

    -

    Note, there were 4 digits that were hard to read in the original document. Best estimates of these digits were used for the yields of the affects plots. The yields were watermarked with an extra .01 added.

    -

    The botanical composition of species clearly influenced the total herbage.

    -

    Field length: 40 plots * 5 links = 200 links

    -

    Field width: 19 plots * 10 links = 190 links

    -

    Source

    - -

    J. Griffiths Davies (1931). - The Experimental Error of the Yield from Small Plots of Natural Pasture. - Council for Scientific and Industrial Research (Aust.) Bulletin 48. - Table 1.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(davies.pasture.uniformity)
-  dat <- davies.pasture.uniformity
-
-  # range(dat$yield) # match Davies
-  # mean(dat$yield) # 227.77, Davies has 221.7
-  # sd(dat$yield)/mean(dat$yield) # 33.9, Davies has 32.5
-
-  # libs(lattice)
-  # qqmath( ~ yield, dat) # clearly non-normal, skewed right
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=(40*5)/(19*10), # true aspect
-          main="davies.pasture.uniformity")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/day.wheat.uniformity-1.png b/docs/reference/day.wheat.uniformity-1.png deleted file mode 100644 index 7b3b1195..00000000 Binary files a/docs/reference/day.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/day.wheat.uniformity-2.png b/docs/reference/day.wheat.uniformity-2.png deleted file mode 100644 index 4fdf343c..00000000 Binary files a/docs/reference/day.wheat.uniformity-2.png and /dev/null differ diff --git a/docs/reference/day.wheat.uniformity-3.png b/docs/reference/day.wheat.uniformity-3.png deleted file mode 100644 index 9b0062c4..00000000 Binary files a/docs/reference/day.wheat.uniformity-3.png and /dev/null differ diff --git a/docs/reference/day.wheat.uniformity-4.png b/docs/reference/day.wheat.uniformity-4.png deleted file mode 100644 index 855a6fb6..00000000 Binary files a/docs/reference/day.wheat.uniformity-4.png and /dev/null differ diff --git a/docs/reference/day.wheat.uniformity-5.png b/docs/reference/day.wheat.uniformity-5.png deleted file mode 100644 index c468add6..00000000 Binary files a/docs/reference/day.wheat.uniformity-5.png and /dev/null differ diff --git a/docs/reference/day.wheat.uniformity.html b/docs/reference/day.wheat.uniformity.html deleted file mode 100644 index dbf4e5ec..00000000 --- a/docs/reference/day.wheat.uniformity.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — day.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat in 1903 in Missouri.

    -
    - - 
    data("day.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 3090 observations on the following 4 variables.

    -
    row

    row

    -
    col

    col

    -
    grain

    grain weight, grams

    -
    straw

    straw weight, grams

    - -
    - -

    Details

    - - - -

    These data are from the Shelbina field of the Missouri Agricultural - Experiment Station. The field (plat) was about 1/4 acre in area and - apparently uniform throughout. In the fall of 1912, wheat was drilled - in rows 8 inches apart, each row 155 feet long. The wheat was - harvested in June, in 5-foot segments. The gross weight and the grain - weight was measured, the straw weight was calculated by subtraction.

    -

    Field width: 31 series * 5 feet = 155 feet

    -

    Field length: 100 rows, 8 inches apart = 66.66 feet

    -

    Source

    - -

    James Westbay Day (1916). - The relation of size, shape, and number of replications of plats to - probable error in field experimentation. - Dissertation, University of Missouri. Table 1, page 22. - https://hdl.handle.net/10355/56391

    -

    References

    - -

    James W. Day (1920). - The relation of size, shape, and number of replications of plats to - probable error in field experimentation. - Agronomy Journal, 12, 100-105. - https://doi.org/10.2134/agronj1920.00021962001200030002x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(day.wheat.uniformity)
-dat <- day.wheat.uniformity
-
-libs(desplot)
-desplot(dat, grain~col*row,
-        flip=TRUE, aspect=(100*8)/(155*12), # true aspect
-        main="day.wheat.uniformity - grain yield")
-
      
-# similar to Day table IV
-libs(lattice)
-xyplot(grain~straw, data=dat, main="day.wheat.uniformity", type=c('p','r'))
-
    # cor(dat$grain, dat$straw) # .9498 # Day calculated 0.9416
-  
-libs(desplot)
-desplot(dat, straw~col*row,
-        flip=TRUE, aspect=(100*8)/(155*12), # true aspect
-        main="day.wheat.uniformity - straw yield")
-
      
-# Day fig 2
-coldat <- aggregate(grain~col, dat, sum) 
-xyplot(grain ~ col, coldat, type='l', ylim=c(2500,6500))
-
    dat$rowgroup <- round((dat$row +1)/3,0)
-rowdat <- aggregate(grain~rowgroup, dat, sum) 
-xyplot(grain ~ rowgroup, rowdat, type='l', ylim=c(2500,6500))
-
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/denis.missing-1.png b/docs/reference/denis.missing-1.png deleted file mode 100644 index b75d5837..00000000 Binary files a/docs/reference/denis.missing-1.png and /dev/null differ diff --git a/docs/reference/denis.missing.html b/docs/reference/denis.missing.html deleted file mode 100644 index fc6ec9dc..00000000 --- a/docs/reference/denis.missing.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Multi-environment trial with structured missing values — denis.missing • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial with structured missing values

    - - -
    - -
    -

    Grain yield was measured on 5 genotypes in 26 environments. Missing - values were non-random, but structured.

    -
    - - - -

    Format

    - -

    -
    env

    environment, 26 levels

    -
    gen

    genotype factor, 5 levels

    -
    yield

    yield

    - -
    - -

    Used with permission of Jean-Baptists Denis.

    -

    Source

    - -

    Denis, J. B. and C P Baril, 1992, Sophisticated models with numerous missing - values: The multiplicative interaction model as an example. - Biul. Oceny Odmian, 24--25, 7--31.

    -

    References

    - -

    H P Piepho, (1999) Stability analysis using the SAS system, - Agron Journal, 91, 154--160. - https://doi.og/10.2134/agronj1999.00021962009100010024x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(denis.missing)
-dat <- denis.missing
-
-# view missingness structure
-libs(reshape2)
-acast(dat, env~gen, value.var='yield')
-
    #>     G1 G2 G3 G4 G5
-#> E01 NA NA NA 66 49
-#> E02 NA NA NA 59 40
-#> E03 NA NA NA 67 57
-#> E04 NA NA NA 55 53
-#> E05 NA NA 75 85 74
-#> E06 NA NA 72 77 71
-#> E07 NA NA 55 51 46
-#> E08 NA NA 52 49 44
-#> E09 NA NA 79 80 62
-#> E10 63 64 72 65 51
-#> E11 72 82 83 88 72
-#> E12 69 76 78 86 70
-#> E13 55 53 40 63 60
-#> E14 52 59 54 53 51
-#> E15 67 62 65 68 65
-#> E16 63 74 73 63 59
-#> E17 55 54 NA 52 51
-#> E18 66 66 NA NA 66
-#> E19 57 54 NA NA NA
-#> E20 69 64 NA NA NA
-#> E21 73 69 NA NA NA
-#> E22 68 66 NA NA NA
-#> E23 64 79 NA NA NA
-#> E24 67 66 NA NA NA
-#> E25 70 66 NA NA NA
-#> E26 70 65 NA 60 NA
    
-
-libs(lattice)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(yield ~ gen*env, data=dat,
-          col.regions=redblue,
-          main="denis.missing - incidence heatmap")
-
    
-# stability variance (Table 3 in Piepho)
-libs(nlme)
-m1 <- lme(yield ~ -1 + gen, data=dat, random= ~ 1|env,
-          weights = varIdent(form= ~ 1|gen),
-          na.action=na.omit)
-svar <- m1$sigma^2 * c(1, coef(m1$modelStruct$varStruct, unc = FALSE))^2
-round(svar, 2)
-
    #>          G5    G3    G1    G2 
-#> 39.25 22.95 54.36 12.17 23.77 
    ##          G5    G3    G1    G2
-## 39.25 22.95 54.36 12.17 23.77
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/denis.ryegrass-1.png b/docs/reference/denis.ryegrass-1.png deleted file mode 100644 index 938c5b76..00000000 Binary files a/docs/reference/denis.ryegrass-1.png and /dev/null differ diff --git a/docs/reference/denis.ryegrass.html b/docs/reference/denis.ryegrass.html deleted file mode 100644 index 3545c295..00000000 --- a/docs/reference/denis.ryegrass.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - - -Multi-environment trial of perennial ryegrass in France — denis.ryegrass • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of perennial ryegrass in France

    - - -
    - -
    -

    Plant strength of perennial ryegrass in France for 21 genotypes at 7 - locations.

    -
    - - - -

    Format

    - -

    A data frame with 147 observations on the following 3 variables.

    -
    gen

    genotype, 21 levels

    -
    loc

    location, 7 levels

    -
    strength

    average plant strength * 100

    - -
    - -

    Details

    - -

    INRA conducted a breeding trial in western France with 21 genotypes at - 7 locations. The observed data is 'strength' averaged over 7-10 - plants per plot and three plots per location (after adjusting for - blocking effects). Each plant was scored on a scale 0-9.

    -

    The original data had a value of 86.0 for genotype G1 at location - L4--this was replaced by an additive estimated value of 361.2 as in - Gower and Hand (1996).

    -

    Source

    - -

    Jean-Baptiste Denis and John C. Gower, 1996. - Asymptotic confidence regions for biadditive models: - interpreting genotype-environment interaction, - Applied Statistics, 45, 479-493. - https://doi.org/10.2307/2986069

    -

    References

    - -

    Gower, J.C. and Hand, D.J., 1996. Biplots. - Chapman and Hall.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(denis.ryegrass)
-dat <- denis.ryegrass
-
-# biplots (without ellipses) similar to Denis figure 1
-libs(gge)
-m1 <- gge(dat, strength ~ gen*loc, scale=FALSE)
-biplot(m1, main="denis.ryegrass biplot")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/depalluel.sheep-1.png b/docs/reference/depalluel.sheep-1.png deleted file mode 100644 index 6799182c..00000000 Binary files a/docs/reference/depalluel.sheep-1.png and /dev/null differ diff --git a/docs/reference/depalluel.sheep.html b/docs/reference/depalluel.sheep.html deleted file mode 100644 index c593a60d..00000000 --- a/docs/reference/depalluel.sheep.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - -Latin square of four breeds of sheep with four diets — depalluel.sheep • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Latin square of four breeds of sheep with four diets

    - - -
    - -
    -

    Latin square of four breeds of sheep with four diets

    -
    - - 
    data("depalluel.sheep")
    - - -

    Format

    - -

    A data frame with 32 observations on the following 5 variables.

    -
    food

    diet

    -
    animal

    animal number

    -
    breed

    sheep breed

    -
    weight

    weight, pounds

    -
    date

    months after start

    - - - -

    Details

    - - - -

    This may be the earliest known Latin Square experiment.

    -

    Four sheep from each of four breeds were randomized to four feeds and - four slaughter dates.

    -

    Sheep that eat roots will eat more than sheep eating corn, but each - acre of land produces more roots than corn.

    -

    de Palleuel said: In short, by adopting the use of roots, instead of - corn, for the fattening of all sorts of cattle, the farmers in the - neighborhood of the capital will not only gain great profit - themselves, but will also very much benefit the public by supplying - this great city with resources, and preventing the sudden rise of meat - in her markets, which is often considerable.

    -

    Source

    - -

    M. Crette de Palluel (1788). - On the advantage and economy of feeding sheep in the house with roots. - Annals of Agriculture, 14, 133-139. - https://books.google.com/books?id=LXIqAAAAYAAJ&pg=PA133

    -

    References

    - -

    None

    - -

    Examples

    - 
    
-library(agridat)
-
-data(depalluel.sheep)
-dat <- depalluel.sheep
-
-# Not the best view...weight gain is large in the first month, then slows down
-# and the linear line hides this fact
-libs(lattice)
-xyplot(weight ~ date|food, dat, group=animal, type='l', auto.key=list(columns=4),
-       xlab="Months since start",
-       main="depalluel.sheep")
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/devries.pine-1.png b/docs/reference/devries.pine-1.png deleted file mode 100644 index 6a6fa971..00000000 Binary files a/docs/reference/devries.pine-1.png and /dev/null differ diff --git a/docs/reference/devries.pine-2.png b/docs/reference/devries.pine-2.png deleted file mode 100644 index 1545a3ba..00000000 Binary files a/docs/reference/devries.pine-2.png and /dev/null differ diff --git a/docs/reference/devries.pine.html b/docs/reference/devries.pine.html deleted file mode 100644 index fb3c8910..00000000 --- a/docs/reference/devries.pine.html +++ /dev/null @@ -1,249 +0,0 @@ - - - - - - - - -Graeco-Latin Square experiment in pine — devries.pine • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Graeco-Latin Square experiment in pine

    - - -
    - -
    -

    Graeco-Latin Square experiment in pine

    -
    - - 
    data("devries.pine")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 6 variables.

    -
    block

    block

    -
    row

    row

    -
    col

    column

    -
    spacing

    spacing treatment

    -
    thinning

    thinning treatment

    -
    volume

    stem volume in m^3/ha

    -
    growth

    annual stem volume increment m^3/ha at age 11

    - -
    - -

    Details

    - -

    Experiment conducted on Caribbean Pine at Coebiti in Surinam (Long 55 - 28 30 W, Lat 5 18 5 N). Land was cleared in Jan 1965 and planted May - 1965. Each experimental plot was 60m x 60m. Roads 10 m wide run - between the rows. Each block is thus 180m wide and 200m deep. Data - were collected only on 40m x 40m plots in the center of each - experimental unit. Plots were thinned in 1972 and 1975. - The two treatment factors (spacing, thinning) were assigned in a - Graeco-Latin Square design.

    -

    Spacing: A=2.5, B=3, C=3.5. Thinning: Z=low, M=medium, S=heavy.

    -

    Field width: 4 blocks x 180 m = 720 m

    -

    Field length: 1 block x 200 m = 200 m.

    -

    Source

    - -

    P.G. De Vries, J.W. Hildebrand, N.R. De Graaf. (1978). - Analysis of 11 years growth of carribbean pine in a replicated - Graeco-Latin square spacing-thinning experiment in Surinam. Page 46, 51. - https://edepot.wur.nl/287590

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(devries.pine)
-  dat <- devries.pine
-
-  libs(desplot)
-  desplot(dat, volume ~ col*row,
-          main="devries.pine - expt design and tree volume",
-          col=spacing, num=thinning, cex=1, out1=block, aspect=200/720)
-
      
-  libs(HH)
-  HH::interaction2wt(volume ~ spacing+thinning, dat,
-                     main="devries.pine")
-
    
-  # ANOVA matches appendix 5 of DeVries
-  m1 <- aov(volume ~ block + spacing + thinning + block:factor(row) +
-              block:factor(col), data=dat)
-  anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: volume
-#>                   Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> block              3  5191.8  1730.6 20.4218 5.249e-05 ***
-#> spacing            2 16063.7  8031.9 94.7790 4.453e-08 ***
-#> thinning           2   320.3   160.1  1.8896    0.1935    
-#> block:factor(row)  8   945.0   118.1  1.3939    0.2913    
-#> block:factor(col)  8   784.7    98.1  1.1575    0.3953    
-#> Residuals         12  1016.9    84.7                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/digby.jointregression-1.png b/docs/reference/digby.jointregression-1.png deleted file mode 100644 index 0405d352..00000000 Binary files a/docs/reference/digby.jointregression-1.png and /dev/null differ diff --git a/docs/reference/digby.jointregression.html b/docs/reference/digby.jointregression.html deleted file mode 100644 index ffc5fcc0..00000000 --- a/docs/reference/digby.jointregression.html +++ /dev/null @@ -1,313 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat — digby.jointregression • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat

    - - -
    - -
    -

    Yield of 10 spring wheat varieties for 17 locations in 1976.

    -
    - - - -

    Format

    - -

    A data frame with 134 observations on the following 3 variables.

    -
    gen

    genotype, 10 levels

    -
    env

    environment, 17 levels

    -
    yield

    yield (t/ha)

    - -
    - -

    Details

    - -

    Yield of 10 spring wheat varieties for 17 locations in 1976.

    -

    Used to illustrate modified joint regression.

    -

    Source

    - -

    Digby, P.G.N. (1979). Modified joint regression analysis for - incomplete variety x environment data. - Journal of Agricultural Science, 93, 81-86. - https://doi.org/10.1017/S0021859600086159

    -

    References

    - -

    Hans-Pieter Piepho, 1997. - Analyzing Genotype-Environment Data by Mixed-Models with - Multiplicative Terms. Biometrics, 53, 761-766. - https://doi.org/10.2307/2533976

    -

    RJOINT procedure in GenStat. - https://www.vsni.co.uk/software/genstat/htmlhelp/server/RJOINT.htm

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(digby.jointregression)
-  dat <- digby.jointregression
-  
-  # Simple gen means, ignoring unbalanced data.
-  # Matches Digby table 2, Unadjusted Mean
-  round(tapply(dat$yield, dat$gen, mean),3)
-
    #>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10 
-#> 3.272 3.269 4.040 3.712 3.641 3.144 3.181 2.971 3.697 3.172 
      
-  # Two-way model. Matches Digby table 2, Fitting Constants
-  m00 <- lm(yield ~ 0 + gen + env, dat)
-  round(coef(m00)[1:10]-2.756078+3.272,3) # Adjust intercept
-
    #> genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 
-#>  3.272  3.268  4.051  3.724  3.641  3.195  3.232  3.268  3.749  3.179 
      # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 
-  #  3.272  3.268  4.051  3.724  3.641  3.195  3.232  3.268  3.749  3.179 
-  
-  n.gen <- nlevels(dat$gen)
-  n.env <- nlevels(dat$env)
-  
-  # Estimate theta (env eff)
-  m0 <- lm(yield ~ -1 + env + gen, dat)
-  thetas <- coef(m0)[1:n.env]
-  thetas <- thetas-mean(thetas) # center env effects
-  # Add env effects to the data
-  dat$theta <- thetas[match(paste("env",dat$env,sep=""), names(thetas))]
-  
-  # Initialize beta (gen slopes) at 1
-  betas <- rep(1, n.gen)
-  
-  done <- FALSE
-  while(!done){
-    
-    betas0 <- betas
-    
-    # M1: Fix thetas (env effects), estimate beta (gen slope)
-    m1 <- lm(yield ~ -1 + gen + gen:theta, data=dat)
-    betas <- coef(m1)[-c(1:n.gen)]
-    dat$beta <- betas[match(paste("gen",dat$gen,":theta",sep=""), names(betas))]
-    # print(betas)
-
-    # M2: Fix betas (gen slopes), estimate theta (env slope)
-    m2 <- lm(yield ~ env:beta + gen -1, data=dat)
-    thetas <- coef(m2)[-c(1:n.gen)]
-    thetas[is.na(thetas)] <- 0  # Change last coefficient from NA to 0
-    dat$theta <- thetas[match(paste("env",dat$env,":beta",sep=""), names(thetas))]
-    # print(thetas)
-
-    # Check convergence
-    chg <- sum(((betas-betas0)/betas0)^2)
-    cat("Relative change in betas",chg,"\n")
-    if(chg < .0001) done <- TRUE
-    
-  }
-
    #> Relative change in betas 0.1617878 
-#> Relative change in betas 0.0002241629 
-#> Relative change in betas 1.371073e-06 
    
-  libs(lattice)
-  xyplot(yield ~ theta|gen, data=dat, xlab="theta (environment effect)",
-         main="digby.jointregression - stability plot")
-
    
-  # Dibgy Table 2, modified joint regression
-  
-  # Genotype sensitivities (slopes)
-  round(betas,3) # Match Digby table 2, Modified joint regression sensitivity
-
    #> genG01:theta genG02:theta genG03:theta genG04:theta genG05:theta genG06:theta 
-#>        0.953        0.739        1.082        1.024        1.142        0.877 
-#> genG07:theta genG08:theta genG09:theta genG10:theta 
-#>        1.089        0.914        1.196        0.947 
      # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10
-  #  0.953  0.739  1.082  1.024  1.142  0.877 1.089  0.914  1.196  0.947
-
-  # Env effects. Match Digby table 3, Modified joint reg
-  round(thetas,3)+1.164-.515 # Adjust intercept to match
-
    #> envE01:beta envE02:beta envE03:beta envE04:beta envE05:beta envE06:beta 
-#>      -0.515      -0.578      -0.990      -1.186       1.811       1.696 
-#> envE07:beta envE08:beta envE09:beta envE10:beta envE11:beta envE12:beta 
-#>      -1.096       0.046       0.057       0.825      -0.576       1.568 
-#> envE13:beta envE14:beta envE15:beta envE16:beta envE17:beta 
-#>      -0.779      -0.692       0.836      -1.080       0.649 
      # envE01 envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09 envE10
-  # -0.515 -0.578 -0.990 -1.186  1.811  1.696 -1.096  0.046  0.057  0.825
-  # envE11 envE12 envE13 envE14 envE15 envE16 envE17
-  # -0.576  1.568 -0.779 -0.692  0.836 -1.080  0.649
-
-  # Using 'gnm' gives similar results.
-  # libs(gnm)
-  # m3 <- gnm(yield ~ gen + Mult(gen,env), data=dat) # slopes negated
-  # round(coef(m3)[11:20],3)
-
-  # Using 'mumm' gives similar results, though gen is random and the
-  # coeffecients are shrunk toward 0 a bit.
-  libs(mumm)
-
    #> 
-#> Attaching package: 'mumm'
    #> The following object is masked from 'package:asreml':
-#> 
-#>     lrt
      m1 <- mumm(yield ~ -1 + env + mp(gen, env), dat)
-  round(1 + ranef(m1)$`mp gen:env`,2)
-
    #> $`mp gen:env`
-#>          G01          G02          G03          G04          G05          G06 
-#> -0.030502997 -0.145460728  0.038892126  0.008834426  0.076630297 -0.058095240 
-#>          G07          G08          G09          G10 
-#>  0.052884673 -0.003004166  0.091762826 -0.031937867 
-#> 
    #>  G01  G02  G03  G04  G05  G06  G07  G08  G09  G10 
-#> 0.97 0.85 1.04 1.01 1.08 0.94 1.05 1.00 1.09 0.97 
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/diggle.cow-1.png b/docs/reference/diggle.cow-1.png deleted file mode 100644 index 4c24bacc..00000000 Binary files a/docs/reference/diggle.cow-1.png and /dev/null differ diff --git a/docs/reference/diggle.cow-2.png b/docs/reference/diggle.cow-2.png deleted file mode 100644 index 1430c69b..00000000 Binary files a/docs/reference/diggle.cow-2.png and /dev/null differ diff --git a/docs/reference/diggle.cow.html b/docs/reference/diggle.cow.html deleted file mode 100644 index 47a774b6..00000000 --- a/docs/reference/diggle.cow.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - - - -Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Bodyweight of cows in a 2-by-2 factorial experiment

    - - -
    - -
    -

    Bodyweight of cows in a 2-by-2 factorial experiment.

    -
    - - - -

    Format

    - -

    A data frame with 598 observations on the following 5 variables.

    -
    animal

    Animal factor, 26 levels

    -
    iron

    Factor with levels Iron, NoIron

    -
    infect

    Factor levels Infected, NonInfected

    -
    weight

    Weight in (rounded to nearest 5) kilograms

    -
    day

    Days after birth

    - -
    - -

    Details

    - - - -

    Diggle et al., 1994, pp. 100-101, consider an experiment that studied - how iron dosing (none/standard) and micro-organism (infected or - non-infected) influence the weight of cows.

    -

    Twenty-eight cows were allocated in a 2-by-2 factorial design - with these factors. Some calves were inoculated with tuberculosis at six - weeks of age. At six months, some calves were maintained on - supplemental iron diet for a further 27 months.

    -

    The weight of each animal was measured at 23 times, - unequally spaced. One cow died during the study and data for another - cow was removed.

    -

    Source

    - -

    Diggle, P. J., Liang, K.-Y., & Zeger, S. L. (1994). - Analysis of Longitudinal Data. Page 100-101.

    -

    Retrieved Oct 2011 from - https://www.maths.lancs.ac.uk/~diggle/lda/Datasets/

    -

    References

    - -

    Lepper, AWD and Lewis, VM, 1989. Effects of altered dietary iron - intake in Mycobacterium paratuberculosis-infected dairy cattle: - sequential observations on growth, iron and copper metabolism and - development of paratuberculosis. - Research in veterinary science, 46, 289--296.

    -

    Arunas P. Verbyla and Brian R. Cullis and Michael G. Kenward - and Sue J. Welham, (1999), The analysis of designed experiments and - longitudinal data by using smoothing splines. Appl. Statist., - 48, 269--311.

    -

    SAS/STAT(R) 9.2 User's Guide, Second Edition. - https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(diggle.cow)
-  dat <- diggle.cow
-  
-  # Figure 1 of Verbyla 1999
-  libs(latticeExtra)
-  useOuterStrips(xyplot(weight ~ day|iron*infect, dat, group=animal,
-                        type='b', cex=.5, 
-                        main="diggle.cow"))
-
    
-  # Scaling
-  dat <- transform(dat, time = (day-122)/10)
-
-
-  libs(asreml) # asreml4
-  ## libs(latticeExtra)
-  
-  ## # Smooth for each animal.  No treatment effects. Similar to SAS Output 38.6.9
-
-  m1 <- asreml(weight ~ 1 + lin(time) + animal + animal:lin(time), data=dat,
-               random = ~ animal:spl(time))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:14 2021
-#> Spline: design points closer than 0.00659 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1685.373       89.4521    545 17:08:14    0.0
-#>  2     -1662.656       79.4217    545 17:08:14    0.0
-#>  3     -1633.382       66.9151    545 17:08:14    0.0
-#>  4     -1607.650       55.6989    545 17:08:14    0.0
-#>  5     -1590.169       46.6521    545 17:08:14    0.0
-#>  6     -1584.009       41.6081    545 17:08:14    0.0
-#>  7     -1582.756       39.2910    545 17:08:14    0.0
-#>  8     -1582.631       38.5276    545 17:08:14    0.0
-#>  9     -1582.625       38.3532    545 17:08:14    0.0
-#> 10     -1582.625       38.3205    545 17:08:14    0.0
      p1 <- predict(m1, data=dat, classify="animal:time",
-                design.points=list(time=seq(0,65.9, length=50)))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:14 2021
-#> Spline: design points closer than 0.00659 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1582.625       38.3147    545 17:08:14    0.1
-#>  2     -1582.625       38.3143    545 17:08:14    0.0
-#>  3     -1582.625       38.3136    545 17:08:14    0.0
      p1 <- p1$pvals
-  p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in
-  foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal)
-  foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal)
-  print(foo1+foo2)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/draper.safflower.uniformity-1.png b/docs/reference/draper.safflower.uniformity-1.png deleted file mode 100644 index 9256fd47..00000000 Binary files a/docs/reference/draper.safflower.uniformity-1.png and /dev/null differ diff --git a/docs/reference/draper.safflower.uniformity-2.png b/docs/reference/draper.safflower.uniformity-2.png deleted file mode 100644 index 11417d90..00000000 Binary files a/docs/reference/draper.safflower.uniformity-2.png and /dev/null differ diff --git a/docs/reference/draper.safflower.uniformity-3.png b/docs/reference/draper.safflower.uniformity-3.png deleted file mode 100644 index fe46ff50..00000000 Binary files a/docs/reference/draper.safflower.uniformity-3.png and /dev/null differ diff --git a/docs/reference/draper.safflower.uniformity.html b/docs/reference/draper.safflower.uniformity.html deleted file mode 100644 index e4f5d45f..00000000 --- a/docs/reference/draper.safflower.uniformity.html +++ /dev/null @@ -1,284 +0,0 @@ - - - - - - - - -Uniformity trial of safflower — draper.safflower.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Uniformity trial of safflower

    - - -
    - -
    -

    Uniformity trial of safflower in Arizona in 1958.

    -
    - - 
    data("draper.safflower.uniformity")
    - - -

    Format

    - -

    A data frame with 640 observations on the following 4 variables.

    -
    expt

    experiment

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per plot (grams)

    - -
    - -

    Details

    - - - -

    Experiments were conducted at the Agricultural Experiment Station Farm - at Eloy, Arizona. The crop was harvested in July 1958.

    -

    The crop was planted in two rows 12 inches apart on vegetable beds 40 - inches center to center.

    -

    In each test, the end ranges and one row of plots on one side were - next to alleys, and those plots gave estimates of border effects.

    -

    Experiment E4 (four foot test).

    -

    Sandy streaks were present in the field. Average yield was 1487 - lb/ac. A diagonal fertility gradient was in this field. Widening the - plot was equally effective as lengthening the plot to reduce - variability. The optimum plot size was 1 bed wide, 24 feet long. - Considering economic costs, the optimum size was 1 bed, 12 feet long.

    -

    Field width: 16 beds * 3.33 = 53 feet

    -

    Field length: 18 ranges * 4 feet = 72 feet

    - -

    Experiment E5 (five foot test)

    -

    Average yield 2517 lb/ac, typical for this crop. Combining plots - lengthwise was more effective than widening the plots, in order to - reduce variability. The optimum plot size was 1 bed wide, 25 feet - long. - Considering economic costs, the optimum size was 1 bed, 18 feet long.

    -

    Field width: 14 beds * 3.33 = 46.6 feet.

    -

    Field length: 18 ranges * 5 feet = 90 feet.

    -

    Source

    - -

    Arlen D. Draper. (1959). - Optimum plot size and shape for safflower yield tests. - Dissertation. University of Arizona. - https://hdl.handle.net/10150/319371

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
- library(agridat)
-
-  data(draper.safflower.uniformity)
-  dat4 <- subset(draper.safflower.uniformity, expt=="E4")
-  dat5 <- subset(draper.safflower.uniformity, expt=="E5")
-
-  libs(desplot)
-  desplot(dat4, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=72/53, # true aspect
-          main="draper.safflower.uniformity (four foot)")
-
    
-  desplot(dat5, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=90/46, # true aspect
-          main="draper.safflower.uniformity (five foot)")
-
    
-  libs(agricolae)
-  libs(reshape2)
-  # Remove border plots at either end and right side
-  dat4 <- subset(dat4, row>1 & row<20)
-  dat4 <- subset(dat4, col<17)
-  dat5 <- subset(dat5, row>1 & row<20)
-  dat5 <- subset(dat5, col<15)
-  
-  s4 <- index.smith(acast(dat4, row~col, value.var='yield'),
-                    main="draper.safflower.uniformity (four foot)",
-                    col="red")$uni
-
      s4 # match Draper table 2, p 22
-
    #>       Size Width Length plots        Vx   CV
-#>  [1,]    1     1      1   288 2164.6567 22.5
-#>  [2,]    2     1      2   144 1490.8710 18.7
-#>  [3,]    2     2      1   144 1539.8152 19.0
-#>  [4,]    3     1      3    96 1236.1247 17.0
-#>  [5,]    4     2      2    72 1152.7394 16.4
-#>  [6,]    4     4      1    72 1107.6854 16.1
-#>  [7,]    6     1      6    48  805.0568 13.7
-#>  [8,]    6     2      3    48  991.4505 15.3
-#>  [9,]    8     4      2    36  879.1484 14.4
-#> [10,]    8     8      1    36  768.9838 13.4
-#> [11,]    9     1      9    32  724.5614 13.0
-#> [12,]   12     2      6    24  672.9439 12.6
-#> [13,]   12     4      3    24  819.2284 13.9
-#> [14,]   16     8      2    18  615.4627 12.0
-#> [15,]   18     2      9    16  598.8671 11.9
-#> [16,]   24     4      6    12  578.4655 11.7
-#> [17,]   24     8      3    12  613.4109 12.0
-#> [18,]   36     4      9     8  548.4288 11.3
-#> [19,]   48     8      6     6  408.3539  9.8
-#> [20,]   72     8      9     4  417.8544  9.9
      
-  ## s5 <- index.smith(acast(dat5, row~col, value.var='yield'),
-  ##                   main="draper.safflower.uniformity (five foot)",
-  ##                   col="red")$uni
-  ## s5 # match Draper table 1, p 21
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/durban.competition-1.png b/docs/reference/durban.competition-1.png deleted file mode 100644 index 046c86eb..00000000 Binary files a/docs/reference/durban.competition-1.png and /dev/null differ diff --git a/docs/reference/durban.competition-2.png b/docs/reference/durban.competition-2.png deleted file mode 100644 index d7e3952b..00000000 Binary files a/docs/reference/durban.competition-2.png and /dev/null differ diff --git a/docs/reference/durban.competition.html b/docs/reference/durban.competition.html deleted file mode 100644 index 7c3ce947..00000000 --- a/docs/reference/durban.competition.html +++ /dev/null @@ -1,286 +0,0 @@ - - - - - - - - -Sugar beet yields with competition effects — durban.competition • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Sugar beet yields with competition effects

    - - -
    - -
    -

    Sugar beet yields with competition effects

    -
    - - - -

    Format

    - -

    A data frame with 114 observations on the following 5 variables.

    -
    gen

    Genotype factor, 36 levels plus Border

    -
    col

    Column

    -
    block

    Row/Block

    -
    wheel

    Position relative to wheel tracks

    -
    yield

    Root yields, kg/plot

    - -
    - -

    Details

    - -

    This sugar-beet trial was conducted in 1979.

    -

    Single-row plots, 12 m long, 0.5 m between rows. Each block is made - up of all 36 genotypes laid out side by side. Guard/border plots are - at each end. Root yields were collected.

    -

    Wheel tracks are located between columns 1 and 2, and between columns - 5 and 6, for each set of six plots. Each genotype was randomly - allocated once to each pair of plots (1,6), (2,5), (3,4) across the - three reps. Wheel effect were not significant in _this_ trial.

    -

    Field width: 18m + 1m guard rows = 19m

    -

    Field length: 3 blocks * 12m + 2*0.5m spacing = 37m - Retrieved from - https://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat

    -

    Used with permission of Iain Currie.

    -

    Source

    - -

    Durban, M., Currie, I. and R. Kempton, 2001. - Adjusting for fertility and competition in variety trials. - J. of Agricultural Science, 136, 129--140.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(durban.competition)
-dat <- durban.competition
-
-# Check that genotypes were balanced across wheel tracks.
-with(dat, table(gen,wheel))
-
    #>      wheel
-#> gen   1 2 3
-#>   B   0 0 0
-#>   G01 1 1 1
-#>   G02 1 1 1
-#>   G03 1 1 1
-#>   G04 1 1 1
-#>   G05 1 1 1
-#>   G06 1 1 1
-#>   G07 1 1 1
-#>   G08 1 1 1
-#>   G09 1 1 1
-#>   G10 1 1 1
-#>   G11 1 1 1
-#>   G12 1 1 1
-#>   G13 1 1 1
-#>   G14 1 1 1
-#>   G15 1 1 1
-#>   G16 1 1 1
-#>   G17 1 1 1
-#>   G18 1 1 1
-#>   G19 1 1 1
-#>   G20 1 1 1
-#>   G21 1 1 1
-#>   G22 1 1 1
-#>   G23 1 1 1
-#>   G24 1 1 1
-#>   G25 1 1 1
-#>   G26 1 1 1
-#>   G27 1 1 1
-#>   G28 1 1 1
-#>   G29 1 1 1
-#>   G30 1 1 1
-#>   G31 1 1 1
-#>   G32 1 1 1
-#>   G33 1 1 1
-#>   G34 1 1 1
-#>   G35 1 1 1
-#>   G36 1 1 1
    
-libs(desplot)
-desplot(dat, yield ~ col*block,
-        out1=block, text=gen, col=wheel, aspect=37/19, # true aspect
-        main="durban.competition")
-
    
-
-# Calculate residual after removing block/genotype effects
-m1 <- lm(yield ~ gen + block, data=dat)
-dat$res <- resid(m1)
-
-## desplot(dat, res ~ col*block, out1=block, text=gen, col=wheel,
-##         main="durban.competition - residuals")
-
-# Calculate mean of neighboring plots
-dat$comp <- NA
-dat$comp[3:36] <- ( dat$yield[2:35] + dat$yield[4:37] ) / 2
-dat$comp[41:74] <- ( dat$yield[40:73] + dat$yield[42:75] ) / 2
-dat$comp[79:112] <- ( dat$yield[78:111] + dat$yield[80:113] ) / 2
-
-# Demonstrate the competition effect
-# Competitor plots have low/high yield -> residuals are negative/positive
-libs(lattice)
-xyplot(res~comp, dat, type=c('p','r'), main="durban.competition",
-       xlab="Average yield of neighboring plots", ylab="Residual")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/durban.rowcol-1.png b/docs/reference/durban.rowcol-1.png deleted file mode 100644 index 9d583d51..00000000 Binary files a/docs/reference/durban.rowcol-1.png and /dev/null differ diff --git a/docs/reference/durban.rowcol-2.png b/docs/reference/durban.rowcol-2.png deleted file mode 100644 index 7fd908a0..00000000 Binary files a/docs/reference/durban.rowcol-2.png and /dev/null differ diff --git a/docs/reference/durban.rowcol-3.png b/docs/reference/durban.rowcol-3.png deleted file mode 100644 index 057e5ea7..00000000 Binary files a/docs/reference/durban.rowcol-3.png and /dev/null differ diff --git a/docs/reference/durban.rowcol-4.png b/docs/reference/durban.rowcol-4.png deleted file mode 100644 index ee53bdc3..00000000 Binary files a/docs/reference/durban.rowcol-4.png and /dev/null differ diff --git a/docs/reference/durban.rowcol-5.png b/docs/reference/durban.rowcol-5.png deleted file mode 100644 index 8d6a33cc..00000000 Binary files a/docs/reference/durban.rowcol-5.png and /dev/null differ diff --git a/docs/reference/durban.rowcol-6.png b/docs/reference/durban.rowcol-6.png deleted file mode 100644 index cbe83834..00000000 Binary files a/docs/reference/durban.rowcol-6.png and /dev/null differ diff --git a/docs/reference/durban.rowcol.html b/docs/reference/durban.rowcol.html deleted file mode 100644 index 350555ed..00000000 --- a/docs/reference/durban.rowcol.html +++ /dev/null @@ -1,281 +0,0 @@ - -Row-column experiment of spring barley, many varieties — durban.rowcol • agridat - - -
    -
    - - - -
    -
    -
    -

    Row-column experiment of spring barley, many varieties

    - - -
    - -
    -

    Row-column experiment of spring barley, many varieties

    -
    - - -
    -

    Format

    -

    A data frame with 544 observations on the following 5 variables.

    row
    -

    row

    - -
    bed
    -

    bed (column)

    - -
    rep
    -

    rep, 2 levels

    - -
    gen
    -

    genotype, 272 levels

    - -
    yield
    -

    yield, tonnes/ha

    - - -
    -
    -

    Details

    -

    Spring barley variety trial of 272 entries (260 new varieties, 12 - control). Grown at the Scottish Crop Research Institute in 1998. - Row-column design with 2 reps, 16 rows (north/south) by 34 beds - (east/west). - The land sloped downward from row 16 to row 1. Plot yields were - converted to tonnes per hectare.

    -

    Plot dimensions are not given.

    -

    Used with permission of Maria Durban.

    -
    -
    -

    Source

    -

    Durban, Maria and Hackett, Christine and McNicol, James and - Newton, Adrian and Thomas, William and Currie, Iain. 2003. - The practical use of semiparametric models in field trials, - Journal of Agric Biological and Envir Stats, 8, 48-66. - https://doi.org/10.1198/1085711031265

    -
    -
    -

    References

    -

    Edmondson, Rodney (2020). - Multi-level Block Designs for Comparative Experiments. - J of Agric, Biol, and Env Stats. - https://doi.org/10.1007/s13253-020-00416-0

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(durban.rowcol)
-  dat <- durban.rowcol
-  
-  libs(desplot)
-  desplot(dat, yield~bed*row,
-          out1=rep, num=gen, # aspect unknown
-          main="durban.rowcol")
-
-  
-
-  # Durban 2003 Figure 1
-  m10 <- lm(yield~gen, data=dat)
-  dat$resid <- m10$resid
-  ## libs(lattice)
-  ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.rowcol")
-  ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.rowcol")
-  
-  # Figure 3
-  libs(lattice)
-  xyplot(resid ~ bed|factor(row), data=dat,
-         main="durban.rowcol",
-         type=c('p','smooth'))
-
-  
-  
-
-  # Figure 5 - field trend
-  # note, Durban used gam package like this
-  # m1lo <- gam(yield ~ gen + lo(row, span=10/16) + lo(bed, span=9/34), data=dat)
-  libs(mgcv)
-#> Loading required package: nlme
-#> 
-#> Attaching package: 'nlme'
-#> The following object is masked from 'package:dplyr':
-#> 
-#>     collapse
-#> This is mgcv 1.8-38. For overview type 'help("mgcv-package")'.
-  m1lo <- gam(yield ~ gen + s(row) + s(bed, k=5), data=dat)
-  new1 <- expand.grid(row=unique(dat$row),bed=unique(dat$bed))
-  new1 <- cbind(new1, gen="G001")
-  p1lo <- predict(m1lo, newdata=new1)
-  libs(lattice)
-  wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main="Field trend")
-
-
-
-  libs(asreml) # asreml4
-
-  dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
-  dat <- dat[order(dat$rowf, dat$bedf),]
-
-  m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
-                 random=~spl(rowf) + spl(bedf) + units,
-                 family=asr_gaussian(dispersion=1))
-#> Offline License checked out Fri Dec 17 15:16:56 2021
-#> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:16:56 2021
-#> Spline: design points closer than 0.0015 have been merged.
-#> Spline: design points closer than 0.0033 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -120.891           1.0    270 15:16:56    0.1 (1 restrained)
-#>  2      -119.429           1.0    270 15:16:56    0.1 (1 restrained)
-#>  3      -119.337           1.0    270 15:16:56    0.1 (1 restrained)
-#>  4      -119.330           1.0    270 15:16:57    0.1 (1 restrained)
-#>  5      -119.330           1.0    270 15:16:57    0.1
-  m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
-                 random=~spl(rowf) + spl(bedf) + units,
-                 resid = ~ar1(rowf):ar1(bedf))
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:16:57 2021
-#> Spline: design points closer than 0.0015 have been merged.
-#> Spline: design points closer than 0.0033 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       96.2641     0.0728538    270 15:16:57    0.2
-#>  2       99.9959     0.0455420    270 15:16:57    0.2 (2 restrained)
-#>  3      111.7824     0.0939540    270 15:16:57    0.2
-#>  4      114.3283     0.0725701    270 15:16:57    0.1
-#>  5      114.8798     0.0621998    270 15:16:57    0.2
-#>  6      115.1073     0.0575968    270 15:16:58    0.1
-#>  7      115.2248     0.0558544    270 15:16:58    0.1
-#>  8      115.2833     0.0555463    270 15:16:58    0.2
-#>  9      115.3120     0.0558563    270 15:16:58    0.1
-#> 10      115.3273     0.0563693    270 15:16:58    0.1
-#> 11      115.3362     0.0568967    270 15:16:58    0.2
-#> 12      115.3417     0.0573770    270 15:16:58    0.2
-#> 13      115.3452     0.0577936    270 15:16:59    0.2
-#> Warning: Log-likelihood not converged
-#> Warning: Some components changed by more than 1% on the last iteration.
-  m1a2 <- update(m1a2)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:16:59 2021
-#> Spline: design points closer than 0.0015 have been merged.
-#> Spline: design points closer than 0.0033 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       115.347     0.0581463    270 15:16:59    0.2
-#>  2       115.348     0.0582390    270 15:16:59    0.2
-#>  3       115.349     0.0585176    270 15:16:59    0.2
-#>  4       115.350     0.0587488    270 15:16:59    0.2
-#>  5       115.351     0.0589395    270 15:16:59    0.2
-#>  6       115.351     0.0590963    270 15:17:00    0.1
-#>  7       115.351     0.0592248    270 15:17:00    0.1
-  m1a3 <- asreml(yield~gen, data=dat, random=~units,
-                 resid = ~ar1(rowf):ar1(bedf))
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:00 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       45.6323      0.120599    272 15:17:00    0.0
-#>  2       58.3809      0.047543    272 15:17:00    0.0 (2 restrained)
-#>  3      110.6551      0.116261    272 15:17:00    0.0
-#>  4      116.9931      0.099641    272 15:17:00    0.0
-#>  5      120.3275      0.096781    272 15:17:00    0.0
-#>  6      121.2251      0.114524    272 15:17:00    0.0
-#>  7      121.2782      0.124018    272 15:17:00    0.0
-#>  8      121.2798      0.124599    272 15:17:00    0.0
-
-  # Figure 7
-  libs(lattice)
-  v7a <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals)
-  wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a
-
-  
-  v7b <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals)
-  wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b
-
-  
-  v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals)
-  wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c
-
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/durban.splitplot-1.png b/docs/reference/durban.splitplot-1.png deleted file mode 100644 index dcad4d3a..00000000 Binary files a/docs/reference/durban.splitplot-1.png and /dev/null differ diff --git a/docs/reference/durban.splitplot-2.png b/docs/reference/durban.splitplot-2.png deleted file mode 100644 index 97ac9b21..00000000 Binary files a/docs/reference/durban.splitplot-2.png and /dev/null differ diff --git a/docs/reference/durban.splitplot-3.png b/docs/reference/durban.splitplot-3.png deleted file mode 100644 index 2e3a6d28..00000000 Binary files a/docs/reference/durban.splitplot-3.png and /dev/null differ diff --git a/docs/reference/durban.splitplot.html b/docs/reference/durban.splitplot.html deleted file mode 100644 index 4644ea99..00000000 --- a/docs/reference/durban.splitplot.html +++ /dev/null @@ -1,253 +0,0 @@ - -Split-plot experiment of barley with fungicide treatments — durban.splitplot • agridat - - -
    -
    - - - -
    -
    -
    -

    Split-plot experiment of barley with fungicide treatments

    - - -
    - -
    -

    Split-plot experiment of barley with fungicide treatments

    -
    - - -
    -

    Format

    -

    A data frame with 560 observations on the following 6 variables.

    yield
    -

    yield, tonnes/ha

    - -
    block
    -

    block, 4 levels

    - -
    gen
    -

    genotype, 70 levels

    - -
    fung
    -

    fungicide, 2 levels

    - -
    row
    -

    row

    - -
    bed
    -

    bed (column)

    - - -
    -
    -

    Details

    -

    Grown in 1995-1996 at the Scottish Crop Research Institute. - Split-plot design with 4 blocks, 2 whole-plot fungicide treatments, - and 70 barley varieties or variety mixes. Total area was 10 rows - (north/south) by 56 beds (east/west).

    -

    Used with permission of Maria Durban.

    -
    -
    -

    Source

    -

    Durban, Maria and Hackett, Christine and McNicol, James and - Newton, Adrian and Thomas, William and Currie, Iain. 2003. - The practical use of semiparametric models in field trials, - Journal of Agric Biological and Envir Stats, 8, 48-66. - https://doi.org/10.1198/1085711031265.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(durban.splitplot)
-  dat <- durban.splitplot
-
-  libs(desplot)
-  desplot(dat, yield~bed*row,
-          out1=block, out2=fung, num=gen, # aspect unknown
-          main="durban.splitplot")
-
-
-
-  # Durban 2003, Figure 2
-  m20 <- lm(yield~gen + fung + gen:fung, data=dat)
-  dat$resid <- m20$resid
-  ## libs(lattice)
-  ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.splitplot")
-  ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.splitplot")
-
-  # Figure 4 doesn't quite match due to different break points
-  libs(lattice)
-  xyplot(resid ~ bed|factor(row), data=dat,
-         main="durban.splitplot",
-         type=c('p','smooth'))
-
-
-
-  # Figure 6 - field trend
-  # note, Durban used gam package like this
-  # m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat)
-  libs(mgcv)
-  m2lo <- gam(yield ~ gen*fung + s(row, bed,k=45), data=dat)
-  new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed))
-  new2 <- cbind(new2, gen="G01", fung="F1")
-  p2lo <- predict(m2lo, newdata=new2)
-  libs(lattice)
-  wireframe(p2lo~row+bed, new2, aspect=c(1,.5),
-            main="durban.splitplot - Field trend")
-
-
-  libs(asreml) # asreml4
-    
-  # Table 5, variance components.  Table 6, F tests
-  dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
-  dat <- dat[order(dat$rowf, dat$bedf),]
-  m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
-                 resid =~ar1v(rowf):ar1(bedf))
-#> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:10 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       208.653           1.0    420 15:17:10    0.1 (3 restrained)
-#> Log-likelihood decreased to  149.571 ; trying with reduced updates (0.757712)
-#>  2       204.621           1.0    420 15:17:10    0.1
-#>  3       215.185           1.0    420 15:17:10    0.0 (3 restrained)
-#>  4       224.963           1.0    420 15:17:10    0.0 (2 restrained)
-#>  5       231.997           1.0    420 15:17:10    0.0 (3 restrained)
-#> Log-likelihood decreased to  199.331 ; trying with reduced updates (0.408715)
-#>  6       234.142           1.0    420 15:17:10    0.1
-#>  7       245.873           1.0    420 15:17:10    0.0
-#>  8       280.158           1.0    420 15:17:10    0.0
-#>  9       312.280           1.0    420 15:17:11    0.0
-#> 10       328.542           1.0    420 15:17:11    0.0
-#> 11       338.508           1.0    420 15:17:11    0.0
-#> 12       343.036           1.0    420 15:17:11    0.0
-#> 13       343.633           1.0    420 15:17:11    0.0
-#> Warning: Log-likelihood not converged
-#> Warning: Some components changed by more than 1% on the last iteration.
-  m2a2 <- update(m2a2)
-#> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:11 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       343.701           1.0    420 15:17:11    0.1
-#>  2       343.707           1.0    420 15:17:11    0.1 (1 restrained)
-#>  3       343.714           1.0    420 15:17:11    0.0 (1 restrained)
-#>  4       343.715           1.0    420 15:17:11    0.0 (1 restrained)
-#> Warning: Some components changed by more than 1% on the last iteration.
-  
-  libs(lucid)
-  vc(m2a2)
-#>              effect component std.error z.ratio bound  %ch
-#>               block 0.0000002        NA      NA     B 93.7
-#>          block:fung 0.01209    0.01634     0.74     P  0.7
-#>               units 0.02463    0.002465   10        P  0  
-#>         rowf:bedf!R 1                NA      NA     F  0  
-#>  rowf:bedf!rowf!cor 0.8836     0.03721    24        U  0  
-#>  rowf:bedf!rowf!var 0.1262     0.0452      2.8      P  0.2
-#>  rowf:bedf!bedf!cor 0.9202     0.02869    32        U  0  
-  ##             effect component std.error z.ratio bound 
-  ##              block   0              NA      NA     B  NA
-  ##         block:fung   0.01206  0.01512      0.8     P   0
-  ##              units   0.02463  0.002465    10       P   0
-  ##       rowf:bedf(R)   1              NA      NA     F   0
-  ## rowf:bedf!rowf!cor   0.8836   0.03646     24       U   0
-  ## rowf:bedf!rowf!var   0.1261   0.04434      2.8     P   0
-  ## rowf:bedf!bedf!cor   0.9202   0.02846     32       U   0
-  
-  wald(m2a2)
-#> Wald tests for fixed effects.
-#> Response: yield
-#> Terms added sequentially; adjusted for those above.
-#> 
-#>               Df Sum of Sq Wald statistic Pr(Chisq)    
-#> (Intercept)    1   1051.96        1051.96 < 2.2e-16 ***
-#> gen           69   1099.59        1099.59 < 2.2e-16 ***
-#> fung           1     37.04          37.04 1.156e-09 ***
-#> gen:fung      69     89.64          89.64   0.04816 *  
-#> residual (MS)         1.00                             
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-  
-# }
-
    -
    -
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    Developed by Kevin Wright.

    -
    - -
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    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/eden.nonnormal-1.png b/docs/reference/eden.nonnormal-1.png deleted file mode 100644 index 8d6798db..00000000 Binary files a/docs/reference/eden.nonnormal-1.png and /dev/null differ diff --git a/docs/reference/eden.nonnormal-2.png b/docs/reference/eden.nonnormal-2.png deleted file mode 100644 index db6e1b92..00000000 Binary files a/docs/reference/eden.nonnormal-2.png and /dev/null differ diff --git a/docs/reference/eden.nonnormal-3.png b/docs/reference/eden.nonnormal-3.png deleted file mode 100644 index ef020a7c..00000000 Binary files a/docs/reference/eden.nonnormal-3.png and /dev/null differ diff --git a/docs/reference/eden.nonnormal.html b/docs/reference/eden.nonnormal.html deleted file mode 100644 index 4137cd7f..00000000 --- a/docs/reference/eden.nonnormal.html +++ /dev/null @@ -1,318 +0,0 @@ - - - - - - - - -Height of barley plants in a study of non-normal data — eden.nonnormal • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Height of barley plants in a study of non-normal data

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    Height of barley plants in a study of non-normal data.

    -
    - - 
    data("eden.nonnormal")
    - - -

    Format

    - -

    A data frame with 256 observations on the following 3 variables.

    -
    pos

    position within block

    -
    block

    block (numeric)

    -
    height

    height of wheat plant

    - -
    - -

    Details

    - - - -

    This data was used in a very early example of a permutation test.

    -

    Eden & Yates used this data to consider the impact of non-normal data - on the validity of a hypothesis test that assumes normality. They - concluded that the skew data did not negatively affect the analysis of - variance.

    -

    Grown at Rothamsted. Eight blocks of Yeoman II wheat. Sampling of the - blocks was quarter-meter rows, four times in each row. Rows were - selected at random. Position within the rows was partly controlled to - make use of the whole length of the block. Plants at both ends of the - sub-unit were measured. Shoot height is measured from ground level to - the auricle of the last expanded leaf.

    -

    Source

    - -

    T. Eden, F. Yates (1933). - On the validity of Fisher's z test when applied to an actual example of non-normal data. - Journal of Agric Science, 23, 6-17. - https://doi.org/10.1017/S0021859600052862

    -

    References

    - -

    Kenneth J. Berry, Paul W. Mielke, Jr., Janis E. Johnston - Permutation Statistical Methods: An Integrated Approach.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(eden.nonnormal)
-dat <- eden.nonnormal
-mean(dat$height) # 55.23 matches Eden table 1
-
    #> [1] 55.23242
    
-# Eden figure 2
-libs(dplyr, lattice)
-# Blocks had different means, so substract block mean from each datum
-dat <- group_by(dat, block)
-dat <- mutate(dat, blkmn=mean(height))
-dat <- transform(dat, dev=height-blkmn)
-
-histogram( ~ dev, data=dat, breaks=seq(from=-40, to=30, by=2.5),
-          xlab="Deviations from block means",
-          main="eden.nonnormal - heights skewed left")
-
    
-# \dontrun{
-
-  # calculate skewness, permutation 
-  
-  libs(dplyr, lattice, latticeExtra)
-  
-  # Eden table 1
-  # anova(aov(height ~ factor(block), data=dat))
-  
-  # Eden table 2,3. Note, this may be a different definition of skewness
-  # than is commonly used today (e.g. e1071::skewness).
-  skew <- function(x){
-    n <- length(x)
-    x <- x - mean(x)
-    s1 = sum(x)
-    s2 = sum(x^2)
-    s3 = sum(x^3)
-    k3=n/((n-1)*(n-2)) * s3 -3/n*s2*s1 + 2/n^2 * s1^3
-    return(k3)
-  }
-  # Negative values indicate data are skewed left
-  dat <- group_by(dat, block)
-  summarize(dat, s1=sum(height),s2=sum(height^2), mean2=var(height), k3=skew(height))
-
    #> `summarise()` ungrouping output (override with `.groups` argument)
    #> # A tibble: 8 x 5
-#>   block    s1      s2 mean2     k3
-#>   <int> <dbl>   <dbl> <dbl>  <dbl>
-#> 1     1 1682   95930. 243.  -1269.
-#> 2     2 1858  111662. 122.  -1752.
-#> 3     3 1810. 108967. 214.  -3173.
-#> 4     4 1912  121748. 242.  -2548.
-#> 5     5 1722   99026. 205.   -559.
-#> 6     6 1339   63077  227.   -801.
-#> 7     7 1963  123052.  85.0  -713.
-#> 8     8 1854  112366  160.  -1062.
      ##   block     s1       s2     mean2         k3
-  ##   <int>  <dbl>    <dbl>     <dbl>      <dbl>
-  ## 1     1 1682.0  95929.5 242.56048 -1268.5210
-  ## 2     2 1858.0 111661.5 121.97984 -1751.9919
-  ## 3     3 1809.5 108966.8 214.36064 -3172.5284
-  ## 4     4 1912.0 121748.5 242.14516 -2548.2194
-  ## 5     5 1722.0  99026.5 205.20565  -559.0629
-  ## 6     6 1339.0  63077.0 227.36190  -801.2740
-  ## 7     7 1963.0 123052.5  84.99093  -713.2595
-  ## 8     8 1854.0 112366.0 159.67339 -1061.9919
-
-  # Another way to view skewness with qq plot. Panel 3 most skewed.
-  qqmath( ~ dev|factor(block), data=dat,
-         as.table=TRUE,
-         ylab="Deviations from block means",
-         panel = function(x, ...) {
-           panel.qqmathline(x, ...)
-           panel.qqmath(x, ...)
-         })
-
    
-  # Now, permutation test.
-  # Eden: "By a process of amalgamation the eight sets of 32 observations were
-  # reduced to eight sets of four and the data treated as a potential
-  # layout for a 32-plot trial".
-  dat2 <- transform(dat, grp = rep(1:4, each=8))
-  dat2 <- aggregate(height ~ grp+block, dat2, sum)
-  dat2$trt <- rep(letters[1:4], 8)
-  dat2$block <- factor(dat2$block)
-
-  # Treatments were assigned at random 1000 times
-  set.seed(54323)
-  fobs <- rep(NA, 1000)
-  for(i in 1:1000){
-    # randomize treatments within each block
-    # trick from https://stackoverflow.com/questions/25085537
-    dat2$trt <- with(dat2, ave(trt, block, FUN = sample))
-    fobs[i] <- anova(aov(height ~ block + trt, dat2))["trt","F value"]
-  }
-
-  # F distribution with 3,21 deg freedom
-  # Similar to Eden's figure 4, but on a different horizontal scale
-  xval <- seq(from=0,to=max(fobs), length=50)
-  yval <- df(xval, df1 = 3, df2 = 21)
-  # Re-scale, 10 = max of historgram, 0.7 = max of density
-  histogram( ~ fobs, breaks=xval,
-            xlab="F value",
-            main="Observed (histogram) & theoretical (line) F values") +
-    xyplot((10/.7)* yval ~ xval, type="l", lwd=2)
-
    
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
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    Potato yields in response to potash and nitrogen fertilizer

    - - -
    - -
    -

    Potato yields in response to potash and nitrogen fertilizer. - Data from Fisher's 1929 paper Studies in Crop Variation 6. - A different design was used each year.

    -
    - - - -

    Format

    - -

    A data frame with 225 observations on the following 9 variables.

    -
    year

    year/type factor

    -
    yield

    yield, pounds per plot

    -
    block

    block

    -
    row

    row

    -
    col

    column

    -
    trt

    treatment factor

    -
    nitro

    nitrogen fertilizer, cwt/acre

    -
    potash

    potash fertilizer, cwt/acre

    -
    ptype

    potash type

    - -
    - -

    Details

    - -

    The data is of interest to show the gradual development of - experimental designs in agriculture.

    -

    In 1925/1926 the potato variety was Kerr's Pink. In 1927 Arran - Comrade.

    -

    In the 1925a/1926a qualitative experiments, the treatments are - O=None, S=Sulfate, M=Muriate, P=Potash manure salts. - The design was a Latin Square.

    -

    The 1925/1926b/1927 experiments were RCB designs with treatment codes - defining the amount and type of fertilizer used. Note: the 't' - treatment was not defined in the original paper.

    -

    Source

    - -

    T Eden and R A Fisher, 1929. - Studies in Crop Variation. VI. Experiments on the response of the - potato to potash and nitrogen. - Journal of Agricultural Science, 19: 201-213.

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    
-library(agridat)
-
-data(eden.potato)
-dat <- eden.potato
-
-# 1925 qualitative
-d5a <- subset(dat, year=='1925a')
-libs(desplot)
-desplot(d5a, trt~col*row,
-        text=yield, cex=1, shorten='no', # aspect unknown
-        main="eden.potato: 1925 qualitative")
-
    anova(m5a <- aov(yield~trt+factor(row)+factor(col), d5a)) # table 2
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>             Df Sum Sq Mean Sq  F value    Pr(>F)    
-#> trt          3 120175   40058 120.4532 9.472e-06 ***
-#> factor(row)  3   1740     580   1.7437   0.25729    
-#> factor(col)  3   5841    1947   5.8547   0.03247 *  
-#> Residuals    6   1995     333                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# 1926 qualitative
-d6a <- subset(dat, year=='1926a')
-libs(desplot)
-desplot(d6a, trt~col*row,
-        text=yield, cex=1, shorten='no', # aspect unknown
-        main="eden.potato: 1926 qualitative")
-
    anova(m6a <- aov(yield~trt+factor(row)+factor(col), d6a)) # table 4
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>             Df  Sum Sq Mean Sq F value   Pr(>F)   
-#> trt          3 21531.6  7177.2 20.8577 0.001416 **
-#> factor(row)  3 12055.3  4018.4 11.6779 0.006452 **
-#> factor(col)  3  3989.2  1329.7  3.8644 0.074784 . 
-#> Residuals    6  2064.6   344.1                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# 1925 quantitative
-d5 <- subset(dat, year=='1925b')
-libs(desplot)
-desplot(d5, yield ~ col*row,
-        out1=block, text=trt, cex=1, # aspect unknown
-        main="eden.potato: 1925 quantitative")
-
    
-# Trt 't' not defined, seems to be the same as 'a'
-libs(lattice)
-dotplot(trt~yield|block, d5,
-        # aspect unknown
-        main="eden.potato: 1925 quantitative")
-
    anova(m5 <- aov(yield~trt+block, d5)) # table 6
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> trt       11 464251   42205  40.623 5.534e-16 ***
-#> block      3  22030    7343   7.068 0.0008496 ***
-#> Residuals 33  34285    1039                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# 1926 quantitative
-d6 <- subset(dat, year=='1926b')
-libs(desplot)
-desplot(d6, yield ~ col*row,
-        out1=block, text=trt, cex=1, # aspect unknown
-        main="eden.potato: 1926 quantitative")
-
    anova(m6 <- aov(yield~trt+block, d6)) # table 7
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> trt       15 261497 17433.2  8.0575 2.243e-08 ***
-#> block      3  11303  3767.7  1.7414    0.1721    
-#> Residuals 45  97361  2163.6                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# 1927 qualitative + quantitative
-d7 <- droplevels(subset(dat, year==1927))
-libs(desplot)
-desplot(d7, yield ~ col*row,
-        out1=block, text=trt, cex=1, col=ptype, # aspect unknown
-      main="eden.potato: 1927 qualitative + quantitative")
-
    
-# Table 8.  Anova, mean yield tons / acre
-anova(m7 <- aov(yield~trt+block+ptype + ptype:potash, d7))
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> trt           8  49929  6241.1 11.4674 1.048e-09 ***
-#> block         8  22721  2840.2  5.2185 5.708e-05 ***
-#> ptype         2  14461  7230.5 13.2853 1.673e-05 ***
-#> ptype:potash  2   1007   503.5  0.9251    0.4021    
-#> Residuals    60  32655   544.2                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    libs(reshape2)
-me7 <- melt(d7, measure.vars='yield')
-acast(me7, potash~nitro, fun=mean) * 40/2240 # English ton = 2240 pounds
-
    #>          0        2        4
-#> 0 6.544643 7.060516 7.157738
-#> 2 6.513889 7.531746 7.357143
-#> 4 6.193452 7.215278 7.435516
    acast(me7, potash~ptype, fun=mean) * 40/2240
-
    #>          M        N        P        S
-#> 0      NaN 6.920966      NaN      NaN
-#> 2 7.163690      NaN 6.856151 7.382937
-#> 4 7.037698      NaN 6.458333 7.348214
    
-
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    - - -
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    Developed by Kevin Wright.

    -
    - -
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    Site built with pkgdown 1.6.1.

    -
    - -
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    - - - - - - - - diff --git a/docs/reference/eden.tea.uniformity-1.png b/docs/reference/eden.tea.uniformity-1.png deleted file mode 100644 index 98da0942..00000000 Binary files a/docs/reference/eden.tea.uniformity-1.png and /dev/null differ diff --git a/docs/reference/eden.tea.uniformity.html b/docs/reference/eden.tea.uniformity.html deleted file mode 100644 index 1f389ac1..00000000 --- a/docs/reference/eden.tea.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trial of tea — eden.tea.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of tea

    - - -
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    -

    Uniformity trial of tea in Ceylon.

    -
    - - 
    data("eden.tea.uniformity")
    - - -

    Format

    - -

    A data frame with 144 observations on the following 4 variables.

    -
    entry

    entry number

    -
    yield

    yield

    -
    row

    row

    -
    col

    column

    - - - -

    Details

    - -

    Tea plucking in Ceylon extended from 20 Apr 1928 to 10 Dec 1929. There were 42 pluckings.

    -

    The field was divided into 144 plots of 1/72 acre = 605 sq ft. - Each plot contained 6 rows of bushes, approximately 42 bushes. (Each row was thus about 7 bushes).

    -

    Plots in row 12 were at high on a hillside, plots in row 1 were low on the hill.

    -

    Note: We will assume the plots are roughly square: 6 rows of 7 bushes.

    -

    Field width: 12 plots * 24.6 feet = 295 feet

    -

    Field length: 12 plots * 24.6 feet = 295 feet

    -

    Source

    - -

    T. Eden. (1931). - Studies in the yield of tea. 1. The experimental errors of field - experiments with tea. - Agricultural Science, 21, 547-573. - https://doi.org/10.1017/S0021859600088511

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(eden.tea.uniformity)
-  dat <- eden.tea.uniformity
-
-  # sum(dat$yield) # 140050.6 matches total yield in appendix A
-  # mean(dat$yield) # 972.574 match page 5554
-
-  m1 <- aov(yield ~ factor(entry) + factor(row) + factor(col), data=dat)
-  summary(m1)
    #>                Df  Sum Sq Mean Sq F value   Pr(>F)    
-#> factor(entry)  11  349240   31749   1.365    0.200    
-#> factor(row)    11 1293253  117568   5.054 2.38e-06 ***
-#> factor(col)    11  335452   30496   1.311    0.228    
-#> Residuals     110 2558996   23264                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          aspect=1,
-          main="eden.tea.uniformity")
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/edwards.oats-1.png b/docs/reference/edwards.oats-1.png deleted file mode 100644 index 80ab6908..00000000 Binary files a/docs/reference/edwards.oats-1.png and /dev/null differ diff --git a/docs/reference/edwards.oats-2.png b/docs/reference/edwards.oats-2.png deleted file mode 100644 index f3f3f7a5..00000000 Binary files a/docs/reference/edwards.oats-2.png and /dev/null differ diff --git a/docs/reference/edwards.oats.html b/docs/reference/edwards.oats.html deleted file mode 100644 index b1b613a0..00000000 --- a/docs/reference/edwards.oats.html +++ /dev/null @@ -1,265 +0,0 @@ - - - - - - - - -Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Multi-environment trial of oats in United States, 5 locations, 7 years.

    - - -
    - -
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    Multi-environment trial of oats in 5 locations, 7 years, with 3 - replicates in each trial.

    -
    - - 
    data("edwards.oats")
    - - -

    Format

    - -

    A data frame with 3694 observations on the following 7 variables.

    -
    eid

    Environment identification (factor)

    -
    year

    Year

    -
    loc

    Location name

    -
    block

    Block

    -
    gen

    Genotype name

    -
    yield

    Yield

    -
    testwt

    Test weight

    - -
    - -

    Details

    - -

    This data comes from a breeding program, but does not have the usual - pattern of (1) genotypes entering/leaving the program (2) check - genotypes that remain throughout the duration of the program.

    -

    Experiments were conducted by the Iowa State University Oat Variety - Trial in the years 1997 to 2003.

    -

    In each year there were 40 genotypes, with about 30 released checks - and 10 experimental lines. Each genotype appeared in a range of 3 to - 34 of the year-loc combinations.

    -

    The trials were grown in five locations in Iowa: - Ames, Nashua, Crawfordsville, Lewis, Sutherland. - In 1998 there was no trial grown at Sutherland. - There were 3 blocks in each trial.

    -

    Five genotypes were removed from the data because of low yields (and are not included here).

    -

    The environment identifaction values are the same as in Edwards (2006) - table 1.

    -

    Electronic data supplied by Jode Edwards.

    -

    Source

    - -

    Jode W. Edwards, Jean-Luc Jannink (2006). - Bayesian Modeling of Heterogeneous Error and Genotype x Environment Interaction Variances. - Crop Science, 46, 820-833. - https://dx.doi.org/10.2135/cropsci2005.0164

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  libs(dplyr,lattice, reshape2, stringr)
-  data(edwards.oats)
-  dat <- edwards.oats
-  dat$env <- paste0(dat$year,".",dat$loc)
-  dat$eid <- factor(dat$eid)
-  mat <- reshape2::acast(dat, env ~ gen,
-                         fun.aggregate=mean, value.var="yield", na.rm=TRUE)
-  lattice::levelplot(mat, aspect="m",
-                     main="edwards.oats",
-                     xlab="environment", ylab="genotype",
-                     scales=list(x=list(rot=90)))
-
    
-  # Calculate BLUEs of gen/env effects
-  m1 <- lm(yield ~ gen+eid, dat)
-
-  gg <- coef(m1)[2:80]
-  names(gg) <- stringr::str_replace(names(gg), "gen", "")
-  gg <- c(0,gg)
-  names(gg)[1] <- "ACStewart"
-
-  ee <- coef(m1)[81:113]
-  names(ee) <- stringr::str_replace(names(ee), "eid", "")
-  ee <- c(0,ee)
-  names(ee)[1] <- "1"
-  
-  # Subtract gen/env coefs from yield values
-  dat2 <- dat
-  dat2$gencoef <- gg[match(dat2$gen, names(gg))]
-  dat2$envcoef <- ee[match(dat2$eid, names(ee))]
-  dat2 <- dplyr::mutate(dat2, y = yield - gencoef - envcoef)
-
-  # Calculate variance for each gen*env. Shape of the graph is vaguely
-  # similar to Fig 2 of Edwards et al (2006), who used a Bayesian model
-  dat2 <- group_by(dat2, gen, eid)
-  dat2sum <- summarize(dat2, stddev = sd(y))
-
    #> `summarise()` regrouping output by 'gen' (override with `.groups` argument)
      bwplot(stddev ~ eid, dat2sum)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/engelstad.nitro-1.png b/docs/reference/engelstad.nitro-1.png deleted file mode 100644 index 9d651bd0..00000000 Binary files a/docs/reference/engelstad.nitro-1.png and /dev/null differ diff --git a/docs/reference/engelstad.nitro-2.png b/docs/reference/engelstad.nitro-2.png deleted file mode 100644 index c9628b4c..00000000 Binary files a/docs/reference/engelstad.nitro-2.png and /dev/null differ diff --git a/docs/reference/engelstad.nitro.html b/docs/reference/engelstad.nitro.html deleted file mode 100644 index bebcfbdf..00000000 --- a/docs/reference/engelstad.nitro.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn with nitrogen fertilizer

    - - -
    - -
    -

    Corn yield response to nitrogen fertilizer for a single variety of - corn at two locations over five years

    -
    - - - -

    Format

    - -

    A data frame with 60 observations on the following 4 variables.

    -
    loc

    location, 2 levels

    -
    year

    year, 1962-1966

    -
    nitro

    nitrogen fertilizer kg/ha

    -
    yield

    yield, quintals/ha

    - - - -

    Details

    - -

    Corn yield response to nitrogen fertilizer for a single variety of - corn at two locations in Tennessee over five years. - The yield data is the mean of 9 replicates. The original - paper fits quadratic curves to the data. Schabenberger and Pierce fit - multiple models including linear plateau. The example below fits a - quadratic plateau for one year/loc. In the original paper, the 1965 - and 1966 data for the Knoxville location was not used as it appeared - that the response due to nitrogen was minimal in 1965 and nonexistant - in 1966. The economic optimum can be found by setting the tangent - equal to the ratio of (fertilizer price)/(grain price).

    -

    Source

    - -

    Engelstad, OP and Parks, WL. 1971. - Variability in Optimum N Rates for Corn. - Agronomy Journal, 63, 21--23.

    -

    References

    - -

    Schabenberger, O. and Pierce, F.J., 2002. - Contemporary statistical models for the plant and soil sciences, - CRC. Page 254-259.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(engelstad.nitro)
-dat <- engelstad.nitro
-
-libs(latticeExtra)
-useOuterStrips(xyplot(yield ~ nitro | factor(year)*loc, dat,
-                      main="engelstad.nitro"))
    
-# Fit a quadratic plateau model to one year/loc
-j62 <- droplevels(subset(dat, loc=="Jackson" & year==1962))
-# ymax is maximum yield, M is the change point, k affects curvature
-m1 <-  nls(yield ~ ymax*(nitro > M) +
-           (ymax - (k/2) * (M-nitro)^2) * (nitro < M),
-           data= j62,
-           start=list(ymax=80, M=150, k=.01))
-
-# Plot the raw data and model
-newdat <- data.frame(nitro=seq(0,max(dat$nitro)))
-p1 <- predict(m1, new=newdat)
-plot(yield ~ nitro, j62)
    lines(p1 ~ newdat$nitro, col="blue")
    title("engelstad.nitro: quadratic plateau at Jackson 1962")
    
-# Optimum nitro level ignoring prices = 225
-coef(m1)['M']
    #>        M 
-#> 225.3404 
    
-# Optimum nitro level using $0.11 for N cost, $1.15 for grain price = 140
-# Set the first derivative equal to N/corn price, k(M-nitro)=.11/1.15
-coef(m1)['M']-(.11/1.15)/coef(m1)['k']
    #>        M 
-#> 140.7837 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/fan.stability-1.png b/docs/reference/fan.stability-1.png deleted file mode 100644 index 9372fc95..00000000 Binary files a/docs/reference/fan.stability-1.png and /dev/null differ diff --git a/docs/reference/fan.stability.html b/docs/reference/fan.stability.html deleted file mode 100644 index e8abadc1..00000000 --- a/docs/reference/fan.stability.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - - -Multi-environment trial of maize hybrids in China — fan.stability • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize hybrids in China

    - - -
    - -
    - -

    Yield of 13 hybrids, grown in 10 locations across 2 years. Conducted - in Yunnan, China.

    - -
    - - -

    Format

    - -

    A data frame with 260 observations on the following 5 variables.

    -
    gen

    genotype

    -
    maturity

    maturity, days

    -
    year

    year

    -
    loc

    location

    -
    yield

    yield, Mg/ha

    -
    - - -

    Details

    - -

    Data are the mean of 3 reps.

    -

    These data were used to conduct a stability analysis of yield.

    - -

    Source

    - -

    Fan, X.M. and Kang, M.S. and Chen, H. and Zhang, Y. and Tan, J. and Xu, C. (2007). - Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. - Agronomy Journal, 99, 220-228. - https://doi.org/10.2134/agronj2006.0144

    -

    Used with permission of Manjit Kang.

    - - -

    Examples

    - 
    
-library(agridat)
-data(fan.stability)
-dat <- fan.stability
-
-dat$env <- factor(paste(dat$loc, dat$year, sep=""))
-libs(lattice)
-dotplot(gen~yield|env, dat, main="fan.stability")
    
-libs(reshape2, agricolae)
-dm <- acast(dat, gen~env, value.var='yield')
-# Use 0.464 as pooled error from ANOVA.  Calculate yield mean/stability.
-stability.par(dm, rep=3, MSerror=0.464) # Table 5 of Fan et al.
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/federer.diagcheck-1.png b/docs/reference/federer.diagcheck-1.png deleted file mode 100644 index 3fbb0158..00000000 Binary files a/docs/reference/federer.diagcheck-1.png and /dev/null differ diff --git a/docs/reference/federer.diagcheck.html b/docs/reference/federer.diagcheck.html deleted file mode 100644 index 771a492f..00000000 --- a/docs/reference/federer.diagcheck.html +++ /dev/null @@ -1,391 +0,0 @@ - - - - - - - - -Wheat experiment with diagonal checks — federer.diagcheck • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Wheat experiment with diagonal checks

    - - -
    - -
    -

    Wheat experiment augmented with two check varieties in diagonal strips.

    -
    - - - -

    Format

    - -

    A data frame with 180 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype, 120 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    This experiment was conducted by Matthew Reynolds, CIMMYT. There are - 180 plots in the field, 60 for the diagonal checks (G121 and G122) and - 120 for new varieties.

    -

    Federer used this data in multiple papers to illustrate the use of - orthogonal polynomials to model field trends that are not related to - the genetic effects.

    -

    Note: Federer and Wolfinger (2003) provide a SAS program for analysis - of this data. However, when the SAS program is used to analyze this - data, the results do not match the results given in Federer (1998) nor - Federer and Wolfinger (2003). The differences are slight, which - suggests a typographical error in the presentation of the data.

    -

    The R code below provides results that are consistent with the SAS - code of Federer & Wolfinger (2003) when both are applied to this - version of the data.

    -

    Plot dimensions are not given.

    -

    Source

    - -

    Federer, Walter T. 1998. Recovery of interblock, intergradient, and - intervariety information in incomplete block and lattice rectangle - design experiments. Biometrics, 54, 471--481. - https://doi.org/10.2307/3109756

    -

    References

    - -

    Walter T Federer and Russell D Wolfinger, 2003. - Augmented Row-Column Design and Trend Analysis, chapter 28 of - Handbook of Formulas and Software for Plant Geneticists - and Breeders, Haworth Press.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(federer.diagcheck)
-  dat <- federer.diagcheck
-  dat$check <- ifelse(dat$gen == "G121" | dat$gen=="G122", "C","N")
-
-  # Show the layout as in Federer 1998.
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          text=gen, show.key=FALSE, # aspect unknown
-          shorten='no', col=check, cex=.8, col.text=c("yellow","gray"),
-          main="federer.diagcheck")
-
    
-
-  # Now reproduce the analysis of Federer 2003.
-  
-  # Only to match SAS results
-  dat$row <- 16 - dat$row
-  dat <- dat[order(dat$col, dat$row), ]
-  
-  # Add row / column polynomials to the data.
-  # The scaling factors sqrt() are arbitrary, but used to match SAS
-  nr <- length(unique(dat$row))
-  nc <- length(unique(dat$col))
-  rpoly <- poly(dat$row, degree=10) * sqrt(nc)
-  cpoly <- poly(dat$col, degree=10) * sqrt(nr)
-  dat <- transform(dat,
-                   c1 = cpoly[,1], c2 = cpoly[,2], c3 = cpoly[,3],
-                   c4 = cpoly[,4], c6 = cpoly[,6], c8 = cpoly[,8],
-                   r1 = rpoly[,1], r2 = rpoly[,2], r3 = rpoly[,3],
-                   r4 = rpoly[,4], r8 = rpoly[,8], r10 = rpoly[,10])
-  dat$trtn <- ifelse(dat$gen == "G121" | dat$gen=="G122", dat$gen, "G999")
-  dat$new <- ifelse(dat$gen == "G121" | dat$gen=="G122", "N", "Y")
-  dat <- transform(dat, trtn=factor(trtn), new=factor(new))
-  
-  m1 <- lm(yield ~ c1 + c2 + c3 + c4 + c6 + c8
-           + r1 + r2 + r4 + r8 + r10
-           + c1:r1 + c2:r1 + c3:r1 + gen, data = dat)
-  # To get Type III SS use the following
-  # libs(car)
-  # car::Anova(m1, type=3) # Matches PROC GLM output
-  ##                Sum Sq  Df  F value    Pr(>F)    
-  ## (Intercept)  538948   1 159.5804 3.103e-16 ***
-  ## c1            13781   1   4.0806 0.0494940 *  
-  ## c2            51102   1  15.1312 0.0003354 ***
-  ## c3            45735   1  13.5419 0.0006332 ***
-  ## c4            24670   1   7.3048 0.0097349 ** 
-  ## ...
-  
-  
-  # lmer
-  libs(lme4,lucid)
-
-  # "group" for all data
-  dat$one <- factor(rep(1, nrow(dat)))
-  
-  # lmer with bobyqa (default)
-  m2b <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 +
-                                c1 + c2 + c3 + c4 + c6 +
-                                c8 + r1:c1 + r1:c2 + r1:c3 || one) +
-                (1|new:gen),
-              data = dat,
-              control=lmerControl(check.nlev.gtr.1="ignore"))
-
    #> boundary (singular) fit: see ?isSingular
      vc(m2b)
-
    #>       grp        var1 var2     vcov  sdcor
-#>   new.gen (Intercept) <NA>   2870    53.57
-#>       one       r1:c3 <NA>   5534    74.39
-#>     one.1       r1:c2 <NA>  58250   241.4 
-#>     one.2       r1:c1 <NA> 128000   357.8 
-#>     one.3          c8 <NA>   6458    80.36
-#>     one.4          c6 <NA>   1400    37.41
-#>     one.5          c4 <NA>   1792    42.33
-#>     one.6          c3 <NA>   2549    50.48
-#>     one.7          c2 <NA>   5942    77.08
-#>     one.8          c1 <NA>      0     0   
-#>     one.9         r10 <NA>   1133    33.66
-#>    one.10          r8 <NA>   1355    36.81
-#>    one.11          r4 <NA>   2269    47.63
-#>    one.12          r2 <NA>    241.9  15.55
-#>    one.13          r1 <NA>   9203    95.93
-#>  Residual        <NA> <NA>   4412    66.42
      ##      grp        var1 var2     vcov  sdcor
-  ##  new.gen (Intercept) <NA>   2869    53.57
-  ##      one       r1:c3 <NA>   5532    74.37
-  ##    one.1       r1:c2 <NA>  58230   241.3
-  ##    one.2       r1:c1 <NA> 128000   357.8
-  ##    one.3          c8 <NA>   6456    80.35
-  ##    one.4          c6 <NA>   1400    37.41
-  ##    one.5          c4 <NA>   1792    42.33
-  ##    one.6          c3 <NA>   2549    50.49
-  ##    one.7          c2 <NA>   5942    77.08
-  ##    one.8          c1 <NA>      0     0
-  ##    one.9         r10 <NA>   1133    33.66
-  ##   one.10          r8 <NA>   1355    36.81
-  ##   one.11          r4 <NA>   2269    47.63
-  ##   one.12          r2 <NA>    241.8  15.55
-  ##   one.13          r1 <NA>   9200    95.92
-  ## Residual        <NA> <NA>   4412    66.42
-  
-  # lmer with Nelder_Mead gives 'wrong' results
-  ## m2n <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 +
-  ##             c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 || one) +
-  ##             (1|new:gen)
-  ##             , data = dat,
-  ##             control=lmerControl(optimizer="Nelder_Mead",
-  ##                                 check.nlev.gtr.1="ignore"))
-  ## vc(m2n)
-  ##    groups        name variance   stddev
-  ##  new.gen  (Intercept)   3228    56.82
-  ##  one      r1:c3         7688    87.68
-  ##  one.1    r1:c2        69750   264.1
-  ##  one.2    r1:c1       107400   327.8
-  ##  one.3    c8            6787    82.38
-  ##  one.4    c6            1636    40.45
-  ##  one.5    c4           12270   110.8
-  ##  one.6    c3            2686    51.83
-  ##  one.7    c2            7645    87.43
-  ##  one.8    c1               0     0.0351
-  ##  one.9    r10           1976    44.45
-  ##  one.10   r8            1241    35.23
-  ##  one.11   r4            2811    53.02
-  ##  one.12   r2             928.2  30.47
-  ##  one.13   r1           10360   101.8
-  ##  Residual               4127    64.24
-  
-  libs(asreml,lucid) # asreml4
-    
-  m3 <- asreml(yield ~ -1 + trtn, data=dat,
-               random = ~ r1 + r2 + r4 + r8 + r10 +
-                 c1 + c2 + c3 + c4 + c6 + c8 +
-                 r1:c1 + r1:c2 + r1:c3 + new:gen)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:36 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -894.584      7526.086    177 17:08:36    0.0 (2 restrained)
-#>  2      -889.375      6662.270    177 17:08:36    0.0 (2 restrained)
-#>  3      -884.552      5769.453    177 17:08:36    0.0 (2 restrained)
-#>  4      -881.358      5118.056    177 17:08:36    0.0 (1 restrained)
-#>  5      -879.668      4685.628    177 17:08:36    0.0
-#>  6      -879.163      4511.141    177 17:08:36    0.0
-#>  7      -879.061      4442.917    177 17:08:36    0.0
-#>  8      -879.050      4420.492    177 17:08:36    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      ## coef(m3)
-  ## # REML cultivar means.  Very similar to Federer table 2.
-  ## rev(sort(round(coef(m3)$fixed[3] + coef(m3)$random[137:256,],0)))
-  ## ## gen_G060 gen_G021 gen_G011 gen_G099 gen_G002
-  ## ##      974      949      945      944      942
-  ## ## gen_G118 gen_G058 gen_G035 gen_G111 gen_G120
-  ## ##      938      937      937      933      932
-  ## ## gen_G046 gen_G061 gen_G082 gen_G038 gen_G090
-  ## ##      932      931      927      927      926
-
-  ## vc(m3)
-  ## ##           effect component std.error z.ratio constr
-  ## ##        r1!r1.var   9201        13720    0.67    pos
-  ## ##        r2!r2.var    241.7       1059    0.23    pos
-  ## ##        r4!r4.var   2269         3915    0.58    pos
-  ## ##        r8!r8.var   1355         2627    0.52    pos
-  ## ##      r10!r10.var   1133         2312    0.49    pos
-  ## ##        c1!c1.var      0.01         0    4.8   bound
-  ## ##        c2!c2.var   5942         8969    0.66    pos
-  ## ##        c3!c3.var   2549         4177    0.61    pos
-  ## ##        c4!c4.var   1792         3106    0.58    pos
-  ## ##        c6!c6.var   1400         2551    0.55    pos
-  ## ##        c8!c8.var   6456         9702    0.67    pos
-  ## ##     r1:c1!r1.var 128000       189700    0.67    pos
-  ## ##     r1:c2!r1.var  58230        90820    0.64    pos
-  ## ##     r1:c3!r1.var   5531        16550    0.33    pos
-  ## ##  new:gen!new.var   2869         1367    2.1     pos
-  ## ##       R!variance   4412          915    4.8     pos
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/federer.tobacco-1.png b/docs/reference/federer.tobacco-1.png deleted file mode 100644 index 909fb0b9..00000000 Binary files a/docs/reference/federer.tobacco-1.png and /dev/null differ diff --git a/docs/reference/federer.tobacco.html b/docs/reference/federer.tobacco.html deleted file mode 100644 index e387e360..00000000 --- a/docs/reference/federer.tobacco.html +++ /dev/null @@ -1,261 +0,0 @@ - - - - - - - - -RCB of tobacco, height plants exposed to radiation — federer.tobacco • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB of tobacco, height plants exposed to radiation

    - - -
    - -
    -

    RCB of tobacco, height plants exposed to radiation

    -
    - - - -

    Format

    - -

    A data frame with 56 observations on the following 4 variables.

    -
    row

    row

    -
    block

    block, numeric

    -
    dose

    radiation dose, roentgens

    -
    height

    height of 20 plants, cm

    - -
    - -

    Details

    - -

    An experiment conducted in 1951 and described in Federer (1954). The - treatment involved exposing tobacco seeds to seven different doses of - radiation. The seedlings were transplanted to the field in an RCB - experiment with 7 treatments in 8 blocks. The physical layout of the - experiment was in 8 rows and 7 columns.

    -

    Shortly after the plants were transplanted to the field it became - apparent that an environmental gradient existed. The response variable - was the total height (centimeters) of 20 plants.

    -

    Source

    - -

    Walter T Federer and C S Schlottfeldt, 1954. - The use of covariance to control gradients in experiments. - Biometrics, 10, 282--290. - https://doi.org/10.2307/3001881

    -

    References

    - -

    R. D. Cook and S. Weisberg (1999). - Applied Regression Including Computing and Graphics.

    -

    Walter T Federer and Russell D Wolfinger, 2003. - PROC GLM and PROC MIXED Codes for Trend Analyses for - Row-Column Designed Experiments, - Handbook of Formulas and Software for Plant Geneticists and - Breeders, Haworth Press.

    -

    Paul N Hinz, (1987). - Nearest-Neighbor Analysis in Practice, - Iowa State Journal of Research, 62, 199--217. - https://lib.dr.iastate.edu/iowastatejournalofresearch/vol62/iss2/1

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(federer.tobacco)
-dat <- federer.tobacco
-
-# RCB analysis.  Treatment factor not signficant.
-dat <- transform(dat, dosef=factor(dose), rowf=factor(row),
-                 blockf=factor(block))
-m1 <- lm(height ~ blockf + dosef, data=dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: height
-#>           Df  Sum Sq Mean Sq F value Pr(>F)
-#> blockf     7  388315   55474  1.8352 0.1056
-#> dosef      6  273875   45646  1.5100 0.1985
-#> Residuals 42 1269586   30228               
    
-# RCB residuals show strong spatial trends
-libs(desplot)
-dat$resid <- resid(m1)
-desplot(dat, resid ~ row * block,
-        # aspect unknown
-        main="federer.tobacco")
-
    
-
-# Row-column analysis.  Treatment now significant
-m2 <- lm(height ~ rowf + blockf + dosef, data=dat)
-anova(m2)
-
    #> Analysis of Variance Table
-#> 
-#> Response: height
-#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> rowf       6 1159072  193179 26.2763 9.065e-12 ***
-#> blockf     7  388315   55474  7.5455 1.355e-05 ***
-#> dosef      6  119724   19954  2.7141   0.02806 *  
-#> Residuals 36  264666    7352                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
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    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
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    - - - - - - - - diff --git a/docs/reference/figures/agridat_full_logo.png b/docs/reference/figures/agridat_full_logo.png deleted file mode 100644 index 95da155c..00000000 Binary files a/docs/reference/figures/agridat_full_logo.png and /dev/null differ diff --git a/docs/reference/figures/baker.barley.uniformity.png b/docs/reference/figures/baker.barley.uniformity.png deleted file mode 100644 index 73040526..00000000 Binary files a/docs/reference/figures/baker.barley.uniformity.png and /dev/null differ diff --git a/docs/reference/figures/logo.png b/docs/reference/figures/logo.png deleted file mode 100644 index 10c94c2a..00000000 Binary files a/docs/reference/figures/logo.png and /dev/null differ diff --git a/docs/reference/figures/stroupsplitplot.pdf b/docs/reference/figures/stroupsplitplot.pdf deleted file mode 100644 index cb72af3c..00000000 Binary files a/docs/reference/figures/stroupsplitplot.pdf and /dev/null differ diff --git a/docs/reference/figures/stroupsplitplot.png b/docs/reference/figures/stroupsplitplot.png deleted file mode 100644 index 92d97b51..00000000 Binary files a/docs/reference/figures/stroupsplitplot.png and /dev/null differ diff --git a/docs/reference/fisher.barley-1.png b/docs/reference/fisher.barley-1.png deleted file mode 100644 index be3e1e88..00000000 Binary files a/docs/reference/fisher.barley-1.png and /dev/null differ diff --git a/docs/reference/fisher.barley.html b/docs/reference/fisher.barley.html deleted file mode 100644 index 5665c77c..00000000 --- a/docs/reference/fisher.barley.html +++ /dev/null @@ -1,357 +0,0 @@ - - - - - - - - -Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Multi-environment trial of 5 barley varieties, 6 locations, 2 years

    - - -
    - -
    -

    Multi-environment trial of 5 barley varieties, 6 locations, 2 years

    -
    - - 
    data("fisher.barley")
    - - -

    Format

    - -

    A data frame with 60 observations on the following 4 variables.

    -
    yield

    yield, bu/ac

    -
    gen

    genotype/variety, 5 levels

    -
    env

    environment/location, 2 levels

    -
    year

    year, 1931/1932

    - -
    - -

    Details

    - -

    Trials of 5 varieties of barley were conducted at 6 stations in - Minnesota during the years 1931-1932.

    -

    This is a subset of Immer's barley data. The yield values here are - totals of 3 reps (Immer gave the average yield of 3 reps).

    -

    Source

    - - - -

    Ronald Fisher (1935). - The Design of Experiments.

    -

    References

    - - - -

    George Fernandez (1991). - Analysis of Genotype x Environment Interaction by Stability Estimates. - Hort Science, 26, 947-950.

    -

    F. Yates & W. G. Cochran (1938). - The Analysis of Groups of Experiments. - Journal of Agricultural Science, 28, 556-580, table 1. - https://doi.org/10.1017/S0021859600050978

    -

    G. K. Shukla, 1972. - Some statistical aspects of partitioning of genotype-environmental - components of variability. - Heredity, 29, 237-245. Table 1. - https://doi.org/10.1038/hdy.1972.87

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(fisher.barley)
-  dat <- fisher.barley
-
-  libs(dplyr,lattice)
-
    #> 
-#> Attaching package: 'dplyr'
    #> The following object is masked from 'package:nlme':
-#> 
-#>     collapse
    #> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
    #> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
      # Yates 1938 figure 1. Regression on env mean
-  # Sum years within loc
-  dat2 <- aggregate(yield ~ gen + env, data=dat, FUN=sum)
-  # Avg within env
-  emn <- aggregate(yield ~ env, data=dat2, FUN=mean)
-  dat2$envmn <- emn$yield[match(dat2$env, emn$env)]
-  xyplot(yield ~ envmn, dat2, group=gen, type=c('p','r'),
-         main="fisher.barley - stability regression",
-         xlab="Environment total", ylab="Variety mean",
-         auto.key=list(columns=3))
-
    
-
-  if(0){
-    # calculate stability according to the sum-of-squares approach used by
-    # Shukla (1972), eqn 11. match to Shukla, Table 4, M.S. column
-    # also matches fernandez, table 3, stabvar column
-    libs(dplyr)
-    dat2 <- dat
-    dat2 <- group_by(dat2, gen,env)
-    dat2 <- summarize(dat2, yield=sum(yield)) # means across years
-    dat2 <- group_by(dat2, env)
-    dat2 <- mutate(dat2, envmn=mean(yield)) # env means
-    dat2 <- group_by(dat2, gen)
-    dat2 <- mutate(dat2, genmn=mean(yield)) # gen means
-    dat2 <- ungroup(dat2)
-    dat2 <- mutate(dat2, grandmn=mean(yield)) # grand mean
-    # correction factor overall
-    dat2 <- mutate(dat2, cf = sum((yield - genmn - envmn + grandmn)^2))
-    t=5; s=6 # t genotypes, s environments
-    dat2 <- group_by(dat2, gen)
-    dat2 <- mutate(dat2, ss=sum((yield-genmn-envmn+grandmn)^2))
-    # divide by 6 to scale down to plot-level
-    dat2 <- mutate(dat2, sig2i = 1/((s-1)*(t-1)*(t-2)) * (t*(t-1)*ss-cf)/6)
-    dat2[!duplicated(dat2$gen),c('gen','sig2i')]    
-    ##       <chr>     <dbl>
-    ## 1 Manchuria  25.87912
-    ## 2  Peatland  75.68001
-    ## 3  Svansota  19.59984
-    ## 4     Trebi 225.52866
-    ## 5    Velvet  22.73051
-  }
-
-
-  libs(asreml,lucid) # asreml3
-  # mixed model approach gives similar results (but not identical)
-
-  dat2 <- dat
-  dat2 <- dplyr::group_by(dat2, gen,env)
-  dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years
-
    #> `summarise()` regrouping output by 'gen' (override with `.groups` argument)
      dat2 <- dat2[order(dat2$gen),]
-  
-  # G-side
-  m1g <- asreml(yield ~ gen, data=dat2,
-                random = ~ env + at(gen):units,
-                family=asr_gaussian(dispersion=1.0))
-
    #> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -141.646           1.0     25 17:08:37    0.0
-#>  2      -128.547           1.0     25 17:08:37    0.0
-#>  3      -114.150           1.0     25 17:08:37    0.0
-#>  4      -104.133           1.0     25 17:08:37    0.0
-#>  5       -98.582           1.0     25 17:08:37    0.0
-#>  6       -96.781           1.0     25 17:08:37    0.0
-#>  7       -96.398           1.0     25 17:08:37    0.0
-#>  8       -96.355           1.0     25 17:08:37    0.0
-#>  9       -96.349           1.0     25 17:08:37    0.0
-#> 10       -96.348           1.0     25 17:08:37    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m1g <- update(m1g)
-
    #> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -96.3476           1.0     25 17:08:37    0.0
-#>  2      -96.3476           1.0     25 17:08:37    0.0
      summary(m1g)$varcomp[-1,1:2]/6
-
    #>                            component std.error
-#> at(gen, Manchuria):units  33.8145031  27.22721
-#> at(gen, Peatland):units   70.4489092  50.52680
-#> at(gen, Svansota):units   25.2728568  21.92919
-#> at(gen, Trebi):units     231.6981702 150.80464
-#> at(gen, Velvet):units     13.9325646  16.58571
-#> units!R                    0.1666667        NA
      #                            component std.error
-  # at(gen, Manchuria):units  33.8145031  27.22721
-  # at(gen, Peatland):units   70.4489092  50.52680
-  # at(gen, Svansota):units   25.2728568  21.92919
-  # at(gen, Trebi):units     231.6981702 150.80464
-  # at(gen, Velvet):units     13.9325646  16.58571
-  # units!R                    0.1666667        NA
-
-  # R-side estimates = G-side estimate + 0.1666 (resid variance)
-  m1r <- asreml(yield ~ gen, data=dat2,
-                random = ~ env,
-                residual = ~ dsum( ~ units|gen))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -106.401           1.0     25 17:08:37    0.0 (1 restrained)
-#>  2      -102.077           1.0     25 17:08:37    0.0
-#>  3      -100.377           1.0     25 17:08:37    0.0
-#>  4       -98.882           1.0     25 17:08:37    0.0
-#>  5       -97.150           1.0     25 17:08:37    0.0
-#>  6       -96.577           1.0     25 17:08:37    0.0
-#>  7       -96.413           1.0     25 17:08:37    0.0
-#>  8       -96.363           1.0     25 17:08:37    0.0
-#>  9       -96.351           1.0     25 17:08:37    0.0
-#> 10       -96.348           1.0     25 17:08:37    0.0
-#> 11       -96.348           1.0     25 17:08:37    0.0
      m1r <- update(m1r)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -96.3476           1.0     25 17:08:37    0.0
-#>  2      -96.3476           1.0     25 17:08:37    0.0
      summary(m1r)$varcomp[-1,1:2]/6
-
    #>                 component std.error
-#> gen_Manchuria!R  34.00058  27.24871
-#> gen_Peatland!R   70.65501  50.58925
-#> gen_Svansota!R   25.42022  21.88606
-#> gen_Trebi!R     231.85846 150.78756
-#> gen_Velvet!R     14.08405  16.55558
      #                     component std.error
-  # gen_Manchuria!R  34.00058  27.24871
-  # gen_Peatland!R   70.65501  50.58925
-  # gen_Svansota!R   25.42022  21.88606
-  # gen_Trebi!R     231.85846 150.78756
-  # gen_Velvet!R     14.08405  16.55558
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/fisher.latin.html b/docs/reference/fisher.latin.html deleted file mode 100644 index fbc1ccce..00000000 --- a/docs/reference/fisher.latin.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Latin square experiment on mangolds — fisher.latin • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Latin square experiment on mangolds

    - - -
    - -
    - -

    Latin square experiment on mangolds. Used by R. A. Fisher.

    - -
    - - 
    data("fisher.latin")
    - -

    Format

    - -

    A data frame with 25 observations on the following 4 variables.

    -
    trt

    treatment factor, 5 levels

    -
    yield

    yield

    -
    row

    row

    -
    col

    column

    -
    - - -

    Details

    - -

    Yields are root weights. Data originally collected by Mercer and - Hall as part of a uniformity trial.

    -

    This data is the same as the data from columns 1-5, rows 16-20, of the - mercer.mangold.uniformity data in this package.

    -

    Unsurprisingly, there are no significant treatment differences.

    - -

    Source

    - -

    Mercer, WB and Hall, AD, 1911. - The experimental error of field trials - The Journal of Agricultural Science, 4, 107-132. Table 1. - http::/doi.org/10.1017/S002185960000160X

    -

    R. A. Fisher. Statistical Methods for Research Workers.

    - - -

    Examples

    - 
    
-library(agridat)
-
-data(fisher.latin)
-dat <- fisher.latin
-
-# Standard latin-square analysis
-m1 <- lm(yield ~ trt + factor(row) + factor(col), data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>             Df Sum Sq Mean Sq F value   Pr(>F)   
-#> trt          4  330.2   82.56  0.5647 0.692978   
-#> factor(row)  4 4240.2 1060.06  7.2511 0.003294 **
-#> factor(col)  4  701.8  175.46  1.2002 0.360412   
-#> Residuals   12 1754.3  146.19                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/foulley.calving-1.png b/docs/reference/foulley.calving-1.png deleted file mode 100644 index 9043e34e..00000000 Binary files a/docs/reference/foulley.calving-1.png and /dev/null differ diff --git a/docs/reference/foulley.calving.html b/docs/reference/foulley.calving.html deleted file mode 100644 index 6e969f0b..00000000 --- a/docs/reference/foulley.calving.html +++ /dev/null @@ -1,322 +0,0 @@ - - - - - - - - -Calving difficulty by calf sex and age of dam — foulley.calving • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Calving difficulty by calf sex and age of dam

    - - -
    - -
    -

    Calving difficulty by calf sex and age of dam

    -
    - - 
    data("foulley.calving")
    - - -

    Format

    - -

    A data frame with 54 observations on the following 4 variables.

    -
    sex

    calf gender

    -
    age

    dam age factor, 9 levels

    -
    score

    score for birthing difficulty, S1 < S2 < S3

    -
    count

    count of births for each category

    - -
    - -

    Details

    - -

    These data are calving difficulty scores for purebred US Simmental - cows.

    -

    The raw data show that the greatest calving difficulty is for young - dams with male calves. - Differences between male/female calves decreased with age of the dam.

    -

    The goodness of fit can be improved by using a scaling effect for age - of dam.

    -

    Note: The paper by Foulley and Gianola has '21943' as the count for - score 1, F, >8. This data uses '20943' so that the marginal totals - from this data match the marginal totals given in the paper.

    -

    Used with permission of Jean-Louis Foulley.

    -

    Source

    - -

    JL Foulley, D Gianola (1996). - Statistical Analysis of Ordered Categorical Data via a Structured - Heteroskedastic Threshold Model. - Genet Sel Evol, 28, 249--273. - https://doi.org/10.1051/gse:19960304

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(foulley.calving)
-dat <- foulley.calving
-
-## Plot
-d2 <- transform(dat,
-                age=ordered(age, levels=c("0.0-2.0","2.0-2.5","2.5-3.0",
-                                          "3.0-3.5","3.5-4.0",
-                                          "4.0-4.5","4.5-5.0","5.0-8.0","8.0+")),
-                score=ordered(score, levels=c('S1','S2','S3')))
-libs(reshape2)
-d2 <- acast(dat, sex+age~score, value.var='count')
-d2 <- prop.table(d2, margin=1)
-libs(lattice)
-thm <- simpleTheme(col=c('skyblue','gray','pink'))
-barchart(d2, par.settings=thm, main="foulley.calving",
-         xlab="Frequency of calving difficulty", ylab="Calf gender and dam age",
-         auto.key=list(columns=3, text=c("Easy","Assited","Difficult")))
-
    
-
-## Ordinal multinomial model
-
-libs(ordinal)
-
    #> 
-#> Attaching package: 'ordinal'
    #> The following objects are masked from 'package:nlme':
-#> 
-#>     ranef, VarCorr
    #> The following objects are masked from 'package:lme4':
-#> 
-#>     ranef, VarCorr
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     slice
    m2 <- clm(score ~ sex*age, data=dat, weights=count, link='probit')
-summary(m2)
-
    #> formula: score ~ sex * age
-#> data:    dat
-#> 
-#>  link   threshold nobs   logLik     AIC       niter max.grad cond.H 
-#>  probit flexible  363759 -108088.45 216214.90 7(0)  5.12e-12 6.0e+02
-#> 
-#> Coefficients:
-#>                  Estimate Std. Error z value Pr(>|z|)    
-#> sexM             0.500605   0.015178  32.982  < 2e-16 ***
-#> age2.0-2.5      -0.237643   0.013846 -17.163  < 2e-16 ***
-#> age2.5-3.0      -0.681648   0.018894 -36.077  < 2e-16 ***
-#> age3.0-3.5      -0.957138   0.018322 -52.241  < 2e-16 ***
-#> age3.5-4.0      -1.082520   0.024356 -44.446  < 2e-16 ***
-#> age4.0-4.5      -1.146834   0.022496 -50.981  < 2e-16 ***
-#> age4.5-5.0      -1.175312   0.028257 -41.594  < 2e-16 ***
-#> age5.0-8.0      -1.280587   0.016948 -75.559  < 2e-16 ***
-#> age8.0+         -1.323749   0.024079 -54.974  < 2e-16 ***
-#> sexM:age2.0-2.5  0.003035   0.019333   0.157  0.87527    
-#> sexM:age2.5-3.0 -0.076677   0.026106  -2.937  0.00331 ** 
-#> sexM:age3.0-3.5 -0.080657   0.024635  -3.274  0.00106 ** 
-#> sexM:age3.5-4.0 -0.135774   0.032927  -4.124 3.73e-05 ***
-#> sexM:age4.0-4.5 -0.124303   0.029819  -4.169 3.07e-05 ***
-#> sexM:age4.5-5.0 -0.198897   0.038309  -5.192 2.08e-07 ***
-#> sexM:age5.0-8.0 -0.135524   0.022804  -5.943 2.80e-09 ***
-#> sexM:age8.0+    -0.131033   0.031852  -4.114 3.89e-05 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Threshold coefficients:
-#>       Estimate Std. Error z value
-#> S1|S2  0.82504    0.01083   76.15
-#> S2|S3  1.52017    0.01138  133.62
    
-##   Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)    
-## sexM             0.500605   0.015178  32.982  < 2e-16 ***
-## age2.0-2.5      -0.237643   0.013846 -17.163  < 2e-16 ***
-## age2.5-3.0      -0.681648   0.018894 -36.077  < 2e-16 ***
-## age3.0-3.5      -0.957138   0.018322 -52.241  < 2e-16 ***
-## age3.5-4.0      -1.082520   0.024356 -44.446  < 2e-16 ***
-## age4.0-4.5      -1.146834   0.022496 -50.981  < 2e-16 ***
-## age4.5-5.0      -1.175312   0.028257 -41.594  < 2e-16 ***
-## age5.0-8.0      -1.280587   0.016948 -75.559  < 2e-16 ***
-## age8.0+         -1.323749   0.024079 -54.974  < 2e-16 ***
-## sexM:age2.0-2.5  0.003035   0.019333   0.157  0.87527    
-## sexM:age2.5-3.0 -0.076677   0.026106  -2.937  0.00331 ** 
-## sexM:age3.0-3.5 -0.080657   0.024635  -3.274  0.00106 ** 
-## sexM:age3.5-4.0 -0.135774   0.032927  -4.124 3.73e-05 ***
-## sexM:age4.0-4.5 -0.124303   0.029819  -4.169 3.07e-05 ***
-## sexM:age4.5-5.0 -0.198897   0.038309  -5.192 2.08e-07 ***
-## sexM:age5.0-8.0 -0.135524   0.022804  -5.943 2.80e-09 ***
-## sexM:age8.0+    -0.131033   0.031852  -4.114 3.89e-05 ***
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## Threshold coefficients:
-##       Estimate Std. Error z value
-## S1|S2  0.82504    0.01083   76.15
-## S2|S3  1.52017    0.01138  133.62
-
-## Note 1.52017 - 0.82504 = 0.695 matches Foulley's '2-3' threshold estimate
-
-predict(m2) # probability of each category
-
    #> $fit
-#>  [1] 0.627195037 0.711933503 0.860542204 0.913436999 0.938551496 0.944707720
-#>  [7] 0.955306699 0.959118319 0.962397773 0.218838605 0.183177857 0.101747212
-#> [13] 0.066737379 0.048833512 0.044302103 0.036355388 0.033449788 0.030928311
-#> [19] 0.153966358 0.104888640 0.037710585 0.019825622 0.012614993 0.010990177
-#> [25] 0.008337912 0.007431893 0.006673917 0.795325167 0.856036899 0.934054457
-#> [31] 0.962639751 0.971775863 0.975687944 0.977268784 0.982381571 0.984174388
-#> [37] 0.140441286 0.104573858 0.052106611 0.030741447 0.023599423 0.020485574
-#> [43] 0.019216920 0.015069324 0.013597527 0.064233547 0.039389243 0.013838932
-#> [49] 0.006618802 0.004624713 0.003826482 0.003514297 0.002549106 0.002228085
-#> 
    
-
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/fox.wheat-1.png b/docs/reference/fox.wheat-1.png deleted file mode 100644 index 2cb35225..00000000 Binary files a/docs/reference/fox.wheat-1.png and /dev/null differ diff --git a/docs/reference/fox.wheat.html b/docs/reference/fox.wheat.html deleted file mode 100644 index 13015ab4..00000000 --- a/docs/reference/fox.wheat.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat, 22 varieties at 14 sites in Australia

    - - -
    - -
    -

    Wheat yields of 22 varieties at 14 sites in Australia

    -
    - - 
    data("fox.wheat")
    - - -

    Format

    - -

    A data frame with 308 observations on the following 4 variables.

    -
    gen

    genotype/variety factor, 22 levels

    -
    site

    site factor, 14 levels

    -
    yield

    yield, tonnes/ha

    -
    state

    state in Australia

    - - - -

    Details

    - -

    The 1975 Interstate Wheat Variety trial in Australia used RCB design - with 4 blocks, 22 varieties in 14 sites. Wagga is represented twice, by - trials sown in May and June.

    -

    The 22 varieties were a highly selected and represent considerable - genetic diversity with four different groups. - (i) from the University of Sydney: Timson, Songlen, Gamenya. - (ii) widely grown on Mallee soils: Heron and Halberd. - (iii) late maturing varieties from Victoria: Pinnacle, KL-21, JL-157. - (iv) with Mexican parentage: WW-15 and Oxley.

    -

    Source

    - -

    Fox, P.N. and Rathjen, A.J. (1981). - Relationships between sites used in the interstate wheat variety trials. - Australian Journal of Agricultural Research, 32, 691-702.

    -

    Electronic version supplied by Jonathan Godfrey.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(fox.wheat)
-dat <- fox.wheat
-
-# Means of varieties.  Slight differences from Fox and Rathjen suggest
-# they had more decimals of precision than shown.
-tapply(dat$yield, dat$gen, mean)
    #> 64-WO-2-22 64-WO-2-29    8/20/09      Eagle    Gamenya    Halberd      Heron 
-#>   2.046429   2.298571   2.466429   2.053571   1.767857   2.043571   1.861429 
-#>     JL-157      KL-21    MM/68/1   MMC/21/9      Oxley   Pinnacle     QX-333 
-#>   2.167143   2.195000   2.204286   2.338571   2.394286   1.919286   1.822143 
-#>    RAC-185    RAC-255     RAC-88    Songlen     Timson     WW-11B      WW-15 
-#>   2.349286   2.141429   2.299286   1.952143   1.645000   2.172143   2.425000 
-#>    WW-97-2 
-#>   2.077143 
    
-# Calculate genotype means, merge into the data
-genm <- tapply(dat$yield, dat$gen, mean)
-dat$genm <- genm[match(dat$gen, names(genm))]
-
-# Calculate slopes for each site.  Matches Fox, Table 2, Col A.
-m1 <- lm(yield~site+site:genm, data=dat)
-sort(round(coef(m1)[15:28],2), dec=TRUE)
    #> siteLongerenong:genm    siteWaggaMay:genm siteTurretfield:genm 
-#>                 2.67                 2.41                 2.18 
-#>      siteUrania:genm   siteWaggaJune:genm    siteNarrabri:genm 
-#>                 1.80                 1.50                 0.72 
-#>    siteNarrogin:genm     siteWarwick:genm siteWonganHills:genm 
-#>                 0.68                 0.65                 0.46 
-#>     siteNangari:genm     siteWalpeup:genm     siteTheGums:genm 
-#>                 0.36                 0.31                 0.15 
-#>   sitePirrinuan:genm    siteMerredin:genm 
-#>                 0.10                 0.00 
    
-# Figure 1 of Fox
-libs(lattice)
-xyplot(yield~genm|state, data=dat, type=c('p','r'), group=site,
-       auto.key=list(columns=4),
-       main="fox.wheat", xlab="Variety mean across all sites",
-       ylab="Variety yield at each site within states")
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/garber.multi.uniformity-1.png b/docs/reference/garber.multi.uniformity-1.png deleted file mode 100644 index b1c6cdf5..00000000 Binary files a/docs/reference/garber.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/garber.multi.uniformity-2.png b/docs/reference/garber.multi.uniformity-2.png deleted file mode 100644 index a59dfb2e..00000000 Binary files a/docs/reference/garber.multi.uniformity-2.png and /dev/null differ diff --git a/docs/reference/garber.multi.uniformity.html b/docs/reference/garber.multi.uniformity.html deleted file mode 100644 index ea821f1a..00000000 --- a/docs/reference/garber.multi.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trials of oat hay and wheat grain — garber.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Uniformity trials of oat hay and wheat grain

    - - -
    - -
    -

    Uniformity trials of oat hay and wheat grain, at West Virginia - Agricultural Experiment Station, 1923-1924, on the same land.

    -
    - - - -

    Format

    - -

    A data frame with 270 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    oats

    yield of oat hay

    -
    wheat

    yield of wheat grain

    - -
    - -

    Details

    - - - -

    Each plot was 68 feet x 21 feet. After discarding a 3.5 foot border on - all sides, the harvested area was 61 feet x 14 feet. The plots were - laid out in doubles with a 14-foot roadway between the plots.

    -

    Note: The orientation of the plots is a guess.

    -

    Field width: 6 plots * 68 feet + 14 ft/roadway * 2 = 436 feet

    -

    Field length: 45 plots * 21 feet/plot = 945 feet

    -

    In 1923 the field was planted to oats and harvested as hay.

    -

    In 1924 the field was planted to wheat and harvested for grain.

    -

    Garber: "Plots 211 to 214, and 261 to 264, [note, these are rows - 11-14, columns 5-6] inclusive, were eliminated from this study because - of the fact that a few years ago a straw stack had stood on or in the - vicinity...which undoubtedly accounts for the relatively high yields - on plots 261 to 264, inclusive."

    -

    Note: The source document contained mean-subtracted yields. The oat - yield in row 22, column 5 was given as +59.7. This is obviously - incorrect, since the negative yields all end in '.7' and positive - yields all ended in '.3'. We used -59.7 as the centered yield value - and added the mean of 1883.7 to all centered yields to obtain absolute - yields.

    -

    Note: Another paper by these authors, - "A Method of Laying Out Experimental Plats", contains three additional - years of uniformity trials, with each crop expressed as a percentage. - https://archive.org/details/in.ernet.dli.2015.229753?q=method+of+laying+out+experiment+plats

    -

    Source

    - -

    Garber, RJ and Mcllvaine, TC and Hoover, MM. 1926. - A study of soil heterogeneity in experiment plots. - Jour Agr Res, 33, 255-268. Tables 3, 5. - https://naldc.nal.usda.gov/download/IND43967148/PDF

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(garber.multi.uniformity)
-  dat <- garber.multi.uniformity
-
-  libs(desplot)
-  desplot(dat, oats ~ col*row,
-          flip=TRUE, tick=TRUE, aspect=945/436, # true aspect
-          main="garber.multi.uniformity oats")
-
    
-  desplot(dat, wheat ~ col*row,
-          flip=TRUE, tick=TRUE, aspect=945/436, # true aspect
-          main="garber.multi.uniformity wheat")
-
    
-  with(dat, cor(oats, wheat)) # = .37 matches Garber
-
    #> [1] 0.36992
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gartner.corn-1.png b/docs/reference/gartner.corn-1.png deleted file mode 100644 index 5686bb97..00000000 Binary files a/docs/reference/gartner.corn-1.png and /dev/null differ diff --git a/docs/reference/gartner.corn-2.png b/docs/reference/gartner.corn-2.png deleted file mode 100644 index 76596808..00000000 Binary files a/docs/reference/gartner.corn-2.png and /dev/null differ diff --git a/docs/reference/gartner.corn.html b/docs/reference/gartner.corn.html deleted file mode 100644 index b4185ed0..00000000 --- a/docs/reference/gartner.corn.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - - -Yield monitor data from a corn field in Minnesota — gartner.corn • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Yield monitor data from a corn field in Minnesota

    - - -
    - -
    -

    Yield monitor data from a corn field in Minnesota

    -
    - - 
    data("gartner.corn")
    - - -

    Format

    - -

    A data frame with 4949 observations on the following 8 variables.

    -
    long

    longitude

    -
    lat

    latitude

    -
    mass

    grain mass flow per second, pounds

    -
    time

    GPS time, in seconds

    -
    seconds

    seconds elapsed for each datum

    -
    dist

    distance traveled for each datum, in inches

    -
    moist

    grain moisture, percent

    -
    elev

    elevation, feet

    - -
    - -

    Details

    - - - -

    The data was collected 5 Nov 2011 from a corn field south of Mankato, - Minnesota, using a combine-mounted yield monitor. - https://www.google.com/maps/place/43.9237575,-93.9750632

    -

    Each harvested swath was 12 rows wide = 360 inches.

    -

    Time 0 is 5 Nov 2011, 12:38:03 Central Time. - Time 16359 = 4.54 hours later.

    -

    Yield is calculated as total dry weight (corrected to 15.5 percent moisture), divided by 56 pounds (to get bushels), divided by the harvested area. - drygrain = [massflow * seconds * (100-moisture) / (100-15.5)] / 56 - harvested area = (distance * swath width) / 6272640 - yield = drygrain / area

    -

    Source

    - - - -

    Originally from University of Minnesota Precision Agriculture Center. - https://www.soils.umn.edu/academics/classes/soil4111/hw/

    -

    Retrieved 27 Aug 2015 from https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls

    -

    Used under Creative Commons BY-SA 3.0 license.

    -

    References

    - -

    Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei - Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). - Novel approach to the analysis of spatially-varying treatment effects - in on-farm experiments. - Field Crops Research, 255, 15 September 2020, 107783. - https://doi.org/10.1016/j.fcr.2020.107783

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gartner.corn)
-dat <- gartner.corn
-
-# Calculate yield
-dat <- transform(dat, yield=(mass*seconds*(100-moist)/(100-15.5)/56)/(dist*360/6272640))
-# Delete yield outliers
-dat <- subset(dat, yield >50)
-
-# Colors for yield
-medy <- median(dat$yield)
-ncols <- 20
-wwidth <- 150
-brks <- seq(from = -wwidth/2, to=wwidth/2, length=ncols-1)
-brks <- c(-250, brks, 250) # 250 is safe..we cleaned data outside ?(50,450)?
-yldbrks <- brks + medy
-dat <- transform(dat, yldbin = as.numeric(cut(yield, breaks= yldbrks)))
-
-# Add polygons for soil map units
-# Go to: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx
-# Click: Lat and Long. 43.924, -93.975
-# Click the little AOI rectangle icon.  Drag around the field
-# In the AOI Properties, enter the Name: Gartner
-# Click the tab Soil Map to see map unit symbols, names
-# Click: Download Soils Data.  Click: Create Download Link.
-# Download the zip file and find the soilmu_a_aoi files.
-
-# Read shape files
-libs(rgdal)
-
    #> Loading required package: sp
    #> rgdal: version: 1.5-18, (SVN revision 1082)
-#> Geospatial Data Abstraction Library extensions to R successfully loaded
-#> Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
-#> Path to GDAL shared files: C:/kw/R/win-library/4.0/rgdal/gdal
-#> GDAL binary built with GEOS: TRUE 
-#> Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
-#> Path to PROJ shared files: C:/kw/R/win-library/4.0/rgdal/proj
-#> Linking to sp version:1.4-4
-#> To mute warnings of possible GDAL/OSR exportToProj4() degradation,
-#> use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
    # shp <- readOGR("C:/x/rpack/agridat/inst/files","gartner.corn")
-shp <- readOGR(system.file(package="agridat", "files"), "gartner.corn")
-
    #> OGR data source with driver: ESRI Shapefile 
-#> Source: "C:\kw\R\win-library\4.0\agridat\files", layer: "gartner.corn"
-#> with 14 features
-#> It has 4 fields
-#> Integer64 fields read as strings:  SPATIALVER 
    
-# Plot the shapefiles first to set up the coordinate system
-plot(shp, xlim=range(dat$long), ylim=range(dat$lat))
-
    box() # Add the yield points
-
    redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-with(dat, points(long,lat, main="yield heat map",
-                 col=redblue(ncols)[yldbin], cex=.75, pch=16))
-
    plot(shp, add=TRUE, lwd=2) # Overlay soil polygons on top
-
    title("gartner.corn - yield heatmap with soil map unit symbols")
-
    # Manual annotation of soil map units
-text(x = c(-93.97641, -93.97787, -93.97550, -93.97693, -93.97654, -93.97480,
-       -93.97375, -93.978284, -93.977617, -93.976715, -93.975929),
-     y = c(43.92185, 43.92290, 43.92358, 43.92445, 43.92532, 43.92553,
-       43.92568, 43.922163, 43.926427, 43.926993, 43.926631),
-     lab=c("110","319","319","230","105C","110","211","110","211","230","105C"))
-
    
-# Trim off the ends of the field & re-do image above
-dat <- subset(dat, lat < 43.925850 & lat > 43.921178)
-
-# Identify the soil type for each yield point
-dat$ix <- over(SpatialPoints(dat[ , c('long','lat')]),
-               SpatialPolygons(shp@polygons))
-dat$mu <- shp@data[, "MUSYM"][dat$ix]
-# Check the points are properly identified
-# with(dat, points(long,lat, col=redblue(ncols)[ix], cex=.75, pch=16))
-
-# Aggregate points by soil type and analyze
-tapply(dat$yield, dat$mu, mean)
-
    #>     105C      110      211      230      319 
-#> 137.0062 134.9430 143.2656 135.3349 135.0155 
    tapply(dat$yield, dat$mu, sd)
-
    #>      105C       110       211       230       319 
-#> 17.521462 11.166786  8.513848 11.958948 10.668256 
    libs(lattice)
-densityplot(~yield|mu, dat, layout=c(1,5),
-            main="gartner.corn - yield density plot")
-
    
-
-if(0){
-  # Draw a 3D surface.  Clearly shows the low drainage area
-  libs(rgl)
-  dat <- transform(dat, x=long-min(long), y=lat-min(lat), z=elev-min(elev))
-  clear3d()
-  points3d(dat$x, dat$y, dat$z/50000,
-           col=redblue(ncols)[dat$yldbin])
-  axes3d()
-  title3d(xlab='x',ylab='y',zlab='elev')
-  rgl.close()
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gathmann.bt-1.png b/docs/reference/gathmann.bt-1.png deleted file mode 100644 index 070dd296..00000000 Binary files a/docs/reference/gathmann.bt-1.png and /dev/null differ diff --git a/docs/reference/gathmann.bt.html b/docs/reference/gathmann.bt.html deleted file mode 100644 index f58d4667..00000000 --- a/docs/reference/gathmann.bt.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - - - -Impact of Bt corn on non-target species — gathmann.bt • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Impact of Bt corn on non-target species

    - - -
    - -
    -

    Impact of Bt corn on non-target species

    -
    - - - -

    Format

    - -

    A data frame with 16 observations on the following 3 variables.

    -
    gen

    genotype/maize, Bt ISO

    -
    thysan

    thysan abundance

    -
    aranei

    aranei abundance

    - -
    - -

    Details

    - -

    The experiment involved comparing a Bt maize and a near-isogenic - control variety.

    -

    Species abundances were measured for Thysanoptera (thrips) and - Araneida (spiders) in 8 different plots.

    -

    Each response is probably a mean across repeated measurements.

    -

    Used with permission of Achim Gathmann.

    -

    Source

    - -

    L. A. Hothorn, 2005. - Evaluation of Bt-Maize Field Trials by a Proof of Safety. - https://www.seedtest.org/upload/cms/user/presentation7Hothorn.pdf

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gathmann.bt)
-dat <- gathmann.bt
-
-
-# EDA suggests Bt vs ISO is significant for thysan, not for aranei
-libs(lattice)
-libs(reshape2)
-d2 <- melt(dat, id.var='gen')
-bwplot(value ~ gen|variable, d2,
-       main="gathmann.bt", ylab="Insect abundance",
-       panel=function(x,y,...){
-         panel.xyplot(jitter(as.numeric(x)),y,...)
-         panel.bwplot(x,y,...)
-       },
-       scales=list(relation="free"))
-
    
-
-if(0){
-
-  # ----- Parametric CI.  Thysan significant, aranei not significant.
-  libs(equivalence)
-
-  th0 <- with(dat, tost(thysan[1:8], thysan[9:16], alpha=.05, paired=FALSE))
-  lapply(th0[c("estimate","tost.interval")], round, 2)
-  # 14.28-8.72=5.56, (2.51, 8.59) # match Gathmann p. 11
-
-  ar0 <- with(dat, tost(aranei[1:8], aranei[9:16], alpha=.05, epsilon=.4))
-  lapply(ar0[c("estimate","tost.interval")], round, 2)
-  # .57-.47=.10, (-0.19, 0.40) # match Gathmann p. 11
-
-  # ----- Non-parametric exact CI.  Same result.
-  libs(coin)
-
-  th1 <- wilcox_test(thysan ~ gen, data=dat, conf.int=TRUE, conf.level=0.90)
-  lapply(confint(th1), round, 2)
-  # 6.36, (2.8, 9.2) # Match Gathmann p. 11
-
-  ar1 <- wilcox_test(aranei ~ gen, data=dat, conf.int=TRUE, conf.level=0.90)
-  lapply(confint(ar1), round, 2)
-  # .05 (-.2, .4)
-  
-  
-  # ----- Log-transformed exact CI. Same result.
-  th2 <- wilcox_test(log(thysan) ~ gen, data=dat, alternative=c("two.sided"),
-                     conf.int=TRUE, conf.level=0.9)
-  lapply(confint(th2), function(x) round(exp(x),2))
-  # 1.66, (1.38, 2.31) # Match Gathmann p 11
-  
-  # ----- Log-transform doesn't work on aranei, but asinh(x/2) does
-  ar2 <- wilcox_test(asinh(aranei/2) ~ gen, data=dat,
-                     alternative=c("two.sided"),
-                     conf.int=TRUE, conf.level=0.9)
-  lapply(confint(ar2), function(x) round(sinh(x)*2,1))
-
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gauch.soy-1.png b/docs/reference/gauch.soy-1.png deleted file mode 100644 index e3310ebc..00000000 Binary files a/docs/reference/gauch.soy-1.png and /dev/null differ diff --git a/docs/reference/gauch.soy.html b/docs/reference/gauch.soy.html deleted file mode 100644 index 03240ba6..00000000 --- a/docs/reference/gauch.soy.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - - -Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
    -
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    Multi-environment trial of soybeans in New York, 1977 to 1988

    - - -
    - -
    -

    New York soybean yields, 1977 to 1988, for 7 genotypes, 55 - environments (9 loc, 12 years), 2-3 reps.

    -
    - - - -

    Format

    - -

    A data frame with 1454 observations on the following 4 variables.

    -
    yield

    yield, kg/ha

    -
    rep

    repeated measurement

    -
    gen

    genotype, 7 levels

    -
    env

    environment, 55 levels

    -
    year

    year, 77-88

    -
    loc

    location, 10 levels

    - -
    - -

    Details

    - -

    Soybean yields at 13 percent moisture for 7 genotypes in 55 - environments with 4 replicates. Some environments had only 2 or 3 - replicates. The experiment was an RCB design, but some plots were - missing and there were many other soybean varieties in the experiment. - The replications appear in random order and do _NOT_ define blocks. - Environment names are a combination of the first letter of the - location name and the last two digits of the year. The location codes - are: - A=Aurora, C=Chazy, D=Riverhead, E=Etna, G=Geneseo, - I=Ithica, L=Lockport, N=Canton, R=Romulus, V=Valatie. - Plots were 7.6 m long, four rows wide (middle two rows were - harvested).

    -

    This data has been widely used (in various subsets) to promote the - benefits of AMMI (Additive Main Effects Multiplicative Interactions) - analyses.

    -

    The gen x env means of Table 1 (Zobel et al 1998) are least-squares - means (personal communication).

    -

    Retrieved Sep 2011 from - https://www.microcomputerpower.com/matmodel/matmodelmatmodel_sample_.html

    -

    Used with permission of Hugh Gauch.

    -

    Source

    - -

    Zobel, RW and Wright, MJ and Gauch Jr, HG. 1998. - Statistical analysis of a yield trial. - Agronomy journal, 80, 388-393. - https://doi.org/10.2134/agronj1988.00021962008000030002x

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gauch.soy)
-dat <- gauch.soy
-
-## dat <- transform(dat,
-##                  year = substring(env, 2),
-##                  loc = substring(env, 1, 1))
-
-# AMMI biplot
-libs(agricolae)
-# Figure 1 of Zobel et al 1988, means vs PC1 score
-dat2 <- droplevels(subset(dat, is.element(env, c("A77","C77","V77",
-"V78","A79","C79","G79","R79","V79","A80","C80","G80","L80","D80",
-"R80","V80","A81","C81","G81","L81","D81","R81","V81","A82","L82",
-"G82","V82","A83","I83","G83","A84","N84","C84","I84","G84"))))
-
-m2 <- with(dat2, AMMI(env, gen, rep, yield))
-bip <- m2$biplot
-with(bip, plot(yield, PC1, type='n', main="gauch.soy -- AMMI biplot"))
-
    with(bip, text(yield, PC1, rownames(bip),
-               col=ifelse(bip$type=="GEN", "darkgreen", "blue"),
-               cex=ifelse(bip$type=="GEN", 1.5, .75)))
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/george.wheat-1.png b/docs/reference/george.wheat-1.png deleted file mode 100644 index a102f876..00000000 Binary files a/docs/reference/george.wheat-1.png and /dev/null differ diff --git a/docs/reference/george.wheat.html b/docs/reference/george.wheat.html deleted file mode 100644 index 719b0972..00000000 --- a/docs/reference/george.wheat.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Multi-location/year breeding trial in California — george.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-location/year breeding trial in California

    - - -
    - -
    -

    Multi-location/year breeding trial in California

    -
    - - 
    data("george.wheat")
    - - -

    Format

    - -

    A data frame with 13996 observations on the following 5 variables.

    -
    gen

    genotype number

    -
    year

    year

    -
    loc

    location

    -
    block

    block

    -
    yield

    yield per plot

    - -
    - -

    Details

    - -

    This is a nice example of data from a breeding trial, in which some - check genotypes are kepts during the whole experiment, while other - genotypes enter and leave the breeding program. The data is highly - unbalanced with respect to genotypes-by-environments.

    -

    Results of late-stage small-trials of 211 genotypes of wheat in - California, conducted at 9 locations during the years 2004-2018.

    -

    Each trial was an RCB with 4 blocks.

    -

    The authors used this data to look at GGE biplots across years and - concluded that repeatable genotype-by-location patterns were weak, and - therefore the California cereal production region is a large, - unstable, mega-environment.

    -

    Data downloaded 2019-10-29 from Dryad, - https://doi.org/10.5061/dryad.bf8rt6b. - Data are public domain.

    -

    Source

    - -

    Nicholas George and Mark Lundy (2019). - Quantifying Genotype x Environment Effects in Long-Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region. - Crop Science, 59, 1960-1972. - https://doi.org/10.2135/cropsci2019.01.0010

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  libs(lattice, reshape2)
-  data(george.wheat)
-  dat <- george.wheat
-  
-  dat$env <- paste0(dat$year, ".", dat$loc)
-  # average reps, cast to matrix
-  mat <- reshape2::acast(dat, gen ~ env, value.var="yield", fun=mean, na.rm=TRUE)
-  lattice::levelplot(mat, aspect="m", 
-                     main="george.wheat", xlab="genotype", ylab="environment",
-                     scales=list(x=list(cex=.3,rot=90),y=list(cex=.5)))
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/giles.wheat-1.png b/docs/reference/giles.wheat-1.png deleted file mode 100644 index 3a3c3db0..00000000 Binary files a/docs/reference/giles.wheat-1.png and /dev/null differ diff --git a/docs/reference/giles.wheat.html b/docs/reference/giles.wheat.html deleted file mode 100644 index a20cca17..00000000 --- a/docs/reference/giles.wheat.html +++ /dev/null @@ -1,630 +0,0 @@ - - - - - - - - -Straw length and ear emergence for wheat genotypes. — giles.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Straw length and ear emergence for wheat genotypes.

    - - -
    - -
    -

    Straw length and ear emergence for wheat genotypes. Data are - unbalanced with respect to experiment year and genotype.

    -
    - - 
    data("giles.wheat")
    - - -

    Format

    - -

    A data frame with 247 observations on the following 4 variables.

    -
    gen

    genotype. Note, this is numeric!

    -
    env

    environment

    -
    straw

    straw length

    -
    emergence

    ear emergence, Julian date

    - -
    - -

    Details

    - - - -

    Highly unbalanced data of straw length and ear emergence date for - wheat genotypes.

    -

    The 'genotype' column is called 'Accession number' in original data. - The genotypes were chosen to represent the range of variation in the - trait.

    -

    The Julian date was found to be preferable to other methods (such as - days from sowing).

    -

    Piepho (2003) fit a bilinear model to the straw emergence data. This - is similar to Finlay-Wilkinson regression.

    -

    Source

    - -

    R. Giles (1990). - Utilization of unreplicated observations of agronomic characters in a wheat germplasm collection. - In: Wheat Genetic Resources. Meeting Diverse Needs. - Wiley, Chichester, U.K., pp.113-130.

    -

    References

    - - - -

    Piepho, HP (2003). - Model-based mean adjustment in quantitative germplasm evaluation data. - Genetic Resources and Crop Evolution, 50, 281-290. - https://doi.org/10.1023/A:1023503900759

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(giles.wheat)
-dat <- giles.wheat
-dat <- transform(dat, gen=factor(gen))
-dat <- transform(dat, env=factor(env))
-dat1 <- subset(dat, !is.na(straw))
-dat2 <- subset(dat, !is.na(emergence))
-
-# Traits are not related
-# with(dat, plot(straw~emergence))
-
-# Show unbalancedness of data
-libs(lattice, reshape2)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(acast(dat, gen ~ env, value.var='emergence'),
-          col.regions=redblue,
-          xlab="genotype", ylab="year",
-          main="giles.wheat - straw length")
-
    
-
-
-# ----- Analysis of straw length -----
-
-libs(emmeans)
-
    #> 
-#> Attaching package: 'emmeans'
    #> The following object is masked from 'package:HH':
-#> 
-#>     as.glht
    
-# Mean across years. Matches Piepho Table 7 'Simple'
-m1 = lm(straw ~ gen, data=dat1)
-emmeans(m1, 'gen')
-
    #>  gen  emmean   SE df lower.CL upper.CL
-#>  264   113.7 6.78 40    100.0    127.4
-#>  271    89.2 5.87 40     77.4    101.1
-#>  277   110.7 6.78 40     97.0    124.4
-#>  303    73.7 6.78 40     60.0     87.4
-#>  590    35.7 6.78 40     22.0     49.4
-#>  788    84.3 6.78 40     70.6     98.0
-#>  789   107.7 6.78 40     94.0    121.4
-#>  929    81.3 6.78 40     67.6     95.0
-#>  988   132.7 6.78 40    119.0    146.4
-#>  1189  131.0 5.87 40    119.1    142.9
-#>  1567   68.3 6.78 40     54.6     82.0
-#>  1571   79.0 6.78 40     65.3     92.7
-#>  2079   96.3 6.78 40     82.6    110.0
-#>  2098   83.0 6.78 40     69.3     96.7
-#>  2194   78.7 6.78 40     65.0     92.4
-#>  2208   54.0 6.78 40     40.3     67.7
-#>  2217  103.3 6.78 40     89.6    117.0
-#>  2495   82.3 6.78 40     68.6     96.0
-#>  2499  111.3 6.78 40     97.6    125.0
-#> 
-#> Confidence level used: 0.95 
    
-# Simple two-way model.  NOT the bi-additive model of Piepho.
-m2 = lm(straw ~ gen + env, data=dat1)
-emmeans(m2, 'gen')
-
    #>  gen  emmean   SE df lower.CL upper.CL
-#>  264   124.3 7.52 30    109.0    139.7
-#>  271    97.9 6.35 30     85.0    110.9
-#>  277   121.3 7.52 30    106.0    136.7
-#>  303    84.3 7.52 30     69.0     99.7
-#>  590    45.9 6.16 30     33.3     58.4
-#>  788    82.0 5.65 30     70.5     93.6
-#>  789   105.4 5.65 30     93.8    116.9
-#>  929    78.5 5.41 30     67.5     89.6
-#>  988   128.9 5.40 30    117.9    139.9
-#>  1189  128.5 4.78 30    118.7    138.3
-#>  1567   64.1 6.32 30     51.2     77.0
-#>  1571   74.8 6.32 30     61.9     87.7
-#>  2079   92.6 6.01 30     80.3    104.9
-#>  2098   79.3 6.01 30     67.0     91.5
-#>  2194   73.2 6.24 30     60.4     85.9
-#>  2208   48.5 6.24 30     35.8     61.3
-#>  2217   97.9 6.24 30     85.1    110.6
-#>  2495   74.1 6.56 30     60.7     87.5
-#>  2499  103.1 6.56 30     89.7    116.5
-#> 
-#> Results are averaged over the levels of: env 
-#> Confidence level used: 0.95 
    
-# Bi-additive model. Matches Piepho Table 6, rows (c)
-libs(gnm)
-m3 <- gnm(straw ~ env + Mult(gen,env), data=dat1)
-
    #> Initialising
-#> Running start-up iterations..
-#> Running main iterations.........................................................
-#> ..........
-#> Done
    cbind(adjusted=round(fitted(m3),0), dat1) 
-
    #>     adjusted  gen  env straw emergence
-#> 1        120  264 1970   121       156
-#> 2         94  271 1970    90       155
-#> 3        117  277 1970   117       148
-#> 4         83  303 1970    86       159
-#> 20        91  264 1971    90       146
-#> 21        67  271 1971    68       146
-#> 22        89  277 1971    89       137
-#> 23        56  303 1971    57       157
-#> 24        28  590 1971    27       157
-#> 39       130  264 1972   130       150
-#> 40        96  271 1972    98       152
-#> 41       126  277 1972   126       143
-#> 42        81  303 1972    78       154
-#> 43        41  590 1972    42       161
-#> 59       101  271 1973   101        NA
-#> 63        80  788 1973    80        NA
-#> 64        96  789 1973    96        NA
-#> 81        38  590 1974    38       163
-#> 82        85  788 1974    82       154
-#> 83       106  789 1974   112       147
-#> 84        81  929 1974    81       149
-#> 85       121  988 1974   124       165
-#> 86       126 1189 1974   120       154
-#> 101       89  788 1975    91       160
-#> 102      119  789 1975   115       155
-#> 103       83  929 1975    83       157
-#> 104      142  988 1975   144       169
-#> 105      148 1189 1975   149       170
-#> 122       79  929 1976    80       151
-#> 124      115 1189 1976   110       158
-#> 125       67 1567 1976    60       153
-#> 126       76 1571 1976    80       151
-#> 127       89 2079 1976   100       148
-#> 128       83 2098 1976    80       134
-#> 142      133  988 1977   130       168
-#> 143      139 1189 1977   145       168
-#> 146       91 2079 1977    85       161
-#> 147       78 2098 1977    75       143
-#> 148       72 2194 1977    75       161
-#> 149       47 2208 1977    50       157
-#> 150       99 2217 1977   100       161
-#> 165      103 2079 1978   104       158
-#> 166       90 2098 1978    94       139
-#> 167       83 2194 1978    76       163
-#> 168       56 2208 1978    57       158
-#> 169      111 2217 1978   112       158
-#> 170       81 2495 1978    82       153
-#> 171      121 2499 1978   120       156
-#> 182       61 1567 1979    65       166
-#> 183       75 1571 1979    75       169
-#> 186       80 2194 1979    85       169
-#> 187       60 2208 1979    55       162
-#> 188      101 2217 1979    98       164
-#> 189       78 2495 1979    75       159
-#> 190      109 2499 1979   110       162
-#> 201       79 1567 1980    80       162
-#> 202       86 1571 1980    82       160
-#> 208       88 2495 1980    90       153
-#> 209      104 2499 1980   104       157
    
-# ----- Analysis of Ear emergence -----
-
-# Simple two-way model. 
-m4 = lm(emergence ~ 1 + gen + env, data=dat2)
-emmeans(m4, c('gen','env')) # Matches Piepho Table 9. rpws (c)
-
    #>  gen  env   emmean   SE df lower.CL upper.CL
-#>  264  1970     155 1.31 43      152      157
-#>  271  1970     156 1.31 43      153      159
-#>  277  1970     146 1.31 43      144      149
-#>  303  1970     161 1.31 43      158      164
-#>  590  1970     167 1.51 43      164      170
-#>  788  1970     160 1.72 43      156      163
-#>  789  1970     154 1.72 43      151      158
-#>  929  1970     156 2.16 43      152      161
-#>  988  1970     168 2.10 43      164      173
-#>  1189 1970     164 2.10 43      160      168
-#>  1567 1970     160 2.51 43      155      166
-#>  1571 1970     160 2.51 43      155      165
-#>  2079 1970     158 2.43 43      153      163
-#>  2098 1970     141 2.43 43      136      146
-#>  2194 1970     162 2.48 43      157      167
-#>  2208 1970     157 2.48 43      152      162
-#>  2217 1970     159 2.48 43      154      164
-#>  2495 1970     153 2.54 43      148      158
-#>  2499 1970     156 2.54 43      151      161
-#>  264  1971     146 1.25 43      144      149
-#>  271  1971     148 1.25 43      145      150
-#>  277  1971     138 1.25 43      135      141
-#>  303  1971     153 1.25 43      150      155
-#>  590  1971     158 1.28 43      156      161
-#>  788  1971     151 1.62 43      148      155
-#>  789  1971     146 1.62 43      143      149
-#>  929  1971     148 2.06 43      144      152
-#>  988  1971     160 2.00 43      156      164
-#>  1189 1971     156 2.00 43      152      160
-#>  1567 1971     152 2.43 43      147      157
-#>  1571 1971     152 2.43 43      147      157
-#>  2079 1971     149 2.34 43      145      154
-#>  2098 1971     132 2.34 43      128      137
-#>  2194 1971     154 2.40 43      149      158
-#>  2208 1971     148 2.40 43      143      153
-#>  2217 1971     150 2.40 43      145      155
-#>  2495 1971     145 2.46 43      140      150
-#>  2499 1971     148 2.46 43      143      153
-#>  264  1972     150 1.25 43      147      152
-#>  271  1972     151 1.25 43      148      154
-#>  277  1972     141 1.25 43      139      144
-#>  303  1972     156 1.25 43      154      159
-#>  590  1972     162 1.28 43      159      164
-#>  788  1972     155 1.62 43      152      158
-#>  789  1972     149 1.62 43      146      153
-#>  929  1972     151 2.06 43      147      156
-#>  988  1972     163 2.00 43      159      168
-#>  1189 1972     159 2.00 43      155      163
-#>  1567 1972     156 2.43 43      151      160
-#>  1571 1972     155 2.43 43      150      160
-#>  2079 1972     153 2.34 43      148      158
-#>  2098 1972     136 2.34 43      131      141
-#>  2194 1972     157 2.40 43      152      162
-#>  2208 1972     152 2.40 43      147      157
-#>  2217 1972     154 2.40 43      149      159
-#>  2495 1972     148 2.46 43      143      153
-#>  2499 1972     151 2.46 43      146      156
-#>  264  1973a    151 1.20 43      149      154
-#>  271  1973a    152 1.20 43      150      155
-#>  277  1973a    143 1.20 43      140      145
-#>  303  1973a    158 1.20 43      155      160
-#>  590  1973a    163 1.18 43      161      165
-#>  788  1973a    156 1.33 43      154      159
-#>  789  1973a    151 1.33 43      148      153
-#>  929  1973a    153 1.87 43      149      157
-#>  988  1973a    165 1.81 43      161      169
-#>  1189 1973a    161 1.81 43      157      164
-#>  1567 1973a    157 2.28 43      152      162
-#>  1571 1973a    157 2.28 43      152      161
-#>  2079 1973a    154 2.18 43      150      159
-#>  2098 1973a    137 2.18 43      133      142
-#>  2194 1973a    158 2.24 43      154      163
-#>  2208 1973a    153 2.24 43      149      158
-#>  2217 1973a    155 2.24 43      151      160
-#>  2495 1973a    149 2.31 43      145      154
-#>  2499 1973a    153 2.31 43      148      157
-#>  264  1973b    152 1.20 43      150      154
-#>  271  1973b    153 1.20 43      151      156
-#>  277  1973b    144 1.20 43      141      146
-#>  303  1973b    158 1.20 43      156      161
-#>  590  1973b    164 1.18 43      161      166
-#>  788  1973b    157 1.33 43      154      160
-#>  789  1973b    151 1.33 43      149      154
-#>  929  1973b    154 1.87 43      150      157
-#>  988  1973b    166 1.81 43      162      169
-#>  1189 1973b    161 1.81 43      158      165
-#>  1567 1973b    158 2.28 43      153      162
-#>  1571 1973b    157 2.28 43      153      162
-#>  2079 1973b    155 2.18 43      151      159
-#>  2098 1973b    138 2.18 43      134      142
-#>  2194 1973b    159 2.24 43      155      164
-#>  2208 1973b    154 2.24 43      149      158
-#>  2217 1973b    156 2.24 43      151      160
-#>  2495 1973b    150 2.31 43      145      155
-#>  2499 1973b    153 2.31 43      149      158
-#>  264  1974     148 1.66 43      145      152
-#>  271  1974     150 1.66 43      146      153
-#>  277  1974     140 1.66 43      137      143
-#>  303  1974     155 1.66 43      152      158
-#>  590  1974     160 1.43 43      157      163
-#>  788  1974     153 1.33 43      151      156
-#>  789  1974     148 1.33 43      145      151
-#>  929  1974     150 1.42 43      147      153
-#>  988  1974     162 1.33 43      159      165
-#>  1189 1974     158 1.33 43      155      161
-#>  1567 1974     154 1.93 43      150      158
-#>  1571 1974     154 1.93 43      150      158
-#>  2079 1974     152 1.81 43      148      155
-#>  2098 1974     135 1.81 43      131      138
-#>  2194 1974     156 1.88 43      152      159
-#>  2208 1974     150 1.88 43      147      154
-#>  2217 1974     152 1.88 43      149      156
-#>  2495 1974     147 1.96 43      143      151
-#>  2499 1974     150 1.96 43      146      154
-#>  264  1975     156 1.80 43      153      160
-#>  271  1975     158 1.80 43      154      161
-#>  277  1975     148 1.80 43      144      152
-#>  303  1975     163 1.80 43      159      166
-#>  590  1975     168 1.66 43      165      172
-#>  788  1975     161 1.41 43      159      164
-#>  789  1975     156 1.41 43      153      159
-#>  929  1975     158 1.42 43      155      161
-#>  988  1975     170 1.33 43      167      173
-#>  1189 1975     166 1.33 43      163      169
-#>  1567 1975     162 1.93 43      158      166
-#>  1571 1975     162 1.93 43      158      166
-#>  2079 1975     159 1.81 43      156      163
-#>  2098 1975     142 1.81 43      139      146
-#>  2194 1975     164 1.88 43      160      167
-#>  2208 1975     158 1.88 43      154      162
-#>  2217 1975     160 1.88 43      156      164
-#>  2495 1975     155 1.96 43      151      159
-#>  2499 1975     158 1.96 43      154      162
-#>  264  1976     147 2.07 43      143      152
-#>  271  1976     149 2.07 43      144      153
-#>  277  1976     139 2.07 43      135      143
-#>  303  1976     154 2.07 43      150      158
-#>  590  1976     159 1.92 43      155      163
-#>  788  1976     152 1.78 43      149      156
-#>  789  1976     147 1.78 43      143      151
-#>  929  1976     149 1.48 43      146      152
-#>  988  1976     161 1.23 43      159      164
-#>  1189 1976     157 1.23 43      154      159
-#>  1567 1976     153 1.46 43      150      156
-#>  1571 1976     153 1.46 43      150      156
-#>  2079 1976     150 1.39 43      148      153
-#>  2098 1976     133 1.39 43      131      136
-#>  2194 1976     155 1.58 43      151      158
-#>  2208 1976     149 1.58 43      146      152
-#>  2217 1976     151 1.58 43      148      154
-#>  2495 1976     146 1.63 43      142      149
-#>  2499 1976     149 1.63 43      146      152
-#>  264  1977     156 2.14 43      152      161
-#>  271  1977     157 2.14 43      153      162
-#>  277  1977     148 2.14 43      144      152
-#>  303  1977     163 2.14 43      158      167
-#>  590  1977     168 1.99 43      164      172
-#>  788  1977     161 1.86 43      157      165
-#>  789  1977     156 1.86 43      152      159
-#>  929  1977     158 1.69 43      154      161
-#>  988  1977     170 1.25 43      167      172
-#>  1189 1977     166 1.25 43      163      168
-#>  1567 1977     162 1.64 43      159      165
-#>  1571 1977     162 1.64 43      158      165
-#>  2079 1977     159 1.37 43      157      162
-#>  2098 1977     142 1.37 43      140      145
-#>  2194 1977     163 1.38 43      161      166
-#>  2208 1977     158 1.38 43      155      161
-#>  2217 1977     160 1.38 43      157      163
-#>  2495 1977     154 1.62 43      151      158
-#>  2499 1977     158 1.62 43      155      161
-#>  264  1978     154 2.12 43      150      158
-#>  271  1978     155 2.12 43      151      160
-#>  277  1978     146 2.12 43      141      150
-#>  303  1978     160 2.12 43      156      165
-#>  590  1978     166 1.98 43      162      170
-#>  788  1978     159 1.84 43      155      163
-#>  789  1978     154 1.84 43      150      157
-#>  929  1978     156 1.67 43      152      159
-#>  988  1978     168 1.23 43      165      170
-#>  1189 1978     164 1.23 43      161      166
-#>  1567 1978     160 1.54 43      157      163
-#>  1571 1978     160 1.54 43      156      163
-#>  2079 1978     157 1.35 43      154      160
-#>  2098 1978     140 1.35 43      137      143
-#>  2194 1978     161 1.34 43      159      164
-#>  2208 1978     156 1.34 43      153      159
-#>  2217 1978     158 1.34 43      155      161
-#>  2495 1978     152 1.41 43      149      155
-#>  2499 1978     156 1.41 43      153      158
-#>  264  1979     161 2.27 43      156      166
-#>  271  1979     162 2.27 43      158      167
-#>  277  1979     153 2.27 43      148      157
-#>  303  1979     167 2.27 43      163      172
-#>  590  1979     173 2.13 43      169      177
-#>  788  1979     166 2.01 43      162      170
-#>  789  1979     161 2.01 43      156      165
-#>  929  1979     163 1.82 43      159      166
-#>  988  1979     175 1.48 43      172      178
-#>  1189 1979     170 1.48 43      167      173
-#>  1567 1979     167 1.41 43      164      170
-#>  1571 1979     166 1.41 43      164      169
-#>  2079 1979     164 1.59 43      161      167
-#>  2098 1979     147 1.59 43      144      150
-#>  2194 1979     168 1.40 43      165      171
-#>  2208 1979     163 1.40 43      160      166
-#>  2217 1979     165 1.40 43      162      168
-#>  2495 1979     159 1.38 43      156      162
-#>  2499 1979     163 1.38 43      160      165
-#>  264  1980     155 2.43 43      150      160
-#>  271  1980     156 2.43 43      152      161
-#>  277  1980     147 2.43 43      142      152
-#>  303  1980     162 2.43 43      157      167
-#>  590  1980     167 2.31 43      162      172
-#>  788  1980     160 2.19 43      156      165
-#>  789  1980     155 2.19 43      150      159
-#>  929  1980     157 2.01 43      153      161
-#>  988  1980     169 1.73 43      165      172
-#>  1189 1980     165 1.73 43      161      168
-#>  1567 1980     161 1.48 43      158      164
-#>  1571 1980     161 1.48 43      158      164
-#>  2079 1980     158 1.83 43      155      162
-#>  2098 1980     141 1.83 43      138      145
-#>  2194 1980     162 1.79 43      159      166
-#>  2208 1980     157 1.79 43      154      161
-#>  2217 1980     159 1.79 43      156      163
-#>  2495 1980     153 1.48 43      150      156
-#>  2499 1980     157 1.48 43      154      160
-#> 
-#> Confidence level used: 0.95 
    emmeans(m4, 'gen') # Match Piepho table 10, Least Squares column
-
    #>  gen  emmean   SE df lower.CL upper.CL
-#>  264     153 1.31 43      150      155
-#>  271     154 1.31 43      151      157
-#>  277     144 1.31 43      142      147
-#>  303     159 1.31 43      156      162
-#>  590     165 1.20 43      162      167
-#>  788     158 1.19 43      155      160
-#>  789     152 1.19 43      150      155
-#>  929     154 1.35 43      152      157
-#>  988     166 1.11 43      164      169
-#>  1189    162 1.11 43      160      164
-#>  1567    159 1.58 43      155      162
-#>  1571    158 1.58 43      155      161
-#>  2079    156 1.50 43      153      159
-#>  2098    139 1.50 43      136      142
-#>  2194    160 1.55 43      157      163
-#>  2208    155 1.55 43      152      158
-#>  2217    157 1.55 43      154      160
-#>  2495    151 1.60 43      148      154
-#>  2499    154 1.60 43      151      158
-#> 
-#> Results are averaged over the levels of: env 
-#> Confidence level used: 0.95 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gilmour.serpentine-1.png b/docs/reference/gilmour.serpentine-1.png deleted file mode 100644 index 1d09e118..00000000 Binary files a/docs/reference/gilmour.serpentine-1.png and /dev/null differ diff --git a/docs/reference/gilmour.serpentine-2.png b/docs/reference/gilmour.serpentine-2.png deleted file mode 100644 index cc311d1e..00000000 Binary files a/docs/reference/gilmour.serpentine-2.png and /dev/null differ diff --git a/docs/reference/gilmour.serpentine-3.png b/docs/reference/gilmour.serpentine-3.png deleted file mode 100644 index e51a894e..00000000 Binary files a/docs/reference/gilmour.serpentine-3.png and /dev/null differ diff --git a/docs/reference/gilmour.serpentine-4.png b/docs/reference/gilmour.serpentine-4.png deleted file mode 100644 index b7df283f..00000000 Binary files a/docs/reference/gilmour.serpentine-4.png and /dev/null differ diff --git a/docs/reference/gilmour.serpentine.html b/docs/reference/gilmour.serpentine.html deleted file mode 100644 index 0b3e7ec1..00000000 --- a/docs/reference/gilmour.serpentine.html +++ /dev/null @@ -1,349 +0,0 @@ - - - - - - - - -Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -

    Wheat yield in South Australia with serpentine row/col effects

    - - -
    - -
    -

    An RCB experiment of wheat in South Australia, with strong spatial - variation and serpentine row/column effects.

    -
    - - - -

    Format

    - -

    A data frame with 330 observations on the following 5 variables.

    -
    col

    column

    -
    row

    row

    -
    rep

    replicate factor, 3 levels

    -
    gen

    wheat variety, 108 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    A randomized complete block experiment. There are 108 varieties in 3 - reps. Plots are 6 meters long, 0.75 meters wide, trimmed to 4.2 - meters lengths before harvest. Trimming was done by spraying the wheat - with herbicide. The sprayer travelled in a serpentine pattern up and - down columns. The trial was sown in a serpentine manner with a - planter that seeds three rows at a time (Left, Middle, Right).

    -

    Field width 15 columns * 6 m = 90 m

    -

    Field length 22 plots * .75 m = 16.5 m

    -

    Used with permission of Arthur Gilmour, in turn with permission from - Gil Hollamby.

    -

    Source

    - -

    Arthur R Gilmour and Brian R Cullis and Arunas P Verbyla, 1997. - Accounting for natural and extraneous variation in the analysis of - field experiments. - Journal of Agric Biol Env Statistics, 2, 269-293.

    -

    References

    - -

    N. W. Galwey. 2014. - Introduction to Mixed Modelling: Beyond Regression and Analysis - of Variance. - Table 10.9

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(gilmour.serpentine)
-  dat <- gilmour.serpentine
-
-  libs(desplot)
-  desplot(dat, yield~ col*row,
-          num=gen, show.key=FALSE, out1=rep,
-          aspect = 16.5/90, # true aspect
-          main="gilmour.serpentine")
-
    
-
-  # Extreme field trend.  Blocking insufficient--needs a spline/smoother
-  # xyplot(yield~col, data=dat, main="gilmour.serpentine")
-
-
-  # ----------------------------------------------------------------------------
-
-  
-  libs(asreml,lucid) # asreml4
-    
-  dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
-  dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml
-  
-  # RCB
-  m0 <- asreml(yield ~ gen, data=dat, random=~rep)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:38 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1240.605      14296.31    223 17:08:38    0.0
-#>  2     -1238.949      14052.47    223 17:08:38    0.0
-#>  3     -1237.174      13779.88    223 17:08:38    0.0
-#>  4     -1235.989      13576.73    223 17:08:38    0.0
-#>  5     -1235.390      13443.10    223 17:08:38    0.0
-#>  6     -1235.240      13383.33    223 17:08:38    0.0
-#>  7     -1235.223      13362.53    223 17:08:38    0.0
-#>  8     -1235.222      13359.15    223 17:08:38    0.0
        
-  # Add AR1 x AR1
-  m1 <- asreml(yield ~ gen, data=dat,
-               resid = ~ar1(rowf):ar1(colf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:38 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1276.682      20479.20    223 17:08:38    0.0 (1 restrained)
-#>  2     -1204.192      12555.38    223 17:08:38    0.0 (1 restrained)
-#>  3     -1131.606      10258.80    223 17:08:38    0.0
-#>  4     -1107.525      13045.24    223 17:08:38    0.0
-#>  5     -1103.565      16993.13    223 17:08:38    0.0
-#>  6     -1103.220      18861.79    223 17:08:38    0.0
-#>  7     -1103.192      19475.06    223 17:08:38    0.0
-#>  8     -1103.190      19644.93    223 17:08:38    0.0
-#>  9     -1103.190      19689.59    223 17:08:38    0.0
      
-  # Add spline
-  m2 <- asreml(yield ~ gen + col, data=dat,
-               random= ~ spl(col) + colf,
-               resid = ~ar1(rowf):ar1(colf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:39 2021
-#> Spline: design points closer than 0.0014 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1115.615      4519.781    222 17:08:39    0.0
-#>  2     -1098.274      3998.898    222 17:08:39    0.0
-#>  3     -1084.274      3762.414    222 17:08:39    0.0
-#>  4     -1078.173      3688.511    222 17:08:39    0.0
-#>  5     -1076.057      3634.376    222 17:08:39    0.0
-#>  6     -1075.757      3596.593    222 17:08:39    0.0
-#>  7     -1075.744      3588.592    222 17:08:39    0.0
      
-  # Figure 4 shows serpentine spraying
-  p2 <- predict(m2, data=dat, classify="colf")$pvals
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:39 2021
-#> Spline: design points closer than 0.0014 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1075.744      3589.184    222 17:08:39    0.0
-#>  2     -1075.744      3589.280    222 17:08:39    0.0
-#>  3     -1075.744      3589.514    222 17:08:39    0.0
      plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
-
      
-  # Define column code (due to serpentine spraying)
-  # Rhelp doesn't like double-percent modulus symbol, so compute by hand
-  dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
-  
-  m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
-               random= ~ colf + rowf + spl(colf),
-               resid = ~ar1(rowf):ar1(colf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:39 2021
-#> Spline: design points closer than 0.0014 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1074.771      3215.959    219 17:08:39    0.0
-#>  2     -1065.402      2997.768    219 17:08:39    0.0
-#>  3     -1057.721      2912.815    219 17:08:39    0.0
-#>  4     -1054.628      2939.717    219 17:08:39    0.0
-#>  5     -1053.720      2984.784    219 17:08:39    0.0
-#>  6     -1053.619      3001.884    219 17:08:39    0.0
-#>  7     -1053.616      3009.864    219 17:08:39    0.0
      
-  # Figure 6 shows serpentine row effects
-  p3 <- predict(m3, data=dat, classify="rowf")$pvals
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:39 2021
-#> Spline: design points closer than 0.0014 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1053.616      3013.371    219 17:08:39    0.0
-#>  2     -1053.616      3013.726    219 17:08:39    0.0
-#>  3     -1053.616      3014.611    219 17:08:39    0.0
      plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
-
      text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
-                             'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
-
      
-  # Define row code (due to serpentine planting). 1=middle, 2=left/right
-  dat <- transform(dat, rowcode = factor(row))
-  levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
-                           '2','2','1','2','2','1','2','2','1','2','2','1','2')
-  
-  m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
-               random= ~ colf + rowf + spl(col),
-               resid = ~ar1(rowf):ar1(colf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:39 2021
-#> Spline: design points closer than 0.0014 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -1069.223      3140.388    218 17:08:39    0.0
-#>  2     -1059.875      2949.849    218 17:08:39    0.0
-#>  3     -1052.068      2905.675    218 17:08:39    0.0
-#>  4     -1048.746      2983.113    218 17:08:39    0.0
-#>  5     -1047.717      3075.335    218 17:08:39    0.0
-#>  6     -1047.602      3113.167    218 17:08:39    0.0
-#>  7     -1047.597      3130.509    218 17:08:39    0.0
-#>  8     -1047.596      3137.866    218 17:08:39    0.0
      plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
-       main="gilmour.serpentine")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gilmour.slatehall-1.png b/docs/reference/gilmour.slatehall-1.png deleted file mode 100644 index e9b8eeae..00000000 Binary files a/docs/reference/gilmour.slatehall-1.png and /dev/null differ diff --git a/docs/reference/gilmour.slatehall-2.png b/docs/reference/gilmour.slatehall-2.png deleted file mode 100644 index 08467fd4..00000000 Binary files a/docs/reference/gilmour.slatehall-2.png and /dev/null differ diff --git a/docs/reference/gilmour.slatehall.html b/docs/reference/gilmour.slatehall.html deleted file mode 100644 index c5771d4b..00000000 --- a/docs/reference/gilmour.slatehall.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Slate Hall Farm 1978 — gilmour.slatehall • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -

    Slate Hall Farm 1978

    - - -
    - -
    -

    Yields for a trial at Slate Hall Farm in 1978.

    -
    - - - -

    Format

    - -

    A data frame with 150 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield (grams/plot)

    -
    gen

    genotype factor, 25 levels

    -
    rep

    rep factor, 6 levels

    - -
    - -

    Details

    - -

    The trial was of spring wheat at Slate Hall Farm in 1978. The - experiment was a balanced lattice with 25 varieties in 6 replicates. - The 'rep' labels are arbitrary (no rep labels appeared in the source - data). - Each row within a rep is an incomplete block. - The plot size was 1.5 meters by 4 meters.

    -

    Field width: 10 plots x 4 m = 40 m

    -

    Field length: 15 plots x 1.5 meters = 22.5 m

    -

    Source

    - -

    Arthur R Gilmour and Brian R Cullis and Arunas P Verbyla (1997). - Accounting for natural and extraneous variation in the analysis - of field experiments. - Journal of Agricultural, Biological, and Environmental - Statistics, 2, 269-293. - https://doi.org/10.2307/1400446

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(gilmour.slatehall)
-  dat <- gilmour.slatehall
-
-  libs(desplot)
-  desplot(dat, yield ~ col * row,
-          aspect=22.5/40, num=gen, out1=rep, cex=1,
-          main="gilmour.slatehall")
-
    
-  # ----------------------------------------------------------------------------
-
-  libs(asreml,lucid) # asreml4
-
-  # Model 4 of Gilmour et al 1997
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf, dat$yf), ]
-  m4 <- asreml(yield ~ gen + lin(row), data=dat,
-               random = ~ dev(row) + dev(col),
-               resid = ~ ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:41 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -733.509      30064.20    124 17:08:41    0.0
-#>  2      -725.011      26801.71    124 17:08:41    0.0
-#>  3      -718.953      25508.52    124 17:08:41    0.0
-#>  4      -716.764      24942.33    124 17:08:41    0.0
-#>  5      -716.202      24265.54    124 17:08:41    0.0
-#>  6      -716.167      23974.18    124 17:08:41    0.0
-#>  7      -716.166      23946.12    124 17:08:41    0.0
      # coef(m4)$fixed[1] # linear row
-  # [1] 31.72252 # (sign switch due to row ordering)
-
-  vc(m4)
-
    #>        effect component std.error z.ratio bound %ch
-#>      dev(col)  2519      1959         1.3     P   0
-#>      dev(row) 20290     10260         2       P   0
-#>       xf:yf!R 23950      4616         5.2     P   0
-#>  xf:yf!xf!cor     0.439     0.113     3.9     U   0
-#>  xf:yf!yf!cor     0.125     0.117     1.1     U   0
      ##       effect component std.error z.ratio bound 
-  ##     dev(col)  2519      1959         1.3     P   0
-  ##     dev(row) 20290     10260         2       P   0
-  ##     xf:yf(R) 23950      4616         5.2     P   0
-  ## xf:yf!xf!cor     0.439     0.113     3.9     U   0
-  ## xf:yf!yf!cor     0.125     0.117     1.1     U   0
-  
-  plot(varioGram(m4), main="gilmour.slatehall")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.fractionalfactorial-1.png b/docs/reference/gomez.fractionalfactorial-1.png deleted file mode 100644 index ade59d96..00000000 Binary files a/docs/reference/gomez.fractionalfactorial-1.png and /dev/null differ diff --git a/docs/reference/gomez.fractionalfactorial-2.png b/docs/reference/gomez.fractionalfactorial-2.png deleted file mode 100644 index e63193c6..00000000 Binary files a/docs/reference/gomez.fractionalfactorial-2.png and /dev/null differ diff --git a/docs/reference/gomez.fractionalfactorial.html b/docs/reference/gomez.fractionalfactorial.html deleted file mode 100644 index fdae3a1d..00000000 --- a/docs/reference/gomez.fractionalfactorial.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - - -Rice fractional factorial experiment 1/2 2^6. — gomez.fractionalfactorial • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Rice fractional factorial experiment 1/2 2^6.

    - - -
    - -
    -

    Rice fractional factorial experiment 1/2 2^6. - Two reps with 2 blocks in each rep.

    -
    - - - -

    Format

    - -

    A data frame with 64 observations on the following 6 variables.

    -
    yield

    grain yield in tons/ha

    -
    rep

    replicate, 2 levels

    -
    block

    block within rep, 2 levels

    -
    trt

    treatment, levels (1) to abcdef

    -
    col

    column position in the field

    -
    row

    row position in the field

    - - - -

    Details

    - -

    Grain yield from a 2^6 fractional factorial experiment in blocks of 16 - plots each, with two replications.

    -

    Gomez has some inconsistencies. One example:

    -

    Page 171: treatment (1) in rep 1, block 2 and rep 2, block 1.

    -

    Page 172: treatment (1) in Rep 1, block 1 and rep 2, block 1.

    -

    This data uses the layout shown on page 171.

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 171-172.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.fractionalfactorial)
-dat <- gomez.fractionalfactorial
-
-# trt abcdef has the highest yield
-# Gomez, Figure 4.8
-libs(desplot)
-desplot(dat, yield~col*row,
-        # aspect unknown
-        text=trt, shorten="none", show.key=FALSE, cex=1,
-        main="gomez.fractionalfactorial - treatment & yield")
    
-
-# Split treatment into individual factors
-dat <- transform(dat,
-                 a = -1 + 2 * grepl('a',trt),
-                  b = -1 + 2 * grepl('b',trt),
-                 c = -1 + 2 * grepl('c',trt),
-                 d = -1 + 2 * grepl('d',trt),
-                 e = -1 + 2 * grepl('e',trt),
-                 f = -1 + 2 * grepl('f',trt))
-
-# Gomez table 4.24, trt SS totalled together.
-# Why didn't Gomez nest block within rep?
-m0 <- lm(yield ~ rep * block + trt, dat)
-anova(m0)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df  Sum Sq Mean Sq F value  Pr(>F)    
-#> rep        1  0.0564 0.05641  6.0982 0.01944 *  
-#> block      1  0.0039 0.00391  0.4223 0.52073    
-#> trt       31 12.0815 0.38973 42.1346 < 2e-16 ***
-#> Residuals 30  0.2775 0.00925                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# Gomez table 4.24, trt SS split apart
-m1 <- lm(yield ~ rep * block + (a+b+c+d+e+f)^3, dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq  F value    Pr(>F)    
-#> rep        1 0.0564  0.0564   6.0982 0.0194445 *  
-#> block      1 0.0039  0.0039   0.4223 0.5207290    
-#> a          1 3.0016  3.0016 324.5072 < 2.2e-16 ***
-#> b          1 0.5776  0.5776  62.4461 8.075e-09 ***
-#> c          1 2.0022  2.0022 216.4665 2.928e-15 ***
-#> d          1 3.2041  3.2041 346.4048 < 2.2e-16 ***
-#> e          1 0.5041  0.5041  54.4998 3.175e-08 ***
-#> f          1 1.7623  1.7623 190.5228 1.565e-14 ***
-#> rep:block  1 0.0039  0.0039   0.4223 0.5207290    
-#> a:b        1 0.0342  0.0342   3.7002 0.0639421 .  
-#> a:c        1 0.0132  0.0132   1.4298 0.2411643    
-#> a:d        1 0.0016  0.0016   0.1730 0.6804364    
-#> a:e        1 0.0001  0.0001   0.0108 0.9178793    
-#> a:f        1 0.0410  0.0410   4.4333 0.0437219 *  
-#> b:c        1 0.0352  0.0352   3.8008 0.0606285 .  
-#> b:d        1 0.0410  0.0410   4.4333 0.0437219 *  
-#> b:e        1 0.0138  0.0138   1.4926 0.2313218    
-#> b:f        1 0.0042  0.0042   0.4568 0.5043110    
-#> c:d        1 0.3570  0.3570  38.5970 7.707e-07 ***
-#> c:e        1 0.0116  0.0116   1.2494 0.2725423    
-#> c:f        1 0.0030  0.0030   0.3270 0.5716668    
-#> d:e        1 0.1388  0.1388  15.0014 0.0005404 ***
-#> d:f        1 0.0400  0.0400   4.3245 0.0462114 *  
-#> e:f        1 0.0529  0.0529   5.7192 0.0232535 *  
-#> a:b:d      1 0.0046  0.0046   0.4926 0.4881840    
-#> a:b:e      1 0.0039  0.0039   0.4223 0.5207290    
-#> a:b:f      1 0.0240  0.0240   2.5974 0.1175108    
-#> a:c:d      1 0.0915  0.0915   9.8930 0.0037271 ** 
-#> a:c:e      1 0.0176  0.0176   1.8981 0.1784872    
-#> a:c:f      1 0.0009  0.0009   0.0973 0.7572498    
-#> a:d:e      1 0.0495  0.0495   5.3523 0.0277314 *  
-#> a:d:f      1 0.0462  0.0462   4.9975 0.0329765 *  
-#> a:e:f      1 0.0000  0.0000   0.0027 0.9588823    
-#> Residuals 30 0.2775  0.0092                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-libs(FrF2)
-aliases(m1)
    #>                           
-#>  rep:block = a:b:c = d:e:f
-#>  a:b:d = c:e:f            
-#>  a:b:e = c:d:f            
-#>  a:b:f = c:d:e            
-#>  a:c:d = b:e:f            
-#>  a:c:e = b:d:f            
-#>  a:c:f = b:d:e            
-#>  a:d:e = b:c:f            
-#>  a:d:f = b:c:e            
-#>  a:e:f = b:c:d            
    MEPlot(m1, select=3:8,
-       main="gomez.fractionalfactorial - main effects plot")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.groupsplit-1.png b/docs/reference/gomez.groupsplit-1.png deleted file mode 100644 index 86079145..00000000 Binary files a/docs/reference/gomez.groupsplit-1.png and /dev/null differ diff --git a/docs/reference/gomez.groupsplit.html b/docs/reference/gomez.groupsplit.html deleted file mode 100644 index 3fe4ae95..00000000 --- a/docs/reference/gomez.groupsplit.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Group balanced split-plot design in rice — gomez.groupsplit • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Group balanced split-plot design in rice

    - - -
    - -
    -

    Group balanced split-plot design in rice

    -
    - - - -

    Format

    - -

    A data frame with 270 observations on the following 7 variables.

    -
    col

    column

    -
    row

    row

    -
    rep

    replicate factor, 3 levels

    -
    fert

    fertilizer factor, 2 levels

    -
    gen

    genotype factor, 45 levels

    -
    group

    grouping (genotype) factor, 3 levels

    -
    yield

    yield of rice

    - - - -

    Details

    - -

    Genotype group S1 is less than 105 days growth duration, S2 is - 105-115 days growth duration, S3 is more than 115 days.

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 120.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.groupsplit)
-dat <- gomez.groupsplit
-
-# Gomez figure 3.10.  Obvious fert and group effects
-libs(desplot)
-desplot(dat, group ~ col*row,
-        out1=rep, col=fert, text=gen, # aspect unknown
-        main="gomez.groupsplit")
    
-# Gomez table 3.19 (not partitioned by group)
-m1 <- aov(yield ~ fert*group + gen:group + fert:gen:group +
-            Error(rep/fert/group), data=dat)
-summary(m1)
    #> 
-#> Error: rep
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  2  4.917   2.458               
-#> 
-#> Error: rep:fert
-#>           Df Sum Sq Mean Sq F value Pr(>F)  
-#> fert       1  96.05   96.05    68.7 0.0142 *
-#> Residuals  2   2.80    1.40                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:fert:group
-#>            Df Sum Sq Mean Sq F value Pr(>F)  
-#> group       2  4.259  2.1294   6.674 0.0197 *
-#> fert:group  2  0.628  0.3138   0.984 0.4150  
-#> Residuals   8  2.553  0.3191                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>                 Df Sum Sq Mean Sq F value   Pr(>F)    
-#> group:gen       42 20.494  0.4880   4.461 2.08e-12 ***
-#> fert:group:gen  42  4.093  0.0975   0.891    0.662    
-#> Residuals      168 18.378  0.1094                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.heterogeneity-1.png b/docs/reference/gomez.heterogeneity-1.png deleted file mode 100644 index 8a0a187b..00000000 Binary files a/docs/reference/gomez.heterogeneity-1.png and /dev/null differ diff --git a/docs/reference/gomez.heterogeneity.html b/docs/reference/gomez.heterogeneity.html deleted file mode 100644 index fcc21828..00000000 --- a/docs/reference/gomez.heterogeneity.html +++ /dev/null @@ -1,150 +0,0 @@ - -RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity • agridat - - -
    -
    - - - -
    -
    -
    -

    RCB experiment of rice, heterogeneity of regressions

    - - -
    - -
    -

    RCB experiment of rice, heterogeneity of regressions

    -
    - -
    - 
    data("gomez.heterogeneity")
    -
    - -
    -

    Format

    -

    gen
    -

    genotype

    - -
    yield
    -

    yield kg/ha

    - -
    tillers
    -

    tillers no/hill

    - - -
    -
    -

    Details

    - - -

    An experiment with 3 genotypes to examine the relationship of yield to - number of tillers.

    -

    Used with permission of Kwanchai Gomez.

    -
    -
    -

    Source

    -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 377.

    -
    -
    -

    References

    -

    None.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.heterogeneity)
-dat <- gomez.heterogeneity
-
-  libs(lattice)
-  xyplot(yield ~ tillers, dat, groups=gen,
-         type=c("p","r"),
-         main="gomez.heterogeneity")
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/gomez.heteroskedastic-1.png b/docs/reference/gomez.heteroskedastic-1.png deleted file mode 100644 index 42c83f4d..00000000 Binary files a/docs/reference/gomez.heteroskedastic-1.png and /dev/null differ diff --git a/docs/reference/gomez.heteroskedastic.html b/docs/reference/gomez.heteroskedastic.html deleted file mode 100644 index fcb8effd..00000000 --- a/docs/reference/gomez.heteroskedastic.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of rice, heteroskedastic varieties

    - - -
    - -
    - -

    RCB experiment of rice, heteroskedastic varieties

    - -
    - - 
    data("gomez.heteroskedastic")
    - -

    Format

    - -

    A data frame with 105 observations on the following 4 variables.

    -
    gen

    genotype

    -
    group

    group of genotypes

    -
    rep

    replicate

    -
    yield

    yield

    -
    - - -

    Details

    - - - -

    RCB design with three reps. Genotypes 1-15 are hybrids, 16-32 are - parents, 33-35 are checks.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 310.

    -

    Used with permission of Kwanchai Gomez.

    - -

    References

    - -

    None.

    - - -

    Examples

    - 
    
-library(agridat)
-data(gomez.heteroskedastic)
-dat <- gomez.heteroskedastic
-
-# Fix the outlier as reported by Gomez p. 311
-dat[dat$gen=="G17" & dat$rep=="R2","yield"] <- 7.58
-
-libs(lattice)
-bwplot(gen ~ yield, dat, group=as.numeric(dat$group),
-       ylab="genotype", main="gomez.heterogeneous")
    
-# Match Gomez table 7.28
-m1 <- lm(yield ~ rep + gen, data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> rep        2  3.306 1.65304  5.6164  0.005528 ** 
-#> gen       34 40.020 1.17705  3.9992 5.806e-07 ***
-#> Residuals 68 20.014 0.29432                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Response: yield
-##           Df Sum Sq Mean Sq F value    Pr(>F)    
-## rep        2  3.306 1.65304  5.6164  0.005528 ** 
-## gen       34 40.020 1.17705  3.9992 5.806e-07 ***
-## Residuals 68 20.014 0.29432                      
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.multilocsplitplot-1.png b/docs/reference/gomez.multilocsplitplot-1.png deleted file mode 100644 index 6632ca56..00000000 Binary files a/docs/reference/gomez.multilocsplitplot-1.png and /dev/null differ diff --git a/docs/reference/gomez.multilocsplitplot.html b/docs/reference/gomez.multilocsplitplot.html deleted file mode 100644 index 6bf0d99a..00000000 --- a/docs/reference/gomez.multilocsplitplot.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - - - -Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of rice, split-plot design

    - - -
    - -
    - -

    Grain yield was measured at 3 locations with 2 reps per location. - Within each rep, the main plot was 6 nitrogen fertilizer treatments - and the sub plot was 2 rice varieties.

    - -
    - - -

    Format

    - -

    A data frame with 108 observations on the following 5 variables.

    -
    loc

    location, 3 levels

    -
    nitro

    nitrogen in kg/ha

    -
    rep

    replicate, 2 levels

    -
    gen

    genotype, 2 levels

    -
    yield

    yield, kg/ha

    -
    - - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 339.

    -

    Used with permission of Kwanchai Gomez.

    - - -

    Examples

    - 
    
-library(agridat)
-
-data(gomez.multilocsplitplot)
-dat <- gomez.multilocsplitplot
-dat$nf <- factor(dat$nitro)
-
-# Gomez figure 8.3
-libs(lattice)
-xyplot(yield~nitro, dat, group=loc, type=c('p','smooth'), auto.key=TRUE,
-       main="gomez.multilocsplitplot")
    
-# AOV
-# Be careful to use the right stratum, 'nf' appears in both strata.
-# Still not quite the same as Gomez table 8.21
-t1 <- terms(yield ~ loc * nf * gen + Error(loc:rep:nf),
-            "Error", keep.order=TRUE)
-m1 <- aov(t1, data=dat)
    #> Warning: Error() model is singular
    summary(m1)
    #> 
-#> Error: loc:rep:nf
-#>            Df    Sum Sq  Mean Sq
-#> loc         2   4401065  2200532
-#> nf          5 136849095 27369819
-#> gen         1   2752683  2752683
-#> loc:nf     10  18591666  1859167
-#> loc:gen     2    258519   129260
-#> nf:gen      5   1018467   203693
-#> loc:nf:gen 10   6477080   647708
-#> 
-#> Error: Within
-#>            Df   Sum Sq Mean Sq F value   Pr(>F)    
-#> gen         1  7085791 7085791  18.326 7.67e-05 ***
-#> loc:gen     2  5171261 2585630   6.687  0.00254 ** 
-#> nf:gen      5  3034192  606838   1.569  0.18437    
-#> loc:nf:gen 10  1487520  148752   0.385  0.94801    
-#> Residuals  54 20879417  386656                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-
      # F values are somewhat similar to Gomez Table 8.21
-  libs(lme4)
-  m2 <- lmer(yield ~ loc*nf*gen + (1|loc/rep/nf), dat)
    #> Warning: unable to evaluate scaled gradient
    #> Warning:  Hessian is numerically singular: parameters are not uniquely determined
      anova(m2)
    #> Analysis of Variance Table
-#>            Df   Sum Sq  Mean Sq F value
-#> loc         2   407688   203844  0.5271
-#> nf          5 72885372 14577074 37.6944
-#> gen         1  7558837  7558837 19.5462
-#> loc:nf     10 10143064  1014306  2.6229
-#> loc:gen     2  4268700  2134350  5.5191
-#> nf:gen      5  1501333   300267  0.7765
-#> loc:nf:gen 10  1503043   150304  0.3887
      ## Analysis of Variance Table
-  ##            Df   Sum Sq  Mean Sq F value
-  ## loc         2   117942    58971  0.1525
-  ## nf          5 72841432 14568286 37.6777
-  ## gen         1  7557570  7557570 19.5460
-  ## loc:nf     10 10137188  1013719  2.6218
-  ## loc:gen     2  4270469  2135235  5.5223
-  ## nf:gen      5  1501767   300353  0.7768
-  ## loc:nf:gen 10  1502273   150227  0.3885
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.nitrogen-1.png b/docs/reference/gomez.nitrogen-1.png deleted file mode 100644 index f98a581b..00000000 Binary files a/docs/reference/gomez.nitrogen-1.png and /dev/null differ diff --git a/docs/reference/gomez.nitrogen-2.png b/docs/reference/gomez.nitrogen-2.png deleted file mode 100644 index 19dec07b..00000000 Binary files a/docs/reference/gomez.nitrogen-2.png and /dev/null differ diff --git a/docs/reference/gomez.nitrogen.html b/docs/reference/gomez.nitrogen.html deleted file mode 100644 index 126a3ed5..00000000 --- a/docs/reference/gomez.nitrogen.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - - -Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Soil nitrogen at three times for eight fertilizer treatments

    - - -
    - -
    - -

    Soil nitrogen at three times for eight fertilizer treatments

    - -
    - - -

    Format

    - -

    A data frame with 96 observations on the following 4 variables.

    -
    trt

    nitrogen treatment factor

    -
    nitro

    soil nitrogen content, percent

    -
    rep

    replicate

    -
    stage

    growth stage, three periods

    -
    - - -

    Details

    - -

    Eight fertilizer treatments were tested.

    -

    Soil nitrogen content was measured at three times. P1 = 15 days post - transplanting. P2 = 40 days post transplanting. P3 = panicle initiation.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 259.

    -

    Used with permission of Kwanchai Gomez.

    - -

    References

    - -

    R-help mailing list, 9 May 2013. - Data provided by Cyril Lundrigan. Analysis method by Rich Heiberger.

    - - -

    Examples

    - 
    
-library(agridat)
-
-data(gomez.nitrogen)
-dat <- gomez.nitrogen
-
-# Note the depletion of nitrogen over time (stage)
-libs(HH)
-interaction2wt(nitro ~ rep/trt + trt*stage, data=dat,
-               x.between=0, y.between=0,
-               main="gomez.nitrogen")
    
-# Just the fertilizer profiles
-with(dat, interaction.plot(stage, trt, nitro,
-                           col=1:4, lty=1:3, main="gomez.nitrogen",
-                           xlab="Soil nitrogen at three times for each treatment"))
    
-# Gomez table 6.16
-m1 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat)
-summary(m1)
    #> 
-#> Error: rep
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  3 0.8457  0.2819               
-#> 
-#> Error: rep:trt
-#>           Df Sum Sq Mean Sq F value  Pr(>F)   
-#> trt        7 1.2658 0.18083   4.935 0.00201 **
-#> Residuals 21 0.7695 0.03664                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>           Df Sum Sq Mean Sq F value   Pr(>F)    
-#> stage      2  52.04  26.021 715.871  < 2e-16 ***
-#> trt:stage 14   3.57   0.255   7.008 1.53e-07 ***
-#> Residuals 48   1.74   0.036                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# Gomez table 6.18
-# Treatment                    1 2 3 4 5 6 7 8
-cont <- cbind("T7 vs others" = c( 1, 1, 1, 1, 1, 1,-7, 1),
-              "T8 vs others"  = c( 1, 1, 1, 1, 1, 1, 0,-6),
-              "T2,T5 vs others"   = c(-1, 2,-1,-1, 2,-1, 0, 0),
-              "T2 vs T5"       = c( 0, 1, 0, 0,-1, 0, 0, 0))
-contrasts(dat$trt) <- cont
-contrasts(dat$trt)
    #>    T7 vs others T8 vs others T2,T5 vs others T2 vs T5              
-#> T1            1            1              -1        0 -3.028130e-01
-#> T2            1            1               2        1  2.775558e-17
-#> T3            1            1              -1        0 -2.101031e-01
-#> T4            1            1              -1        0 -3.487772e-01
-#> T5            1            1               2       -1 -1.387779e-17
-#> T6            1            1              -1        0  8.616933e-01
-#> T7           -7            0               0        0  1.387779e-17
-#> T8            1           -6               0        0 -2.775558e-17
-#>                               
-#> T1 -6.632738e-01 -4.673031e-01
-#> T2  8.326673e-17  1.387779e-17
-#> T3 -1.136421e-01  8.324315e-01
-#> T4  7.387109e-01 -2.875078e-01
-#> T5  5.551115e-17  1.387779e-17
-#> T6  3.820501e-02 -7.762061e-02
-#> T7  5.551115e-17  2.775558e-17
-#> T8  5.551115e-17  4.163336e-17
    
-m2 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat)
-summary(m2, expand.split=FALSE,
-        split=list(trt=list(
-                     "T7 vs others"=1,
-                     "T8 vs others"=2,
-                     "T2,T5 vs others"=3,
-                     "T2 vs T5"=4,
-                     rest=c(5,6,7)),
-          "trt:stage"=list(
-            "(T7 vs others):P"=c(1,8),
-            "(T8 vs others):P"=c(2,9),
-            "(T2,T5 vs others):P"=c(3,10),
-            "(T2 vs T5):P"=c(4,11),
-            "rest:P"=c(5,6,7,12,13,14))
-          ))
    #> 
-#> Error: rep
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  3 0.8457  0.2819               
-#> 
-#> Error: rep:trt
-#>                        Df Sum Sq Mean Sq F value  Pr(>F)   
-#> trt                     7 1.2658  0.1808   4.935 0.00201 **
-#>   trt: T7 vs others     1 0.3511  0.3511   9.581 0.00548 **
-#>   trt: T8 vs others     1 0.0455  0.0455   1.242 0.27761   
-#>   trt: T2,T5 vs others  1 0.0228  0.0228   0.621 0.43952   
-#>   trt: T2 vs T5         1 0.1176  0.1176   3.209 0.08764 . 
-#>   trt: rest             3 0.7289  0.2430   6.630 0.00252 **
-#> Residuals              21 0.7695  0.0366                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>                                  Df Sum Sq Mean Sq F value   Pr(>F)    
-#> stage                             2  52.04  26.021 715.871  < 2e-16 ***
-#> trt:stage                        14   3.57   0.255   7.008 1.53e-07 ***
-#>   trt:stage: (T7 vs others):P     2   2.14   1.068  29.391 4.63e-09 ***
-#>   trt:stage: (T8 vs others):P     2   0.54   0.268   7.373 0.001613 ** 
-#>   trt:stage: (T2,T5 vs others):P  2   0.64   0.321   8.843 0.000538 ***
-#>   trt:stage: (T2 vs T5):P         2   0.02   0.011   0.298 0.743303    
-#>   trt:stage: rest:P               6   0.23   0.038   1.051 0.404967    
-#> Residuals                        48   1.74   0.036                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-
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    Developed by Kevin Wright.

    -
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    Site built with pkgdown 1.3.0.9100.

    -
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    - - - - - - - - diff --git a/docs/reference/gomez.nonnormal1-1.png b/docs/reference/gomez.nonnormal1-1.png deleted file mode 100644 index 29a74207..00000000 Binary files a/docs/reference/gomez.nonnormal1-1.png and /dev/null differ diff --git a/docs/reference/gomez.nonnormal1.html b/docs/reference/gomez.nonnormal1.html deleted file mode 100644 index ba7fe1b8..00000000 --- a/docs/reference/gomez.nonnormal1.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Insecticide treatment effectiveness — gomez.nonnormal1 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Insecticide treatment effectiveness

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    Insecticide treatment effectiveness

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    data("gomez.nonnormal1")
    - -

    Format

    - -

    A data frame with 36 observations on the following 3 variables.

    -
    trt

    insecticidal treatment

    -
    rep

    replicate

    -
    larvae

    number of larvae

    -
    - - -

    Details

    - - - -

    Nine treatments (including the control, T9) were used on four - replicates. The number of living insect larvae were recorded.

    -

    The data show signs of non-normality, and a log transform was used by Gomez.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 300.

    -

    Used with permission of Kwanchai Gomez.

    - -

    References

    - -

    None.

    - - -

    Examples

    - 
    
-library(agridat)
-data(gomez.nonnormal1)
-dat <- gomez.nonnormal1
-
-# Gomez figure 7.3
-## libs(dplyr)
-## dat2 <- dat <!-- %&gt;% group_by(trt) -->
-## dat2 <- summarize(dat2, mn=mean(larvae), rng=diff(range(larvae)))
-## plot(rng ~ mn, data=dat2,
-##      xlab="mean number of larvae", ylab="range of number of larvae",
-##      main="gomez.nonnormal1")
-
-
-# Because some of the original values are less than 10,
-# the transform used is log10(x+1) instead of log10(x).
-dat <- transform(dat, tlarvae=log10(larvae+1))
-
-# QQ plots for raw/transformed data
-libs(reshape2, lattice)
-qqmath( ~ value|variable, data=melt(dat),
-       main="gomez.nonnormal1 - raw/transformed QQ plot",
-       scales=list(relation="free"))
    #> Using trt, rep as id variables
    
-
-# Gomez table 7.16
-m1 <- lm(tlarvae ~ rep + trt, data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: tlarvae
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> rep        3 0.9567 0.31889  3.6511 0.0267223 *  
-#> trt        8 3.9823 0.49779  5.6995 0.0004092 ***
-#> Residuals 24 2.0961 0.08734                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Response: tlarvae
-##           Df Sum Sq Mean Sq F value    Pr(>F)    
-## rep        3 0.9567 0.31889  3.6511 0.0267223 *  
-## trt        8 3.9823 0.49779  5.6995 0.0004092 ***
-## Residuals 24 2.0961 0.08734
-
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    Developed by Kevin Wright.

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    Site built with pkgdown 1.3.0.9100.

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    - - - - - - - - diff --git a/docs/reference/gomez.nonnormal2-1.png b/docs/reference/gomez.nonnormal2-1.png deleted file mode 100644 index 4438a8da..00000000 Binary files a/docs/reference/gomez.nonnormal2-1.png and /dev/null differ diff --git a/docs/reference/gomez.nonnormal2.html b/docs/reference/gomez.nonnormal2.html deleted file mode 100644 index 2554c9ca..00000000 --- a/docs/reference/gomez.nonnormal2.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -RCB experiment of rice, measuring white heads — gomez.nonnormal2 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    RCB experiment of rice, measuring white heads

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    RCB experiment of rice, measuring white heads

    - -
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    data("gomez.nonnormal2")
    - -

    Format

    - -

    A data frame with 42 observations on the following 3 variables.

    -
    gen

    genotype

    -
    rep

    replicate

    -
    white

    percentage of white heads

    -
    - - -

    Details

    - -

    The data are the percent of white heads from a rice variety trial of - 14 varieties with 3 reps. Because many of the values are less than - 10, the suggested data transformation is sqrt(x+.5).

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 300.

    -

    Used with permission of Kwanchai Gomez.

    - -

    References

    - -

    None.

    - - -

    Examples

    - 
    
-library(agridat)
-
-data(gomez.nonnormal2)
-dat <- gomez.nonnormal2
-
-# Gomez suggested sqrt transform
-dat <- transform(dat, twhite = sqrt(white+.5))
-
-# QQ plots for raw/transformed data
-libs(reshape2, lattice)
-qqmath( ~ value|variable, data=melt(dat),
-       main="gomez.nonnormal2 - raw/transformed QQ plot",
-       scales=list(relation="free"))
    #> Using gen, rep as id variables
    
-# Gomez anova table 7.21
-m1 <- lm(twhite ~ rep + gen, data=dat)
-anova(m1)
    #> Analysis of Variance Table
-#> 
-#> Response: twhite
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> rep        2  2.401  1.2004  1.9137    0.1678    
-#> gen       13 48.011  3.6931  5.8877 6.366e-05 ***
-#> Residuals 26 16.309  0.6273                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Response: twhite2
-##           Df Sum Sq Mean Sq F value    Pr(>F)    
-## rep        2  2.401  1.2004  1.9137    0.1678    
-## gen       13 48.011  3.6931  5.8877 6.366e-05 ***
-## Residuals 26 16.309  0.6273                      
-
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    Developed by Kevin Wright.

    -
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    Site built with pkgdown 1.3.0.9100.

    -
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    - - - - - - - - diff --git a/docs/reference/gomez.nonnormal3-1.png b/docs/reference/gomez.nonnormal3-1.png deleted file mode 100644 index 8e68c718..00000000 Binary files a/docs/reference/gomez.nonnormal3-1.png and /dev/null differ diff --git a/docs/reference/gomez.nonnormal3.html b/docs/reference/gomez.nonnormal3.html deleted file mode 100644 index 855fddb7..00000000 --- a/docs/reference/gomez.nonnormal3.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - - -RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    RCB experiment of rice, 12 varieties with leafhopper survival

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    RCB experiment of rice, 12 varieties with leafhopper survival

    - -
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    data("gomez.nonnormal3")
    - -

    Format

    - -

    A data frame with 36 observations on the following 3 variables.

    -
    gen

    genotype/variety of rice

    -
    rep

    replicate

    -
    hoppers

    percentage of surviving leafhoppers

    -
    - - -

    Details

    - -

    For each rice variety, 75 leafhoppers were caged and the percentage of - surviving insects was determined.

    -

    Gomez suggest replacing 0 values by 1/(4*75) and replacing 100 by - 1-1/(4*75) where 75 is the number of insects.

    -

    In effect, this means, for example, that (1/4)th of an insect survived.

    -

    Because the data are percents, Gomez suggested using the arcsin - transformation.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 307.

    -

    Used with permission of Kwanchai Gomez.

    - -

    References

    - -

    None.

    - - -

    Examples

    - 
    
-library(agridat)
-data(gomez.nonnormal3)
-dat <- gomez.nonnormal3
-
-# First, replace 0, 100 values
-dat$thoppers <- dat$hoppers
-dat <- transform(dat, thoppers=ifelse(thoppers==0, 1/(4*75), thoppers))
-dat <- transform(dat, thoppers=ifelse(thoppers==100, 100-1/(4*75), thoppers))
-# Arcsin transformation of percentage p converted to degrees
-# is arcsin(sqrt(p))/(pi/2)*90
-dat <- transform(dat, thoppers=asin(sqrt(thoppers/100))/(pi/2)*90)
-
-# QQ plots for raw/transformed data
-libs(reshape2, lattice)
-qqmath( ~ value|variable, data=melt(dat),
-       main="gomez.nonnormal3 - raw/transformed QQ plot",
-       scales=list(relation="free"))
    #> Using gen, rep as id variables
    
-m1 <- lm(thoppers ~ gen, data=dat)
-anova(m1) # Match Gomez table 7.25
    #> Analysis of Variance Table
-#> 
-#> Response: thoppers
-#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> gen       11 16838.7 1530.79  16.502 1.316e-08 ***
-#> Residuals 24  2226.4   92.77                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Response: thoppers
-##           Df  Sum Sq Mean Sq F value    Pr(>F)    
-## gen       11 16838.7 1530.79  16.502 1.316e-08 ***
-## Residuals 24  2226.4   92.77                      
-
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    Developed by Kevin Wright.

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    Site built with pkgdown 1.3.0.9100.

    -
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    - - - - - - - - diff --git a/docs/reference/gomez.rice.uniformity-1.png b/docs/reference/gomez.rice.uniformity-1.png deleted file mode 100644 index fda7d200..00000000 Binary files a/docs/reference/gomez.rice.uniformity-1.png and /dev/null differ diff --git a/docs/reference/gomez.rice.uniformity-2.png b/docs/reference/gomez.rice.uniformity-2.png deleted file mode 100644 index e5036494..00000000 Binary files a/docs/reference/gomez.rice.uniformity-2.png and /dev/null differ diff --git a/docs/reference/gomez.rice.uniformity-3.png b/docs/reference/gomez.rice.uniformity-3.png deleted file mode 100644 index 3670ad17..00000000 Binary files a/docs/reference/gomez.rice.uniformity-3.png and /dev/null differ diff --git a/docs/reference/gomez.rice.uniformity.html b/docs/reference/gomez.rice.uniformity.html deleted file mode 100644 index ff272eb0..00000000 --- a/docs/reference/gomez.rice.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trial of rice — gomez.rice.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of rice

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    Uniformity trial of rice in Philippines.

    -
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    Format

    - -

    A data frame with 648 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    grain yield, grams/m^2

    - - - -

    Details

    - -

    An area 20 meters by 38 meters was planted to rice variety IR8. At - harvest, a 1-meter border was removed around the field and discarded. - Each square meter (1 meter by 1 meter) was harvested and weighed.

    -

    Field width: 18 plots x 1 m = 18 m

    -

    Field length: 38 plots x 1 m = 38 m

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A. (1984). - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 481.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.rice.uniformity)
-dat <- gomez.rice.uniformity
-
-libs(desplot)
-# Raw data plot
-desplot(dat, yield ~ col*row,
-        aspect=38/18, # true aspect
-        main="gomez.rice.uniformity")
    
-libs(desplot, reshape2)
-# 3x3 moving average.  Gomez figure 12.1
-dmat <- melt(dat, id.var=c('col','row'))
-dmat <- acast(dmat, row~col)
-m0 <- dmat
-cx <- 2:17
-rx <- 2:35
-dmat3 <- (m0[rx+1,cx+1]+m0[rx+1,cx]+m0[rx+1,cx-1]+
-            m0[rx,cx+1]+m0[rx,cx]+m0[rx,cx-1]+
-            m0[rx-1,cx+1]+m0[rx-1,cx]+m0[rx-1,cx-1])/9
-dat3 <- melt(dmat3)
-desplot(dat3, value~Var2*Var1,
-        aspect=38/18,
-        at=c(576,637,695,753,811,870,927),
-        main="gomez.rice.uniformity smoothed")
    
-
-libs(agricolae)
- # Gomez table 12.4
-tab <- index.smith(dmat,
-                   main="gomez.rice.uniformity",
-                   col="red")$uniformity
    tab <- data.frame(tab)
-
-## # Gomez figure 12.2
-## op <- par(mar=c(5,4,4,4)+.1)
-## m1 <- nls(Vx ~ 9041/Size^b, data=tab, start=list(b=1))
-## plot(Vx ~ Size, tab, xlab="Plot size, m^2")
-## lines(fitted(m1) ~ tab$Size, col='red')
-## axis(4, at=tab$Vx, labels=tab$CV)
-## mtext("CV", 4, line=2)
-## par(op)
-
-# }
    -
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    Developed by Kevin Wright.

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    Site built with pkgdown 1.5.1.

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    - - - - - - - - diff --git a/docs/reference/gomez.seedrate-1.png b/docs/reference/gomez.seedrate-1.png deleted file mode 100644 index e0271ad1..00000000 Binary files a/docs/reference/gomez.seedrate-1.png and /dev/null differ diff --git a/docs/reference/gomez.seedrate-2.png b/docs/reference/gomez.seedrate-2.png deleted file mode 100644 index 3cf2c8d0..00000000 Binary files a/docs/reference/gomez.seedrate-2.png and /dev/null differ diff --git a/docs/reference/gomez.seedrate.html b/docs/reference/gomez.seedrate.html deleted file mode 100644 index 0f58180c..00000000 --- a/docs/reference/gomez.seedrate.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - - - -RCB experiment of rice, 6 densities — gomez.seedrate • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    RCB experiment of rice, 6 densities

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    RCB experiment of rice, 6 densities

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    Format

    - -

    A data frame with 24 observations on the following 3 variables.

    -
    rate

    kg seeds per hectare

    -
    rep

    rep (block), four levels

    -
    yield

    yield, kg/ha

    -
    - - -

    Details

    - -

    Rice yield at six different densities in an RCB design.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 26.

    -

    Used with permission of Kwanchai Gomez.

    - - -

    Examples

    - 
    
-library(agridat)
-data(gomez.seedrate)
-dat <- gomez.seedrate
-
-libs(lattice)
-xyplot(yield ~ rate, data=dat, group=rep, type='b',
-       main="gomez.seedrate", auto.key=list(columns=4))
    
-# Quadratic response.  Use raw polynomials so we can compute optimum
-m1 <- lm(yield ~ rep + poly(rate,2,raw=TRUE), dat)
--coef(m1)[5]/(2*coef(m1)[6]) # Optimum is at 29
    #> poly(rate, 2, raw = TRUE)1 
-#>                     29.148 
    
-# Plot the model predictions
-libs(latticeExtra)
-newdat <- expand.grid(rep=levels(dat$rep), rate=seq(25,150))
-newdat$pred <- predict(m1, newdat)
-p1 <- aggregate(pred ~ rate, newdat, mean) # average reps
-  xyplot(yield ~ rate, data=dat, group=rep, type='b',
-         main="gomez.seedrate (with model predictions)", auto.key=list(columns=4)) +
-    xyplot(pred ~ rate, p1, type='l', col='black', lwd=2)
    
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    Developed by Kevin Wright.

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    - - - - - - - - diff --git a/docs/reference/gomez.splitplot.subsample-1.png b/docs/reference/gomez.splitplot.subsample-1.png deleted file mode 100644 index 59b2e619..00000000 Binary files a/docs/reference/gomez.splitplot.subsample-1.png and /dev/null differ diff --git a/docs/reference/gomez.splitplot.subsample.html b/docs/reference/gomez.splitplot.subsample.html deleted file mode 100644 index 430a9b39..00000000 --- a/docs/reference/gomez.splitplot.subsample.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - -Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Split-plot experiment of rice, with subsamples

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    Split-plot experiment of rice, with subsamples

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    Format

    - -

    A data frame with 186 observations on the following 5 variables.

    -
    time

    time factor, T1-T4

    -
    manage

    management, M1-M6

    -
    rep

    rep/block, R1-R3

    -
    sample

    subsample, S1-S2

    -
    height

    plant height (cm)

    -
    - - -

    Details

    - -

    A split-plot experiment in three blocks. Whole-plot is 'management', - sub-plot is 'time' of application, with two subsamples. The data are - the heights, measured on two single-hill sampling units in each plot.

    - -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. Page 481.

    -

    Used with permission of Kwanchai Gomez.

    - - -

    Examples

    - 
    
-library(agridat)
-data(gomez.splitplot.subsample)
-dat <- gomez.splitplot.subsample
-
-libs(HH)
-interaction2wt(height ~ rep + time + manage, data=dat,
-               x.between=0, y.between=0,
-               main="gomez.splitplot.subsample - plant height")
    
-
-# Management totals, Gomez table 6.8
-# tapply(dat$height, dat$manage, sum)
-
-# Gomez table 6.11 analysis of variance
-m1 <- aov(height ~ rep + manage + time + manage:time +
-          Error(rep/manage/time), data=dat)
-summary(m1)
    #> 
-#> Error: rep
-#>     Df Sum Sq Mean Sq
-#> rep  2   2632    1316
-#> 
-#> Error: rep:manage
-#>           Df Sum Sq Mean Sq F value Pr(>F)  
-#> manage     7   1482  211.77   2.239 0.0944 .
-#> Residuals 14   1324   94.59                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:manage:time
-#>             Df Sum Sq Mean Sq F value   Pr(>F)    
-#> time         3  820.8  273.61   7.945 0.000211 ***
-#> manage:time 21  475.3   22.63   0.657 0.851793    
-#> Residuals   48 1653.1   34.44                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals 96  167.4   1.744               
    ## Error: rep
-##     Df Sum Sq Mean Sq
-## rep  2   2632    1316
-
-## Error: rep:manage
-##           Df Sum Sq Mean Sq F value Pr(>F)
-## manage     7   1482  211.77   2.239 0.0944 .
-## Residuals 14   1324   94.59
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## Error: rep:manage:time
-##             Df Sum Sq Mean Sq F value   Pr(>F)
-## time         3  820.8  273.61   7.945 0.000211 ***
-## manage:time 21  475.3   22.63   0.657 0.851793
-## Residuals   48 1653.1   34.44
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## Error: Within
-##           Df Sum Sq Mean Sq F value Pr(>F)
-## Residuals 96  167.4   1.744
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.splitsplit-1.png b/docs/reference/gomez.splitsplit-1.png deleted file mode 100644 index 24c4e11d..00000000 Binary files a/docs/reference/gomez.splitsplit-1.png and /dev/null differ diff --git a/docs/reference/gomez.splitsplit-2.png b/docs/reference/gomez.splitsplit-2.png deleted file mode 100644 index 17634026..00000000 Binary files a/docs/reference/gomez.splitsplit-2.png and /dev/null differ diff --git a/docs/reference/gomez.splitsplit-3.png b/docs/reference/gomez.splitsplit-3.png deleted file mode 100644 index 4e5ffd63..00000000 Binary files a/docs/reference/gomez.splitsplit-3.png and /dev/null differ diff --git a/docs/reference/gomez.splitsplit.html b/docs/reference/gomez.splitsplit.html deleted file mode 100644 index cff33d82..00000000 --- a/docs/reference/gomez.splitsplit.html +++ /dev/null @@ -1,270 +0,0 @@ - - - - - - - - -Split-split-plot experiment of rice — gomez.splitsplit • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Split-split-plot experiment of rice

    - - -
    - -
    -

    Grain yield of three varieties of rice grown in a split-split plot - arrangement with 3 reps, nitrogen level as the main plot, management - practice as the sub-plot, and rice variety as the sub-sub plot.

    -
    - - - -

    Format

    - -

    A data frame with 135 observations on the following 7 variables.

    -
    rep

    block, 3 levels

    -
    nitro

    nitrogen fertilizer, in kilograms/hectare

    -
    management

    plot management

    -
    gen

    genotype/variety of rice

    -
    yield

    yield

    -
    col

    column position in the field

    -
    row

    row position in the field

    - - - -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 143.

    -

    References

    - -

    H. P. Piepho, R. N. Edmondson. (2018). - A tutorial on the statistical analysis of factorial experiments - with qualitative and quantitative treatment factor levels. - Jour Agronomy and Crop Science, 8, 1-27. - https://doi.org/10.1111/jac.12267

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(gomez.splitsplit)
-dat <- gomez.splitsplit
-dat$nf <- factor(dat$nitro)
-
-libs(desplot)
-desplot(dat, nf ~ col*row,
-        # aspect unknown
-        out1=rep, col=management, num=gen, cex=1,
-        main="gomez.splitsplit")
    desplot(dat, yield ~ col*row,
-        # aspect unknown
-        out1=rep, main="gomez.splitsplit")
    
-
-libs(HH)
-position(dat$nf) <- c(0,50,80,110,140)
-interaction2wt(yield~rep+nf+management+gen, data=dat,
-               main="gomez.splitsplit",
-               x.between=0, y.between=0,
-               relation=list(x="free", y="same"),
-               rot=c(90,0), xlab="",
-               par.strip.text.input=list(cex=.7))
    
-
-# AOV.  Gomez page 144-153
-m0 <- aov(yield~ nf * management * gen + Error(rep/nf/management),
-         data=dat)
-summary(m0) # Similar to Gomez, p. 153.
    #> 
-#> Error: rep
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  2  0.732   0.366               
-#> 
-#> Error: rep:nf
-#>           Df Sum Sq Mean Sq F value   Pr(>F)    
-#> nf         4  61.64  15.410    27.7 9.73e-05 ***
-#> Residuals  8   4.45   0.556                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:nf:management
-#>               Df Sum Sq Mean Sq F value  Pr(>F)    
-#> management     2  42.94  21.468  81.996 2.3e-10 ***
-#> nf:management  8   1.10   0.138   0.527   0.823    
-#> Residuals     20   5.24   0.262                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>                   Df Sum Sq Mean Sq F value  Pr(>F)    
-#> gen                2 206.01  103.01 207.867 < 2e-16 ***
-#> nf:gen             8  14.14    1.77   3.568 0.00192 ** 
-#> management:gen     4   3.85    0.96   1.943 0.11490    
-#> nf:management:gen 16   3.70    0.23   0.467 0.95376    
-#> Residuals         60  29.73    0.50                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.stripplot-1.png b/docs/reference/gomez.stripplot-1.png deleted file mode 100644 index ac260a4d..00000000 Binary files a/docs/reference/gomez.stripplot-1.png and /dev/null differ diff --git a/docs/reference/gomez.stripplot.html b/docs/reference/gomez.stripplot.html deleted file mode 100644 index 60b39134..00000000 --- a/docs/reference/gomez.stripplot.html +++ /dev/null @@ -1,311 +0,0 @@ - - - - - - - - -Strip-plot experiment of rice — gomez.stripplot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Strip-plot experiment of rice

    - - -
    - -
    -

    A strip-plot experiment with three reps, variety as the horizontal - strip and nitrogen fertilizer as the vertical strip.

    -
    - - - -

    Format

    - -

    -
    yield

    Grain yield in kg/ha

    -
    rep

    Rep

    -
    nitro

    Nitrogen fertilizer in kg/ha

    -
    gen

    Rice variety

    -
    col

    column

    -
    row

    row

    - - - -

    Details

    - -

    Note, this is a subset of the the 'gomez.stripsplitplot' data.

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 110.

    -

    References

    - -

    Jan Gertheiss (2014). - ANOVA for Factors With Ordered Levels. - J Agric Biological Environmental Stat, 19, 258-277.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.stripplot)
-dat <- gomez.stripplot
-
-# Gomez figure 3.7
-libs(desplot)
-desplot(dat, gen ~ col*row,
-        # aspect unknown
-        out1=rep, out2=nitro, num=nitro, cex=1,
-        main="gomez.stripplot")
    
-
-# Gertheiss figure 1
-# library(lattice)
-# dotplot(factor(nitro) ~ yield|gen, data=dat)
-
-# Gomez table 3.12
-# tapply(dat$yield, dat$rep, sum)
-# tapply(dat$yield, dat$gen, sum)
-# tapply(dat$yield, dat$nitro, sum)
-
-# Gomez table 3.15.  Anova table for strip-plot
-dat <- transform(dat, nf=factor(nitro))
-m1 <- aov(yield ~ gen * nf + Error(rep + rep:gen + rep:nf), data=dat)
-summary(m1)
    #> 
-#> Error: rep
-#>           Df  Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  2 9220962 4610481               
-#> 
-#> Error: rep:gen
-#>           Df   Sum Sq  Mean Sq F value  Pr(>F)   
-#> gen        5 57100201 11420040   7.653 0.00337 **
-#> Residuals 10 14922619  1492262                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:nf
-#>           Df   Sum Sq  Mean Sq F value  Pr(>F)   
-#> nf         2 50676061 25338031   34.07 0.00307 **
-#> Residuals  4  2974908   743727                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>           Df   Sum Sq Mean Sq F value   Pr(>F)    
-#> gen:nf    10 23877979 2387798   5.801 0.000427 ***
-#> Residuals 20  8232917  411646                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Error: rep
-##           Df  Sum Sq Mean Sq F value Pr(>F)
-## Residuals  2 9220962 4610481               
-
-## Error: rep:gen
-##           Df   Sum Sq  Mean Sq F value  Pr(>F)   
-## gen        5 57100201 11420040   7.653 0.00337 **
-## Residuals 10 14922619  1492262                   
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## Error: rep:nf
-##           Df   Sum Sq  Mean Sq F value  Pr(>F)   
-## nf         2 50676061 25338031   34.07 0.00307 **
-## Residuals  4  2974908   743727                   
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## Error: Within
-##           Df   Sum Sq Mean Sq F value   Pr(>F)    
-## gen:nf    10 23877979 2387798   5.801 0.000427 ***
-## Residuals 20  8232917  411646                     
-
-
-# More compact view
-## libs(agricolae)
-## with(dat, strip.plot(rep, nf, gen, yield))
-
-## Analysis of Variance Table
-## Response: yield
-##        Df   Sum Sq  Mean Sq F value    Pr(>F)    
-## rep     2  9220962  4610481 11.2001 0.0005453 ***
-## nf      2 50676061 25338031 34.0690 0.0030746 ** 
-## Ea      4  2974908   743727  1.8067 0.1671590    
-## gen     5 57100201 11420040  7.6528 0.0033722 ** 
-## Eb     10 14922619  1492262  3.6251 0.0068604 ** 
-## gen:nf 10 23877979  2387798  5.8006 0.0004271 ***
-## Ec     20  8232917   411646                      
-
-
-# Mixed-model version
-## libs(lme4)
-## m3 <- lmer(yield ~ gen * nf + (1|rep) + (1|rep:nf) + (1|rep:gen), data=dat)
-## anova(m3)
-
-## Analysis of Variance Table
-##        Df   Sum Sq  Mean Sq F value
-## gen     5 15751300  3150260  7.6528
-## nf      2 28048730 14024365 34.0690
-## gen:nf 10 23877979  2387798  5.8006
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.stripsplitplot-1.png b/docs/reference/gomez.stripsplitplot-1.png deleted file mode 100644 index cb951cbd..00000000 Binary files a/docs/reference/gomez.stripsplitplot-1.png and /dev/null differ diff --git a/docs/reference/gomez.stripsplitplot-2.png b/docs/reference/gomez.stripsplitplot-2.png deleted file mode 100644 index ea1a86c2..00000000 Binary files a/docs/reference/gomez.stripsplitplot-2.png and /dev/null differ diff --git a/docs/reference/gomez.stripsplitplot.html b/docs/reference/gomez.stripsplitplot.html deleted file mode 100644 index bceafdfe..00000000 --- a/docs/reference/gomez.stripsplitplot.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - - -Strip-split-plot experiment of rice — gomez.stripsplitplot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Strip-split-plot experiment of rice

    - - -
    - -
    -

    A strip-split-plot experiment with three reps, genotype as the horizontal - strip, nitrogen fertilizer as the vertical strip, and planting method - as the subplot factor.

    -
    - - - -

    Format

    - -

    -
    yield

    grain yield in kg/ha

    -
    planting

    planting factor, P1=broadcast, P2=transplanted

    -
    rep

    rep, 3 levels

    -
    nitro

    nitrogen fertilizer, kg/ha

    -
    gen

    genotype, G1 to G6

    -
    col

    column

    -
    row

    row

    - - - -

    Details

    - -

    Note, this is a superset of the the 'gomez.stripplot' data.

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 155.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.stripsplitplot)
-dat <- gomez.stripsplitplot
-
-# Layout
-libs(desplot)
-desplot(dat, gen ~ col*row,
-        out1=rep, col=nitro, text=planting, cex=1,
-        main="gomez.stripsplitplot")
    
-
-# Gomez table 4.19, ANOVA of strip-split-plot design
-dat <- transform(dat, nf=factor(nitro))
-m1 <- aov(yield ~ nf * gen * planting +
-          Error(rep + rep:nf + rep:gen + rep:nf:gen), data=dat)
-summary(m1)
    #> 
-#> Error: rep
-#>           Df   Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  2 15289498 7644749               
-#> 
-#> Error: rep:nf
-#>           Df    Sum Sq  Mean Sq F value  Pr(>F)   
-#> nf         2 116489166 58244583   36.62 0.00268 **
-#> Residuals  4   6361491  1590373                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:gen
-#>           Df   Sum Sq Mean Sq F value Pr(>F)  
-#> gen        5 49119270 9823854   3.676 0.0379 *
-#> Residuals 10 26721828 2672183                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: rep:nf:gen
-#>           Df   Sum Sq Mean Sq F value Pr(>F)  
-#> nf:gen    10 24595731 2459573   2.575 0.0344 *
-#> Residuals 20 19106733  955337                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>                 Df   Sum Sq Mean Sq F value   Pr(>F)    
-#> planting         1   723079  723079   1.715   0.1986    
-#> nf:planting      2  2468132 1234066   2.927   0.0664 .  
-#> gen:planting     5 23761441 4752288  11.271 1.37e-06 ***
-#> nf:gen:planting 10  7512072  751207   1.782   0.1000 .  
-#> Residuals       36 15179354  421649                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# There is a noticeable linear trend along the y coordinate which may be
-# an artifact that blocking will remove, or may need to be modeled.
-# Note the outside values in the high-nitro boxplot.
-libs("HH")
-interaction2wt(yield ~ nitro + gen + planting + row, dat,
-               x.between=0, y.between=0,
-               x.relation="free")
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gomez.wetdry-1.png b/docs/reference/gomez.wetdry-1.png deleted file mode 100644 index f594b632..00000000 Binary files a/docs/reference/gomez.wetdry-1.png and /dev/null differ diff --git a/docs/reference/gomez.wetdry.html b/docs/reference/gomez.wetdry.html deleted file mode 100644 index f77cce2f..00000000 --- a/docs/reference/gomez.wetdry.html +++ /dev/null @@ -1,241 +0,0 @@ - - - - - - - - -Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
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    Rice yield in wet & dry seasons with nitrogen fertilizer treatments

    - - -
    - -
    -

    Rice yield in wet & dry seasons with nitrogen fertilizer treatments

    -
    - - - -

    Format

    - -

    A data frame with 96 observations on the following 4 variables.

    -
    season

    season = wet/dry

    -
    nitrogen

    nitrogen fertilizer kg/ha

    -
    rep

    replicate

    -
    yield

    grain yield, t/ha

    - -
    - -

    Details

    - -

    Five nitrogen fertilizer treatments were tested in 2 seasons using 3 reps.

    -

    Used with permission of Kwanchai Gomez.

    -

    Source

    - -

    Gomez, K.A. and Gomez, A.A.. 1984, - Statistical Procedures for Agricultural Research. - Wiley-Interscience. - Page 318.

    -

    References

    - -

    Rong-Cai Yang, Patricia Juskiw. (2011). - Analysis of covariance in agronomy and crop research. - Canadian Journal of Plant Science, 91:621-641. - https://doi.org/10.4141/cjps2010-032

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gomez.wetdry)
-dat <- gomez.wetdry
-
-libs(lattice)
-foo1 <- xyplot(yield ~ nitrogen|season, data=dat, 
-               group=rep,type='l',auto.key=list(columns=3),
-               ylab="yield in each season",
-               main="gomez.wetdry raw data & model")
-
-# Yang & Juskiw fit a quadratic model with linear and quadratic
-# contrasts using non-equal intervals of nitrogen levels.
-# This example below omits the tedious contrasts
-
-libs(latticeExtra, lme4)
-m1 <-lmer(yield ~ season*poly(nitrogen, 2) + (1|season:rep), data=dat)
-
    #> boundary (singular) fit: see ?isSingular
    pdat <- expand.grid(season=c('dry','wet'),
-                    nitrogen=seq(from=0,to=150,by=5))
-pdat$pred <- predict(m1, newdata=pdat, re.form= ~ 0)
-foo1 + 
-  xyplot(pred ~ nitrogen|season, data=pdat, type='l',lwd=2,col="black")
-
    
-
-# m2 <-lmer(yield ~ poly(nitrogen, 2) + (1|season:rep), data=dat)
-# anova(m1,m2)
-## m2: yield ~ poly(nitrogen, 2) + (1 | season:rep)
-## m1: yield ~ season * poly(nitrogen, 2) + (1 | season:rep)
-##    Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
-## m2  5 86.418 93.424 -38.209   76.418                             
-## m1  8 64.216 75.425 -24.108   48.216 28.202      3  3.295e-06 ***
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gotway.hessianfly-1.png b/docs/reference/gotway.hessianfly-1.png deleted file mode 100644 index 986b3bd3..00000000 Binary files a/docs/reference/gotway.hessianfly-1.png and /dev/null differ diff --git a/docs/reference/gotway.hessianfly-2.png b/docs/reference/gotway.hessianfly-2.png deleted file mode 100644 index 131766b7..00000000 Binary files a/docs/reference/gotway.hessianfly-2.png and /dev/null differ diff --git a/docs/reference/gotway.hessianfly.html b/docs/reference/gotway.hessianfly.html deleted file mode 100644 index 2b7a09d8..00000000 --- a/docs/reference/gotway.hessianfly.html +++ /dev/null @@ -1,316 +0,0 @@ - - - - - - - - -Hessian fly damage to wheat varieties — gotway.hessianfly • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Hessian fly damage to wheat varieties

    - - -
    - -
    -

    Hessian fly damage to wheat varieties

    -
    - - - -

    Format

    - -

    -
    block

    block factor, 4 levels

    -
    genotype factor, 16 wheat varieties

    -
    lat

    latitude, numeric

    -
    long

    longitude, numeric

    -
    y

    number of damaged plants

    -
    n

    number of total plants

    - -
    - -

    Details

    - -

    The response is binomial.

    -

    Each plot was square.

    -

    Source

    - -

    C. A. Gotway and W. W. Stroup. - A Generalized Linear Model Approach to Spatial Data Analysis and Prediction - Journal of Agricultural, Biological, and Environmental - Statistics, 2, 157-178.

    -

    https://doi.org/10.2307/1400401

    -

    References

    - -

    The GLIMMIX procedure. https://www.ats.ucla.edu/stat/SAS/glimmix.pdf

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(gotway.hessianfly)
-  dat <- gotway.hessianfly
-  
-  dat$prop <- dat$y / dat$n
-  
-  libs(desplot)
-  desplot(dat, prop~long*lat,
-          aspect=1, # true aspect
-          out1=block, num=gen, cex=.75,
-          main="gotway.hessianfly")
-
      
-
-  # ----------------------------------------------------------------------------
-
-  # spaMM package example
-  libs(spaMM)
-
    #> Registered S3 methods overwritten by 'registry':
-#>   method               from 
-#>   print.registry_field proxy
-#>   print.registry_entry proxy
    #> spaMM (Rousset & Ferdy, 2014, version 3.5.0) is loaded.
-#> Type 'help(spaMM)' for a short introduction,
-#> 'news(package='spaMM')' for news,
-#> and 'citation(spaMM)' for proper citation.
    #> 
-#> Attaching package: 'spaMM'
    #> The following object is masked from 'package:mgcv':
-#> 
-#>     negbin
      m1 = HLCor(cbind(y, n-y) ~ 1 + gen + (1|block) + Matern(1|long+lat),
-             data=dat, family=binomial(), ranPars=list(nu=0.5, rho=1/.7))
-  summary(m1)
-
    #> formula: cbind(y, n - y) ~ 1 + gen + (1 | block) + Matern(1 | long + lat)
-#> Estimation of lambda by Laplace REML approximation (p_bv).
-#> Estimation of fixed effects by Laplace ML approximation (p_v).
-#> Family: binomial ( link = logit ) 
-#>  ------------ Fixed effects (beta) ------------
-#>             Estimate Cond. SE t-value
-#> (Intercept)   2.1958   0.6424  3.4181
-#> genG02       -0.5166   0.8296 -0.6227
-#> genG03       -0.9161   0.8380 -1.0932
-#> genG04       -1.8245   0.8010 -2.2777
-#> genG05       -0.7466   0.8636 -0.8645
-#> genG06       -1.5772   0.8311 -1.8977
-#> genG07       -1.1878   0.8337 -1.4248
-#> genG08       -2.2601   0.8212 -2.7523
-#> genG09       -2.0346   0.7992 -2.5457
-#> genG10       -1.2166   0.8583 -1.4175
-#> genG11       -2.3692   0.8228 -2.8794
-#> genG12       -1.9480   0.8070 -2.4139
-#> genG13       -4.6014   0.9629 -4.7785
-#> genG14       -3.2362   0.8525 -3.7960
-#> genG15       -3.0618   0.8279 -3.6984
-#> genG16       -4.1374   0.8887 -4.6554
-#>  --------------- Random effects ---------------
-#> Family: gaussian ( link = identity ) 
-#>                    --- Correlation parameters:
-#>    2.rho     2.nu 
-#> 1.428571 0.500000 
-#>            --- Variance parameters ('lambda'):
-#> lambda = var(u) for u ~ Gaussian; 
-#>    block  :  4.807e-08 
-#>    long + lat  :  0.8619  
-#>              --- Coefficients for log(lambda):
-#>       Group        Term Estimate Cond.SE
-#>       block (Intercept)   -16.85   932.1
-#>  long + lat (Intercept)  -0.1486  0.2781
-#> # of obs: 64; # of groups: block, 4; long + lat, 64 
-#>  ------------- Likelihood values  -------------
-#>                        logLik
-#> p_v(h) (marginal L): -129.594
-#>   p_beta,v(h) (ReL): -123.561
      fixef(m1)
-
    #> (Intercept)      genG02      genG03      genG04      genG05      genG06 
-#>   2.1957920  -0.5166378  -0.9160873  -1.8245162  -0.7466366  -1.5771913 
-#>      genG07      genG08      genG09      genG10      genG11      genG12 
-#>  -1.1877620  -2.2600861  -2.0345601  -1.2165977  -2.3692284  -1.9480056 
-#>      genG13      genG14      genG15      genG16 
-#>  -4.6014047  -3.2361680  -3.0618020  -4.1374257 
      # The following line fails with "Invalid graphics state"
-  # when trying to use pkgdown::build_site
-  # filled.mapMM(m1)
-
-  # ----------------------------------------------------------------------------
-
-  # Block random.  See Glimmix manual, output 1.18.
-  # Note: (Different parameterization)
-  
-  libs(lme4)
-  l2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial,
-    control=glmerControl(check.nlev.gtr.1="ignore"))
-  coef(l2)
-
    #> $block
-#>    (Intercept)     genG02     genG03    genG04     genG05     genG06     genG07
-#> B1    1.507497 -0.1938602 -0.5408074 -1.434189 -0.2037025 -0.9783249 -0.6040858
-#> B2    1.502923 -0.1938602 -0.5408074 -1.434189 -0.2037025 -0.9783249 -0.6040858
-#> B3    1.493663 -0.1938602 -0.5408074 -1.434189 -0.2037025 -0.9783249 -0.6040858
-#> B4    1.509754 -0.1938602 -0.5408074 -1.434189 -0.2037025 -0.9783249 -0.6040858
-#>       genG08    genG09     genG10    genG11   genG12   genG13    genG14
-#> B1 -1.677434 -1.398435 -0.6817111 -1.462973 -1.45909 -3.55288 -2.507322
-#> B2 -1.677434 -1.398435 -0.6817111 -1.462973 -1.45909 -3.55288 -2.507322
-#> B3 -1.677434 -1.398435 -0.6817111 -1.462973 -1.45909 -3.55288 -2.507322
-#> B4 -1.677434 -1.398435 -0.6817111 -1.462973 -1.45909 -3.55288 -2.507322
-#>       genG15    genG16
-#> B1 -2.087175 -2.969689
-#> B2 -2.087175 -2.969689
-#> B3 -2.087175 -2.969689
-#> B4 -2.087175 -2.969689
-#> 
-#> attr(,"class")
-#> [1] "coef.mer"
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/goulden.barley.uniformity-1.png b/docs/reference/goulden.barley.uniformity-1.png deleted file mode 100644 index 6e18a5cd..00000000 Binary files a/docs/reference/goulden.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/goulden.barley.uniformity-2.png b/docs/reference/goulden.barley.uniformity-2.png deleted file mode 100644 index 48656261..00000000 Binary files a/docs/reference/goulden.barley.uniformity-2.png and /dev/null differ diff --git a/docs/reference/goulden.barley.uniformity.html b/docs/reference/goulden.barley.uniformity.html deleted file mode 100644 index 65212fdb..00000000 --- a/docs/reference/goulden.barley.uniformity.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Uniformity trial of barley — goulden.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trial of barley

    - - -
    - -
    -

    Uniformity trial of barley

    -
    - - - -

    Format

    - -

    A data frame with 400 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, grams per plot

    - -
    - -

    Details

    - -

    Yield in grams for 400 square-yard barley plots.

    -

    Field width: 20 plots x 3 feet = 60 feet

    -

    Field length: 20 plots x 3 feet = 60 feet

    -

    Source

    - -

    C. H. Goulden, (1939). - Methods of statistical analysis, 1st ed. Page 18. - https://archive.org/stream/methodsofstatist031744mbp

    -

    References

    - -

    Erwin LeClerg, Warren Leonard, Andrew Clark (1962). - Field Plot Technique, 2nd ed. Page 27.

    -

    Alternatively, page 39 in 1939 edition of "Field Plot Technique". - https://archive.org/stream/fieldplottechniq00leon

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(goulden.barley.uniformity)
-dat <- goulden.barley.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        aspect=20/20, # true aspect
-        main="goulden.barley.uniformity")
-
    
-# Left skewed distribution. See LeClerg, Leonard, Clark
-hist(dat$yield, main="goulden.barley.uniformity",
-     breaks=c(21,40,59,78,97,116,135,154,173,192,211,230,249)+.5)
-
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/goulden.eggs-1.png b/docs/reference/goulden.eggs-1.png deleted file mode 100644 index 06907b1b..00000000 Binary files a/docs/reference/goulden.eggs-1.png and /dev/null differ diff --git a/docs/reference/goulden.eggs.html b/docs/reference/goulden.eggs.html deleted file mode 100644 index 94bf17ba..00000000 --- a/docs/reference/goulden.eggs.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - - -Sample of egg weights on 24 consecutive days — goulden.eggs • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
    -
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    -

    Sample of egg weights on 24 consecutive days

    - - -
    - -
    -

    Sample of egg weights on 24 consecutive days

    -
    - - 
    data("goulden.eggs")
    - - -

    Format

    - -

    A data frame with 240 observations on the following 2 variables.

    -
    day

    day

    -
    weight

    weight

    - -
    - -

    Details

    - - - -

    Data are the weights of 10 eggs taken at random on each day for 24 - days. Day 1 was Dec 10, and Day 24 was Jan 2.

    -

    The control chart for standard deviations shows 4 values beyond the - upper limits. The data reveals a single, unusually large egg on each - of these days. These are almost surely double-yolk eggs.

    -

    Source

    - -

    Cyrus H. Goulden (1952). - Methods of Statistical Analysis, 2nd ed. Page 425.

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(goulden.eggs)
-dat <- goulden.eggs
-
-libs(qicharts)
-
    #> qicharts will no longer be maintained. Please consider moving to qicharts2: https://anhoej.github.io/qicharts2/.
    # Figure 19-4 of Goulden. (Goulden uses 1/n when calculating std dev)
-op <- par(mfrow=c(2,1))
-qic(weight, x = day, data = dat, chart = 'xbar',
-    main = 'goulden.eggs - Xbar chart',
-    xlab = 'Date', ylab = 'Avg egg weight' )
-qic(weight, x = day, data = dat, chart = 's',
-    main = 'goulden.eggs - S chart',
-    xlab = 'Date', ylab = 'Std dev egg weight' )
-
    par(op)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/goulden.latin-1.png b/docs/reference/goulden.latin-1.png deleted file mode 100644 index 70a44f4b..00000000 Binary files a/docs/reference/goulden.latin-1.png and /dev/null differ diff --git a/docs/reference/goulden.latin.html b/docs/reference/goulden.latin.html deleted file mode 100644 index 24276516..00000000 --- a/docs/reference/goulden.latin.html +++ /dev/null @@ -1,235 +0,0 @@ - - - - - - - - -Latin square experiment for testing fungicide — goulden.latin • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Latin square experiment for testing fungicide

    - - -
    - -
    -

    Latin square experiment for testing fungicide

    -
    - - 
    data("goulden.latin")
    - - -

    Format

    - -

    A data frame with 25 observations on the following 4 variables.

    -
    trt

    treatment factor, 5 levels

    -
    yield

    yield

    -
    row

    row

    -
    col

    column

    - -
    - -

    Details

    - - - -

    Five treatments were tested to control stem rust in wheat. Treatment - codes and descriptions: - A = Dusted before rains. - B = Dusted after rains. - C = Dusted once each week. - D = Drifting, once each week. - E = Not dusted.

    -

    Source

    - -

    Cyrus H. Goulden (1952). - Methods of Statistical Analysis, 2nd ed. Page 216.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-library(agridat)
-data(goulden.latin)
-dat <- goulden.latin
-
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        text=trt, cex=1, # aspect unknown
-        main="goulden.latin")
-
    
-
-# Matches Goulden.
-m1 <- lm(yield~ trt + factor(row) + factor(col), data=dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>             Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> trt          4 196.608  49.152 21.0321 2.366e-05 ***
-#> factor(row)  4  46.668  11.667  4.9923   0.01328 *  
-#> factor(col)  4  14.020   3.505  1.4998   0.26343    
-#> Residuals   12  28.044   2.337                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/goulden.splitsplit-1.png b/docs/reference/goulden.splitsplit-1.png deleted file mode 100644 index 711db048..00000000 Binary files a/docs/reference/goulden.splitsplit-1.png and /dev/null differ diff --git a/docs/reference/goulden.splitsplit.html b/docs/reference/goulden.splitsplit.html deleted file mode 100644 index 00e3486f..00000000 --- a/docs/reference/goulden.splitsplit.html +++ /dev/null @@ -1,291 +0,0 @@ - - - - - - - - -Split-split-plot experiment of wheat — goulden.splitsplit • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Split-split-plot experiment of wheat

    - - -
    - -
    -

    Split-split-plot experiment of wheat

    -
    - - 
    data("goulden.splitsplit")
    - - -

    Format

    - -

    A data frame with 160 observations on the following 9 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    -
    inoc

    inoculate

    -
    trt

    treatment number

    -
    gen

    genotype

    -
    dry

    dry/wet dust application

    -
    dust

    dust treatment

    -
    block

    block

    - -
    - -

    Details

    - - - -

    An interesting split-split plot experiment in which the sub-plot - treatments have a 2*5 factorial structure.

    -

    An experiment was conducted in 1932 on the experimental field of the - Dominion Rust Research Laboratory. The study was designed to - determine the effect on the incidence of root rot, of variety of - wheat, kinds of dust for seed treatment, method of application of the - dust, and efficacy of soil inoculation with the root-rot organism.

    -

    The field had 4 blocks.

    -

    Each block has 2 whole plots for the genotypes.

    -

    Each whole-plot had 10 sub-plots for the 5 different kinds of dust and - 2 methods of application.

    -

    Each sub-plot had 2 sub-sub-plots, one for inoculated soil and the - other one for uninoculated soil.

    -

    Source

    - -

    C. H. Goulden, (1939). - Methods of statistical analysis, 1st ed. Page 18. - https://archive.org/stream/methodsofstatist031744mbp

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(goulden.splitsplit)
-dat <- goulden.splitsplit
-
-libs(desplot)
-
-## Experiment design. Goulden p. 152-153
-## desplot(gen ~ col*row, data=dat,
-##         out1=block, out2=trt, text=dust, col=inoc, cex=1,
-##         main="goulden.splitsplit")
-
-desplot(dat, yield ~ col*row, 
-        out1=block, out2=gen,
-        col=inoc, num=trt, cex=1,
-        main="goulden.splitsplit")
-
    
-
-# Match Goulden table 40
-m1 <- aov(yield ~ gen
-          + dust + dry + dust:dry + gen:dust + gen:dry + gen:dust:dry
-          + inoc + inoc:gen + inoc:dust + inoc:dry
-          + inoc:dust:dry +inoc:gen:dust + inoc:gen:dry
-          + Error(block/(gen+gen:dust:dry+gen:inoc:dry)), data=dat)
-
    #> Warning: Error() model is singular
    summary(m1)
-
    #> 
-#> Error: block
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> Residuals  3  989.5   329.8               
-#> 
-#> Error: block:gen
-#>           Df Sum Sq Mean Sq F value Pr(>F)  
-#> gen        1   3639    3639   16.86 0.0262 *
-#> Residuals  3    648     216                 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: block:gen:dust:dry
-#>              Df Sum Sq Mean Sq F value   Pr(>F)    
-#> dust          4  987.6  246.90  12.587 2.65e-07 ***
-#> dry           1  117.3  117.31   5.980   0.0178 *  
-#> dust:dry      4   46.2   11.54   0.588   0.6725    
-#> gen:dust      4  146.7   36.67   1.869   0.1292    
-#> gen:dry       1   91.5   91.51   4.665   0.0352 *  
-#> gen:dust:dry  4  148.1   37.02   1.887   0.1260    
-#> Residuals    54 1059.2   19.62                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: block:gen:dry:inoc
-#>              Df Sum Sq Mean Sq F value  Pr(>F)    
-#> inoc          1  965.3   965.3  90.023 6.3e-07 ***
-#> gen:inoc      1    0.3     0.3   0.029  0.8686    
-#> dry:inoc      1   68.9    68.9   6.426  0.0262 *  
-#> gen:dry:inoc  1    3.9     3.9   0.364  0.5574    
-#> Residuals    12  128.7    10.7                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>               Df Sum Sq Mean Sq F value   Pr(>F)    
-#> dust:inoc      4  379.8   94.95   6.153 0.000394 ***
-#> dust:dry:inoc  4   25.8    6.45   0.418 0.794754    
-#> gen:dust:inoc  4  119.4   29.85   1.935 0.118502    
-#> Residuals     52  802.4   15.43                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
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    Developed by Kevin Wright.

    -
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    Site built with pkgdown 1.6.1.

    -
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    - - - - - - - - diff --git a/docs/reference/graybill.heteroskedastic-1.png b/docs/reference/graybill.heteroskedastic-1.png deleted file mode 100644 index 5eb5d5f2..00000000 Binary files a/docs/reference/graybill.heteroskedastic-1.png and /dev/null differ diff --git a/docs/reference/graybill.heteroskedastic.html b/docs/reference/graybill.heteroskedastic.html deleted file mode 100644 index fa2a19e0..00000000 --- a/docs/reference/graybill.heteroskedastic.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    -
    -

    Multi-environment trial of wheat varieties with heteroskedastic yields

    - - -
    - -
    -

    Wheat varieties with heteroskedastic yields

    -
    - - - -

    Format

    - -

    A data frame with 52 observations on the following 3 variables.

    -
    env

    environment, 13 levels

    -
    gen

    genotype, 4 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Yield of 4 varieties of wheat at 13 locations in Oklahoma, USA.

    -

    The data was used to explore variability between varieties.

    -

    Source

    - -

    F. A. Graybill, 1954. - Variance heterogeneity in a randomized block design, - Biometrics, 10, 516-520.

    -

    References

    - -

    Hans-Pieter Piepho, 1994. - Missing observations in the analysis of stability. - Heredity, 72, 141--145. - https://doi.org/10.1038/hdy.1994.20

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(graybill.heteroskedastic)
-dat <- graybill.heteroskedastic
-
-# Genotypes are obviously not homoscedastic
-boxplot(yield ~ gen, dat, main="graybill.heteroskedastic")
-
    
-# Shukla stability variance of each genotype, same as Grubbs' estimate
-# Matches Piepho 1994 page 143.
-# Do not do this!  Nowadays, use mixed models instead.
-libs("reshape2")
-datm <- acast(dat, gen~env)
-
    #> Using yield as value column: use value.var to override.
    w <- datm
-w <- sweep(w, 1, rowMeans(datm))
-w <- sweep(w, 2, colMeans(datm))
-w <- w + mean(datm)
-w <- rowSums(w^2)
-k=4; n=13
-sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1))
-## sig2
-##     G1     G2     G3     G4
-## 145.98 -14.14  75.15  18.25
-
-var.shukla <- function(x,N){
-  # Estimate variance of shukla stability statistics
-  # Piepho 1994 equation (5)
-  K <- length(x) # num genotypes
-  S <- outer(x,x)
-  S1 <- diag(S)
-  S2 <- rowSums(S) - S1
-  S[!upper.tri(S)] <- 0 # Make S upper triangular
-  # The ith element of S3 is the sum of the upper triangular elements of S,
-  # excluding the ith row and ith column
-  S3 <- sum(S) - rowSums(S) - colSums(S)
-  var.si2 <- 2*S1/(N-1) + 4/( (N-1)*(K-1)^2 ) * ( S2 + S3/(K-2)^2 )
-  return(var.si2)
-}
-
-# Set negative estimates to zero
-sig2[sig2<0] <- 0
-
-# Variance of shukla stat. Match Piepho 1994, table 5, example 1
-var.shukla(sig2,13)
-
    #>        G1        G2        G3        G4 
-#> 4069.3296  138.9424 1423.0797  306.5270 
    ##        G1        G2        G3        G4 
-## 4069.3296  138.9424 1423.0797  306.5270 
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/gregory.cotton.html b/docs/reference/gregory.cotton.html deleted file mode 100644 index 3adb3e8c..00000000 --- a/docs/reference/gregory.cotton.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - - -Factorial experiment of cotton in Sudan. — gregory.cotton • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Factorial experiment of cotton in Sudan.

    - - -
    - -
    -

    Factorial experiment of cotton in Sudan.

    -
    - - 
    data("gregory.cotton")
    - - -

    Format

    - -

    A data frame with 144 observations on the following 6 variables.

    -
    yield

    a numeric vector

    -
    year

    year

    -
    nitrogen

    nitrogen level

    -
    date

    sowing date

    -
    water

    irrigation amount

    -
    spacing

    spacing between plants

    - -
    - -

    Details

    - -

    Experiment conducted in Sudan at the Gezira Research Farm in 1929-1930 - and 1930-1931. The effects on yield of four factors was studied in - all possible combinations.

    -

    Sowing dates in 1929: D1 = Jul 24, D2 = Aug 11, D3 = Sep 2, D4 = Sep 25.

    -

    Spacing: S1 = 25 cm between holes, S2 = 50 cm, S3 = 75 cm. The usual - spacing is 50-70 cm.

    -

    Irrigation: I1 = Light, I2 = Medium, I3 = Heavy.

    -

    Nitrogen: N0 = None/Control, N1 = 600 rotls/feddan.

    -

    In each year there were 4*3*2*2=72 treatments, each replicated four - times. The means are given here.

    -

    Gregory (1932) has two interesting graphics: - 1. radial bar plot - 2. photographs of 3D model of treatment means.

    -

    Source

    - - - -

    Gregory, FG and Crowther, F and Lambert, AR (1932). - The interrelation of factors controlling the production of cotton - under irrigation in the Sudan. - The Journal of Agricultural Science, 22, 617-638. Table 1, 10. - https://doi.org/10.1017/S0021859600054137

    -

    References

    - -

    Paterson, D. Statistical Technique in Agricultural Research, p. 211.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gregory.cotton)
-dat <- gregory.cotton
-
-# Main effect means, Gregory table 2
-## libs(dplyr)
-## dat 
-## dat 
-## dat 
-## dat 
-
-
-# Figure 2 of Gregory. Not recommended, but an interesting exercise.
-# https://stackoverflow.com/questions/13887365
-if(FALSE){
-  libs(ggplot2)
-  d1 <- subset(dat, year=="Y1")
-  d1 <- transform(d1, grp=factor(paste(date,nitrogen,water,spacing)))
-  d1 <- d1[order(d1$grp),] # for angles
-  # Rotate labels on the left half 180 deg. First 18, last 18 labels
-  d1$ang <- 90+seq(from=(360/nrow(d1))/1.5, to=(1.5*(360/nrow(d1)))-360,
-                   length.out=nrow(d1))+80
-  d1$ang[1:18] <- d1$ang[1:18] + 180
-  d1$ang[55:72] <- d1$ang[55:72] + 180
-  # Lables on left half to right-adjusted
-  d1$hjust <- 0
-  d1$hjust[1:18] <- d1$hjust[55:72] <- 1
-  
-  gg <- ggplot(d1, aes(x=grp,y=yield,fill=factor(spacing))) +
-    geom_col() +
-    guides(fill=FALSE) + # no legend for 'spacing'
-    coord_polar(start=-pi/2) + # default is to start at top
-    labs(title="gregory.cotton 1929",x="",y="",label="") +
-    # The bar columns are centered on 1:72, subtract 0.5 to add radial axes
-    geom_vline(xintercept = seq(1, 72, by=3)-0.5, color="gray", size=.25) +
-    geom_vline(xintercept = seq(1, 72, by=18)-0.5, size=1) +
-    geom_vline(xintercept = seq(1, 72, by=9)-0.5, size=.5) +
-    geom_hline(yintercept=c(1,2,3)) + 
-    geom_text(data=d1, aes(x=grp, y=max(yield), label=grp, angle=ang, hjust=hjust),
-              size=2) +
-    theme(panel.background=element_blank(),
-          axis.title=element_blank(),
-          panel.grid=element_blank(),
-          axis.text.x=element_blank(),
-          axis.text.y=element_blank(),
-          axis.ticks=element_blank() )
-  print(gg)
-  
-}
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/grover.diallel.html b/docs/reference/grover.diallel.html deleted file mode 100644 index 8f304104..00000000 --- a/docs/reference/grover.diallel.html +++ /dev/null @@ -1,291 +0,0 @@ - -Diallel 6x6 — grover.diallel • agridat - - -
    -
    - - - -
    -
    -
    -

    Diallel 6x6

    - - -
    - -
    -

    Diallel 6x6 in 4 blocks.

    -
    - -
    - 
    data("grover.diallel")
    -
    - -
    -

    Format

    -

    A data frame with 144 observations on the following 5 variables.

    yield
    -

    a numeric vector

    - -
    rep
    -

    a character vector

    - -
    parent1
    -

    a character vector

    - -
    parent2
    -

    a character vector

    - -
    cross
    -

    a character vector

    - - -
    -
    -

    Details

    -

    Yield for a 6x6 diallel with 4 reps.

    -

    Note: The mean for the 2x2 cross is slightly different than Grover - p. 252. There appears to be an unknown error in the one of the 4 reps - in the data on page 250.

    -
    -
    -

    Source

    -

    Grover, Deepak & Lajpat Rai (2010). - Experimental Designing And Data Analysis In Agriculture And Biology. - Agrotech Publishing Academy. Page 85. - https://archive.org/details/expldesnanddatanalinagblg00023

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  data(grover.diallel)
-  dat <- grover.diallel
-
-  anova(aov(yield ~ rep + cross, data=dat))
-#> Analysis of Variance Table
-#> 
-#> Response: yield
-#>            Df Sum Sq Mean Sq F value    Pr(>F)    
-#> rep         3   2034  677.86  5.7478  0.001113 ** 
-#> cross      35  32773  936.38  7.9399 < 2.2e-16 ***
-#> Residuals 105  12383  117.93                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-  # These effects match the GCA and SCA values in Grover table 3, page 253.
-  
-  library(lmDiallel)
-  m2 <- lm.diallel(yield ~ parent1 + parent2, Block=rep,
-                   data=dat, fct="GRIFFING1")
-  library(multcomp)
-#> Loading required package: mvtnorm
-#> Loading required package: survival
-#> 
-#> Attaching package: 'survival'
-#> The following object is masked from 'package:asreml':
-#> 
-#>     rats
-#> Loading required package: TH.data
-#> 
-#> Attaching package: 'TH.data'
-#> The following object is masked from 'package:MASS':
-#> 
-#>     geyser
-  summary( glht(linfct=diallel.eff(m2), test=adjusted(type="none")) )
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> Warning: Completion with error > abseps
-#> 
-#> 	 Simultaneous Tests for General Linear Hypotheses
-#> 
-#> Linear Hypotheses:
-#>                Estimate Std. Error t value Pr(>|t|)    
-#> Intercept == 0  93.0774     0.9050 102.851    <0.01 ***
-#> g_P1 == 0        1.4851     1.4309   1.038   1.0000    
-#> g_P2 == 0       -0.9911     1.4309  -0.693   1.0000    
-#> g_P3 == 0        2.2631     1.4309   1.582   0.9749    
-#> g_P4 == 0        5.4247     1.4309   3.791   0.0303 *  
-#> g_P5 == 0       -4.2490     1.4309  -2.969   0.1970    
-#> g_P6 == 0       -3.9328     1.4309  -2.748   0.3000    
-#> ts_P1:P1 == 0  -10.4026     4.5249  -2.299   0.6025    
-#> ts_P1:P2 == 0   -9.7214     3.2629  -2.979   0.1931    
-#> ts_P1:P3 == 0   -0.4581     3.2629  -0.140   1.0000    
-#> ts_P1:P4 == 0   17.0428     3.2629   5.223    <0.01 ***
-#> ts_P1:P5 == 0   25.4765     3.2629   7.808    <0.01 ***
-#> ts_P1:P6 == 0  -21.9372     3.2629  -6.723    <0.01 ***
-#> ts_P2:P1 == 0   -9.7214     3.2629  -2.979   0.1925    
-#> ts_P2:P2 == 0    7.0899     4.5249   1.567   0.9772    
-#> ts_P2:P3 == 0   13.7157     3.2629   4.203   0.0110 *  
-#> ts_P2:P4 == 0   -8.7710     3.2629  -2.688   0.3351    
-#> ts_P2:P5 == 0   13.5728     3.2629   4.160   0.0124 *  
-#> ts_P2:P6 == 0  -15.8860     3.2629  -4.869    <0.01 ** 
-#> ts_P3:P1 == 0   -0.4581     3.2629  -0.140   1.0000    
-#> ts_P3:P2 == 0   13.7157     3.2629   4.203   0.0108 *  
-#> ts_P3:P3 == 0  -23.5335     4.5249  -5.201    <0.01 ***
-#> ts_P3:P4 == 0    1.2699     3.2629   0.389   1.0000    
-#> ts_P3:P5 == 0    0.1836     3.2629   0.056   1.0000    
-#> ts_P3:P6 == 0    8.8224     3.2629   2.704   0.3256    
-#> ts_P4:P1 == 0   17.0428     3.2629   5.223    <0.01 ***
-#> ts_P4:P2 == 0   -8.7710     3.2629  -2.688   0.3352    
-#> ts_P4:P3 == 0    1.2699     3.2629   0.389   1.0000    
-#> ts_P4:P4 == 0  -12.2868     4.5249  -2.715   0.3193    
-#> ts_P4:P5 == 0   -9.9156     3.2629  -3.039   0.1707    
-#> ts_P4:P6 == 0   12.6607     3.2629   3.880   0.0245 *  
-#> ts_P5:P1 == 0   25.4765     3.2629   7.808    <0.01 ***
-#> ts_P5:P2 == 0   13.5728     3.2629   4.160   0.0123 *  
-#> ts_P5:P3 == 0    0.1836     3.2629   0.056   1.0000    
-#> ts_P5:P4 == 0   -9.9156     3.2629  -3.039   0.1705    
-#> ts_P5:P5 == 0  -30.4793     4.5249  -6.736    <0.01 ***
-#> ts_P5:P6 == 0    1.1619     3.2629   0.356   1.0000    
-#> ts_P6:P1 == 0  -21.9372     3.2629  -6.723    <0.01 ***
-#> ts_P6:P2 == 0  -15.8860     3.2629  -4.869    <0.01 ** 
-#> ts_P6:P3 == 0    8.8224     3.2629   2.704   0.3259    
-#> ts_P6:P4 == 0   12.6607     3.2629   3.880   0.0244 *  
-#> ts_P6:P5 == 0    1.1619     3.2629   0.356   1.0000    
-#> ts_P6:P6 == 0   15.1782     4.5249   3.354   0.0851 .  
-#> r_P1:P2 == 0     3.1600     3.8395   0.823   1.0000    
-#> r_P1:P3 == 0    -5.8625     3.8395  -1.527   0.9831    
-#> r_P1:P4 == 0    -2.0850     3.8395  -0.543   1.0000    
-#> r_P1:P5 == 0     4.5000     3.8395   1.172   0.9996    
-#> r_P1:P6 == 0    -0.1425     3.8395  -0.037   1.0000    
-#> r_P2:P1 == 0    -3.1600     3.8395  -0.823   1.0000    
-#> r_P2:P3 == 0     3.5100     3.8395   0.914   1.0000    
-#> r_P2:P4 == 0    -0.5700     3.8395  -0.148   1.0000    
-#> r_P2:P5 == 0    -1.4800     3.8395  -0.385   1.0000    
-#> r_P2:P6 == 0     0.9725     3.8395   0.253   1.0000    
-#> r_P3:P1 == 0     5.8625     3.8395   1.527   0.9832    
-#> r_P3:P2 == 0    -3.5100     3.8395  -0.914   1.0000    
-#> r_P3:P4 == 0    -0.6400     3.8395  -0.167   1.0000    
-#> r_P3:P5 == 0     3.0100     3.8395   0.784   1.0000    
-#> r_P3:P6 == 0     0.6550     3.8395   0.171   1.0000    
-#> r_P4:P1 == 0     2.0850     3.8395   0.543   1.0000    
-#> r_P4:P2 == 0     0.5700     3.8395   0.148   1.0000    
-#> r_P4:P3 == 0     0.6400     3.8395   0.167   1.0000    
-#> r_P4:P5 == 0     0.9475     3.8395   0.247   1.0000    
-#> r_P4:P6 == 0    -1.4350     3.8395  -0.374   1.0000    
-#> r_P5:P1 == 0    -4.5000     3.8395  -1.172   0.9996    
-#> r_P5:P2 == 0     1.4800     3.8395   0.385   1.0000    
-#> r_P5:P3 == 0    -3.0100     3.8395  -0.784   1.0000    
-#> r_P5:P4 == 0    -0.9475     3.8395  -0.247   1.0000    
-#> r_P5:P6 == 0    -1.9075     3.8395  -0.497   1.0000    
-#> r_P6:P1 == 0     0.1425     3.8395   0.037   1.0000    
-#> r_P6:P2 == 0    -0.9725     3.8395  -0.253   1.0000    
-#> r_P6:P3 == 0    -0.6550     3.8395  -0.171   1.0000    
-#> r_P6:P4 == 0     1.4350     3.8395   0.374   1.0000    
-#> r_P6:P5 == 0     1.9075     3.8395   0.497   1.0000    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> (Adjusted p values reported -- single-step method)
-#> 
-  ## Linear Hypotheses:
-  ##                Estimate Std. Error t value Pr(>|t|)    
-  ## Intercept == 0  93.0774     0.9050 102.851    <0.01 ***
-  ## g_P1 == 0        1.4851     1.4309   1.038   1.0000    
-  ## g_P2 == 0       -0.9911     1.4309  -0.693   1.0000    
-  ## g_P3 == 0        2.2631     1.4309   1.582   0.9748    
-  ## g_P4 == 0        5.4247     1.4309   3.791   0.0302 *  
-  ## g_P5 == 0       -4.2490     1.4309  -2.969   0.1972    
-  ## g_P6 == 0       -3.9328     1.4309  -2.748   0.3008    
-  ## ts_P1:P1 == 0  -10.4026     4.5249  -2.299   0.6014    
-  ## ts_P1:P2 == 0   -9.7214     3.2629  -2.979   0.1933    
-  ## ts_P1:P3 == 0   -0.4581     3.2629  -0.140   1.0000    
-  ## ts_P1:P4 == 0   17.0428     3.2629   5.223    <0.01 ***
-  ## ts_P1:P5 == 0   25.4765     3.2629   7.808    <0.01 ***
-  ## ts_P1:P6 == 0  -21.9372     3.2629  -6.723    <0.01 ***
-  ## ts_P2:P1 == 0   -9.7214     3.2629  -2.979   0.1928    
-  ## ts_P2:P2 == 0    7.0899     4.5249   1.567   0.9773    
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/grover.rcb.subsample.html b/docs/reference/grover.rcb.subsample.html deleted file mode 100644 index c1bf90c8..00000000 --- a/docs/reference/grover.rcb.subsample.html +++ /dev/null @@ -1,162 +0,0 @@ - -Rice RCB with subsamples — grover.rcb.subsample • agridat - - -
    -
    - - - -
    -
    -
    -

    Rice RCB with subsamples

    - - -
    - -
    -

    An experiment on rice with 9 fertilizer treatments in 4 blocks, - 4 hills per plot.

    -
    - -
    - 
    data("grover.rcb.subsample")
    -
    - -
    -

    Format

    -

    A data frame with 144 observations on the following 4 variables.

    tiller
    -

    number of tillers

    - -
    trt
    -

    treatment factor

    - -
    block
    -

    block factor

    - -
    unit
    -

    subsample unit

    - - -
    -
    -

    Details

    -

    An experiment on rice with 9 fertilizer treatments in 4 blocks, - 4 hills per plot. The response variable is tiller count (per hill). - The hills are sampling units.

    -
    -
    -

    Source

    -

    Grover, Deepak & Lajpat Rai (2010). - Experimental Designing And Data Analysis In Agriculture And Biology. - Agrotech Publishing Academy. Page 85. - https://archive.org/details/expldesnanddatanalinagblg00023

    -
    -
    -

    References

    -

    None.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  data(grover.rcb.subsample)
-  # Fixed-effects ANOVA. Matches Grover page 86.
-  anova(aov(tiller ~ block + trt + block:trt, data=grover.rcb.subsample))
-#> Analysis of Variance Table
-#> 
-#> Response: tiller
-#>            Df Sum Sq Mean Sq F value  Pr(>F)    
-#> block       3    930  310.01  3.6918 0.01415 *  
-#> trt         8  11816 1477.00 17.5891 < 2e-16 ***
-#> block:trt  24   4721  196.71  2.3425 0.00158 ** 
-#> Residuals 108   9069   83.97                    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-  ## Response: tiller
-  ##            Df Sum Sq Mean Sq F value  Pr(>F)    
-  ## block       3    930  310.01  3.6918 0.01415 *  
-  ## trt         8  11816 1477.00 17.5891 < 2e-16 ***
-  ## block:trt  24   4721  196.71  2.3425 0.00158 ** 
-  ## Residuals 108   9069   83.97                    
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/gumpertz.pepper-1.png b/docs/reference/gumpertz.pepper-1.png deleted file mode 100644 index 60bbaadf..00000000 Binary files a/docs/reference/gumpertz.pepper-1.png and /dev/null differ diff --git a/docs/reference/gumpertz.pepper-2.png b/docs/reference/gumpertz.pepper-2.png deleted file mode 100644 index 3d99a87a..00000000 Binary files a/docs/reference/gumpertz.pepper-2.png and /dev/null differ diff --git a/docs/reference/gumpertz.pepper-3.png b/docs/reference/gumpertz.pepper-3.png deleted file mode 100644 index 325428b4..00000000 Binary files a/docs/reference/gumpertz.pepper-3.png and /dev/null differ diff --git a/docs/reference/gumpertz.pepper.html b/docs/reference/gumpertz.pepper.html deleted file mode 100644 index 6dd981fd..00000000 --- a/docs/reference/gumpertz.pepper.html +++ /dev/null @@ -1,265 +0,0 @@ - - - - - - - - -Phytophtera disease incidence in a pepper field — gumpertz.pepper • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Phytophtera disease incidence in a pepper field

    - - -
    - -
    -

    Phytophtera disease incidence in a pepper field

    -
    - - - -

    Format

    - -

    A data frame with 800 observations on the following 6 variables.

    -
    field

    field factor, 2 levels

    -
    row

    x ordinate

    -
    quadrat

    y ordinate

    -
    disease

    presence (Y) or absence (N) of disease

    -
    water

    soil moisture percent

    -
    leaf

    leaf assay count

    - -
    - -

    Details

    - -

    Each field is 20 rows by 20 quadrates, with 2 to 3 bell pepper plants - per plot. If any plant was wilted, dead, or had lesions, the - Phytophthora disease was considered to be present in the plot. The - soil pathogen load was assayed as the number of leaf disks colonized - out of five. In field 2, the pattern of disease presence appears to - follow soil water content. In field 1, no obvious trends were - present.

    -

    Gumpertz et al. model the presence of disease using soil moisture and - leaf assay as covariates, and using disease presence of neighboring - plots as covariates in an autologistic model.

    -

    Used with permission of Marcia Gumpertz. Research funded by USDA.

    -

    Source

    - -

    Marcia L. Gumpertz; Jonathan M. Graham; Jean B. Ristaino (1997). - Autologistic Model of Spatial Pattern of Phytophthora Epidemic - in Bell Pepper: Effects of Soil Variables on Disease Presence. - Journal of Agricultural, Biological, and Environmental - Statistics, Vol. 2, No. 2., pp. 131-156.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(gumpertz.pepper)
-dat <- gumpertz.pepper
-
-# Gumpertz deletes two outliers
-dat[ dat$field =="F1" & dat$row==20 & dat$quadrat==10, 'water'] <- NA
-dat[ dat$field =="F2" & dat$row==5 & dat$quadrat==4, 'water'] <- NA
-
-# Horizontal flip
-dat <- transform(dat, row=21-row)
-
-# Disease presence.  Gumpertz fig 1a, 2a.
-libs(desplot)
-grays <- colorRampPalette(c("#d9d9d9","#252525"))
-desplot(dat, disease ~ row*quadrat|field,
-        col.regions=c('white','black'), aspect=1, # uncertain aspect
-        main="gumpertz.pepper disease presence", )
-
    
-# Soil water.  Gumpertz fig 1b, 2b
-desplot(dat, water ~ row*quadrat|field,
-        col.regions=grays(5), aspect=1, # uncertain aspect
-        at=c(5,7.5,10,12.5,15,18),
-        main="gumpertz.pepper soil moisture")
-
    
-# Leaf assay.  Gumpertz fig 1c, 2c
-desplot(dat, leaf ~ row*quadrat|field,
-        col.regions=grays(6),
-        at=c(0,1,2,3,4,5,6)-.5, aspect=1, # uncertain aspect
-        main="gumpertz.pepper leaf assay", )
-
    
-
-# Use the inner 16x16 grid of plots in field 2
-dat2 <- droplevels(subset(dat, field=="F2" & !is.na(water) &
-                          row > 2 & row < 19 & quadrat > 2 & quadrat < 19))
-
-m21 <- glm(disease ~ water + leaf, data=dat2, family=binomial)
-coef(m21) # These match Gumpertz et al table 4, model 1
-
    #> (Intercept)       water        leaf 
-#>  -9.1019623   0.7059993   0.4603931 
    ## (Intercept)       water        leaf
-##  -9.1019623   0.7059993   0.4603931
-dat2$res21 <- resid(m21)
-if(0){
-  libs(desplot)
-  desplot(dat2, res21 ~ row*quadrat,
-          main="gumpertz.pepper field 2, model 1 residuals")
-  # Still shows obvious trends.  Gumpertz et al add spatial covariates for
-  # neighboring plots, but with only minor improvement in misclassification
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hadasch.lettuce.html b/docs/reference/hadasch.lettuce.html deleted file mode 100644 index bb2ed30b..00000000 --- a/docs/reference/hadasch.lettuce.html +++ /dev/null @@ -1,156 +0,0 @@ - -Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce • agridat - - -
    -
    - - - -
    -
    -
    -

    Lettuce resistance to downy mildew resistance (with marker data)

    - - -
    - -
    -

    Lettuce resistance to downy mildew resistance (with marker data).

    -
    - -
    - 
    data("hadasch.lettuce")
    -
    - -
    -

    Format

    -

    A data frame with 703 observations on the following 4 variables.

    loc
    -

    locations

    - -
    gen
    -

    genotype

    - -
    rep
    -

    replicate

    - -
    dmr
    -

    downy mildew resistance

    - - -
    -
    -

    Details

    - - -

    A biparental cross of 95 recombinant inbred lines of "Salinas 88" (susceptible) and "La Brillante" (highly resistant to downy mildew). The 89 RILs were evaluated in field experiments performed in 2010 and 2011 near Salinas, California. Each loc had a 2 or 3 rep RCB design. There were approximately 30 plants per plot. Plots were scored 0 (no disease) to 5 (severe disease).

    -

    The authors used the following model in a first-stage analysis to compute adjusted means for each genotype:

    -

    y = loc + gen + gen:loc + block:loc + error

    -

    where gen was fixed and all other terms random. The adjusted means were used as the response in a second stage:

    -

    mn = 1 + Zu + error

    -

    where Z is the design matrix of marker effects. The error term is fixed to have covariance matrix R be the same as from the first stage.

    -

    Genotyping was performed with 95 SNPs and 205 amplified fragment length -polymporphism markers so that a marker matrix M (89×300) was -provided. The biallelic marker M(iw) for the ith genotype and the wth -marker with alleles A1 (i.e. the reference allele) and A2 was coded as 1 -for A1,A1, -1 for A2,A2 and 0 for A1,A2 and A2,A2.

    -

    The electronic version of the lettuce data are licensed CC-BY 4 and were -downloaded 20 Feb 2021. -https://figshare.com/articles/dataset/Lettuce_trial_phenotypic_and_marker_data_/8299493

    -
    -
    -

    Source

    -

    Hadasch, S., I. Simko, R. J. Hayes, J. O. Ogutu, and H.P. Piepho (2016). -Comparing the predictive abilities of phenotypic and -marker-assisted selection methods in a biparental lettuce population. -Plant Genome 9. -https://doi.org/10.3835/plantgenome2015.03.0014

    -
    -
    -

    References

    -

    Hayes, R. J., Galeano, C. H., Luo, Y., Antonise, R., & Simko, I. (2014). -Inheritance of Decay of Fresh-cut Lettuce in a Recombinant Inbred Line Population from "Salinas 88" × "La Brillante". -J. Amer. Soc. Hort. Sci., 139(4), 388-398. -https://doi.org/10.21273/JASHS.139.4.388

    -
    - -
    -

    Examples

    - -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/hanks.sprinkler-1.png b/docs/reference/hanks.sprinkler-1.png deleted file mode 100644 index d70d82d3..00000000 Binary files a/docs/reference/hanks.sprinkler-1.png and /dev/null differ diff --git a/docs/reference/hanks.sprinkler-2.png b/docs/reference/hanks.sprinkler-2.png deleted file mode 100644 index e25e90cc..00000000 Binary files a/docs/reference/hanks.sprinkler-2.png and /dev/null differ diff --git a/docs/reference/hanks.sprinkler.html b/docs/reference/hanks.sprinkler.html deleted file mode 100644 index bb5864de..00000000 --- a/docs/reference/hanks.sprinkler.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - - -Wheat yields in a line-source sprinkler experiment — hanks.sprinkler • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Wheat yields in a line-source sprinkler experiment

    - - -
    - -
    -

    Three wheat varieties planted in 3 blocks, with a line sprinkler - crossing all whole plots.

    -
    - - - -

    Format

    - -

    A data frame with 108 observations on the following 7 variables.

    -
    block

    block

    -
    row

    row

    -
    subplot

    column

    -
    gen

    genotype, 3 levels

    -
    yield

    yield (tons/ha)

    -
    irr

    irrigation level, 1..6

    -
    dir

    direction from sprinkler, N/S

    - -
    - -

    Details

    - -

    A line-source sprinkler is placed through the middle of the experiment - (between subplots 6 and 7). Subplots closest to the sprinkler receive - the most irrigation. Subplots far from the sprinkler (near the edges) - have the lowest yields.

    -

    One data value was modified from the original (following - the example of other authors).

    -

    Source

    - -

    Hanks, R.J., Sisson, D.V., Hurst, R.L, and Hubbard K.G. (1980). - Statistical Analysis of Results from Irrigation Experiments Using the - Line-Source Sprinkler System. - Soil Science Society of America Journal, 44, 886-888. - https://doi.org/10.2136/sssaj1980.03615995004400040048x

    -

    References

    - -

    Johnson, D. E., Chaudhuri, U. N., and Kanemasu, E. T. (1983). - Statistical Analysis of Line-Source Sprinkler Irrigation Experiments - and Other Nonrandomized Experiments Using Multivariate Methods. - Soil Science Society American Journal, 47, 309-312.

    -

    Stroup, W. W. (1989). - Use of Mixed Model Procedure to Analyze Spatially Correlated Data: An - Example Applied to a Line-Source Sprinkler Irrigation Experiment. - Applications of Mixed Models in Agriculture and Related - Disciplines, Southern Cooperative Series Bulletin No. 343, 104-122.

    -

    SAS Stat User's Guide. - https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect038.htm

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(hanks.sprinkler)
-  dat <- hanks.sprinkler
-
-  # The line sprinkler is vertical between subplots 6 & 7
-  libs(desplot)
-  desplot(dat, yield~subplot*row,
-          out1=block, out2=irr, cex=1, # aspect unknown
-          num=gen, main="hanks.sprinkler")
-
      
-  
-  libs(lattice)
-  xyplot(yield~subplot|block, dat, type=c('b'), group=gen,
-         layout=c(1,3), auto.key=TRUE,
-         main="hanks.sprinkler",
-         panel=function(x,y,...){
-           panel.xyplot(x,y,...)
-           panel.abline(v=6.5, col='wheat')
-         })
-
      
-  # ----------------------------------------------------------------------------
-
-  ## This is the model from the SAS documentation
-  ## proc mixed;
-  ## class block gen dir irr;
-  ## model yield = gen|dir|irr@2;
-  ## random block block*dir block*irr;
-  ## repeated / type=toep(4) sub=block*gen r;
-  
-  # ----------------------------------------------------------------------------
-
-  # asreml 3
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-
-    dat <- transform(dat, subf=factor(subplot),
-                     irrf=factor(irr))
-    dat <- dat[order(dat$block, dat$gen, dat$subplot),]
-    
-    m1 <- asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf,
-                 data=dat,
-                 random= ~ block + block:dir + block:irrf,
-                 rcov= ~ block:gen:corb(subf, k=4))
-    
-    libs(lucid)
-    vc(m1)
-    ##                effect component std.error z.ratio constr
-    ##       block!block.var  0.2194     0.2393     0.92    pos
-    ##   block:dir!block.var  0.01768    0.03154    0.56    pos
-    ##  block:irrf!block.var  0.03539    0.03617    0.98    pos
-    ##            R!variance  0.285      0.05086    5.6     pos
-    ##                R!cor1  0.02802    0.1143     0.25  uncon
-    ##                R!cor2  0.005095   0.1278     0.04  uncon
-    ##                R!cor3 -0.3246     0.0905    -3.6   uncon
-    
-    ## # convert asreml correlations to SAS covariances
-    ## round(.2850 * c(1, .02802, .005095, -.3246),4) # res var * (cor1, cor2, cor3)
-    ## [1]  0.2850  0.0080  0.0015 -0.0925
-  }
-
-  # asreml 4
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-
-    dat <- transform(dat, subf=factor(subplot),
-                     irrf=factor(irr))
-    dat <- dat[order(dat$block, dat$gen, dat$subplot),]
-
-    # In asreml3, we can specify corb(subf, 3)
-    # In asreml4, only corb(subf, 1) runs.  corb(subf, 3) says:
-    # Correlation structure is not positive definite
-    m1 <- asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf,
-                 data=dat,
-                 random= ~ block + block:dir + block:irrf,
-                 resid = ~ block:gen:corb(subf, 2))
-
-    lucid::vc(m1)
-    #                   effect component std.error z.ratio bound 
-    #                    block   0.194     0.2231     0.87     P 0.5
-    #                block:dir   0.02729   0.04959    0.55     P 0  
-    #               block:irrf   0.02275   0.0347     0.66     P 0.1
-    #         block:gen:subf!R   0.3234    0.05921    5.5      P 0  
-    # block:gen:subf!subf!cor1   0.169     0.09906    1.7      P 0.1
-
-  }
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:44 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -22.0631      0.331054     82 17:08:44    0.0
-#>  2      -20.5511      0.353273     82 17:08:44    0.0
-#>  3      -20.8457      0.496038     82 17:08:44    0.0
-#>  4      -30.1543      0.850251     82 17:08:44    0.0 (3 restrained)
-#> Likelihood evaluation failed with fault 1005 ; trying with reduced updates
    #> Error in asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf,     data = dat, random = ~block + block:dir + block:irrf, resid = ~block:gen:corb(subf,         2)): Correlation structure is not positive definite
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hanover.whitepine-1.png b/docs/reference/hanover.whitepine-1.png deleted file mode 100644 index eebb9931..00000000 Binary files a/docs/reference/hanover.whitepine-1.png and /dev/null differ diff --git a/docs/reference/hanover.whitepine.html b/docs/reference/hanover.whitepine.html deleted file mode 100644 index 769fe508..00000000 --- a/docs/reference/hanover.whitepine.html +++ /dev/null @@ -1,307 +0,0 @@ - - - - - - - - -Mating crosses of white pine trees — hanover.whitepine • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Mating crosses of white pine trees

    - - -
    - -
    -

    Mating crosses of white pine trees

    -
    - - 
    data("hanover.whitepine")
    - - -

    Format

    - -

    A data frame with 112 observations on the following 4 variables.

    -
    rep

    replicate

    -
    female

    female parent

    -
    male

    male parent

    -
    length

    epicotyl length, cm

    - -
    - -

    Details

    - - - -

    Four male (pollen parent) White Pine trees were mated to seven female - trees and 2654 progeny were grown in four replications, one plot per - mating in each replication. Parent trees were sourced from Idaho, - USA. The data are plot means of epicotyl length.

    -

    Becker (1984) used these data to demonstrate the calculation of - heritability.

    -

    Source

    - -

    Hanover, James W and Barnes, Burton V. (1962). - Heritability of height growth in year-old western white pine. - Proc Forest Genet Workshop. 22, 71--76.

    -

    Walter A. Becker (1984). - Manual of Quantitative Genetics, 4th ed. Page 83.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(hanover.whitepine)
-  dat <- hanover.whitepine
-
-  libs(lattice)
-  # Relatively high male-female interaction in growth comared
-  # to additive gene action. Response is more consistent within
-  # male progeny than female progeny.
-  # with(dat, interaction.plot(female, male, length))
-  # with(dat, interaction.plot(male, female, length))
-  bwplot(length ~ male|female, data=dat,
-         main="hanover.whitepine - length for male:female crosses",
-         xlab="Male parent", ylab="Epicotyl length")
-
      
-  # Progeny sums match Becker p 83
-  sum(dat$length) # 380.58
-
    #> [1] 380.58
      aggregate(length ~  female + male, data=dat, FUN=sum)
-
    #>    female male length
-#> 1    F193  M17  15.87
-#> 2    F195  M17  14.49
-#> 3    F197  M17  18.89
-#> 4    F201  M17  11.93
-#> 5    F203  M17  16.61
-#> 6    F204  M17  12.82
-#> 7    F208  M17  15.91
-#> 8    F193  M19  12.49
-#> 9    F195  M19  10.95
-#> 10   F197  M19  10.39
-#> 11   F201  M19  11.02
-#> 12   F203  M19  15.90
-#> 13   F204  M19   9.94
-#> 14   F208  M19  10.78
-#> 15   F193  M22  16.94
-#> 16   F195  M22  12.80
-#> 17   F197  M22  14.06
-#> 18   F201  M22  15.01
-#> 19   F203  M22  12.92
-#> 20   F204  M22  13.65
-#> 21   F208  M22  14.58
-#> 22   F193  M58  14.52
-#> 23   F195  M58  14.28
-#> 24   F197  M58  13.94
-#> 25   F201  M58  12.41
-#> 26   F203  M58  11.63
-#> 27   F204  M58  11.78
-#> 28   F208  M58  14.07
      
-  # Sum of squares matches Becker p 85
-  m1 <- aov(length ~ rep + male + female + male:female, data=dat)
-  anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: length
-#>             Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> rep          3  1.8218  0.6073  3.0299   0.03406 *  
-#> male         3 12.3538  4.1179 20.5460 5.393e-10 ***
-#> female       6  6.4977  1.0829  5.4032 9.740e-05 ***
-#> male:female 18 13.4609  0.7478  3.7312 2.266e-05 ***
-#> Residuals   81 16.2344  0.2004                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      
-  # Variance components match Becker p. 85
-  libs(lme4)
-  libs(lucid)
-  m2 <- lmer(length ~ (1|rep) + (1|male) + (1|female) + (1|male:female), data=dat)
-  #as.data.frame(lme4::VarCorr(m2))
-  vc(m2)
-
    #>          grp        var1 var2    vcov  sdcor
-#>  male:female (Intercept) <NA> 0.1369  0.3699
-#>       female (Intercept) <NA> 0.02094 0.1447
-#>         male (Intercept) <NA> 0.1204  0.3469
-#>          rep (Intercept) <NA> 0.01453 0.1205
-#>     Residual        <NA> <NA> 0.2004  0.4477
      ##         grp        var1 var2    vcov  sdcor
-  ## male:female (Intercept) <NA> 0.1369  0.3699
-  ##      female (Intercept) <NA> 0.02094 0.1447
-  ##        male (Intercept) <NA> 0.1204  0.3469
-  ##         rep (Intercept) <NA> 0.01453 0.1205
-  ##    Residual        <NA> <NA> 0.2004  0.4477
-  
-  # Becker used this value for variability between individuals, within plot
-  s2w <- 1.109
-  
-  # Calculating heritability for individual trees
-  s2m <- .120
-  s2f <- .0209
-  s2mf <- .137
-  vp <- s2m + s2f + s2mf + s2w  # variability of phenotypes = 1.3869
-  4*s2m / vp # heritability male 0.346
-
    #> [1] 0.3460956
      4*s2f / vp # heritability female 0.06
-
    #> [1] 0.06027832
      2*(s2m+s2f)/vp # heritability male+female .203
-
    #> [1] 0.203187
      # As shown in the boxplot, heritability is stronger through the
-  # males than through the females.
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/harris.multi.uniformity-1.png b/docs/reference/harris.multi.uniformity-1.png deleted file mode 100644 index 445d8909..00000000 Binary files a/docs/reference/harris.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/harris.multi.uniformity-2.png b/docs/reference/harris.multi.uniformity-2.png deleted file mode 100644 index b9ed0486..00000000 Binary files a/docs/reference/harris.multi.uniformity-2.png and /dev/null differ diff --git a/docs/reference/harris.multi.uniformity-3.png b/docs/reference/harris.multi.uniformity-3.png deleted file mode 100644 index 440d90e0..00000000 Binary files a/docs/reference/harris.multi.uniformity-3.png and /dev/null differ diff --git a/docs/reference/harris.multi.uniformity.html b/docs/reference/harris.multi.uniformity.html deleted file mode 100644 index b3abf7ff..00000000 --- a/docs/reference/harris.multi.uniformity.html +++ /dev/null @@ -1,334 +0,0 @@ - - - - - - - - -Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trials with multiple crops, 15 years on the same land

    - - -
    - -
    -

    Uniformity trials with multiple crops, at Huntley Field Station, - Montana, 1911-1925.

    -
    - - - -

    Format

    - -

    A data frame with 1058 observations on the following 5 variables.

    -
    series

    series (field coordinate)

    -
    plot

    plot number (field coordinate)

    -
    year

    year, 1911-1925

    -
    crop

    crop factor

    -
    yield

    yield, numeric

    - -
    - -

    Details

    - - - -

    Field width: 2 plots * 317 ft + 5 feet alley = 639 feet

    -

    Field length: 23 plots * 23.3 feet = 536 feet

    -

    All yields are given in pound per plot, except sugar beets, which are - given in tons per acre. Harris (1928) shows a map of the location.

    -

    Harris 1920: - In the spring of 1911 this field was laid out into 46 plots, each - measuring 23.5 by 317 feet and containing 0.17 acre, arranged in two - parallel series of 23 plots each. The two series of plots were - separated merely by a temporary irrigation ditch. In 1911 it was - planted to sugar beets, and in the spring of 1912 it was seeded to - alfalfa, and one cutting was harvested that year. This stand remained - on the ground during 1913 and 1914, when the entire field was - fall-plowed. In 1913 three cuttings were made, but the third cutting - was lost in a heavy wind which scattered and mixed the crop before - weighings from the various plots could be made. The first cutting, - designated as alfalfa I, was made on plots one-half the original - size. The second cutting was harvested from plots one-quarter the - original size. The first and second cuttings in 1914 were weighed for - plots one-quarter the original size--that is, 0.0425-acre plots--while - the third cutting was recorded for plots one-third the original - size. These furnish the data for alfalfa I, II, and III for 1914. - Total yields for the first and second cuttings in 1913 and 1914 and - for the first, second, and third cuttings in 1914 are also considered. - In 1915 and 1916 ear corn was grown. In 1917 the fields were planted - to oats, and records were made of grain, straw, and total yield. In - 1918 silage corn was grown. In 1919 the land produced a crop of - barley.

    -

    Harris 1928: - The southeast corner of Series II, the east series, is - about 80 feet from the main canal, and the southwest corner of Series - III is about 50 feet from Ouster Coulee. The main project canal - carries normally during the irrigation season about 400 second-feet of - water. The water surface in the canal is about 4 feet above the high - corner of the field. It is evident from surface conditions, as well as - from borings made between the canal and the field, that there is - extensive seepage from the canal into the subsoil of the field. The - volume of this seepage has been larger in recent years than it was in - the earlier years of the cropping experiments, probably because the - canal bank has been worn away by internal erosion, exposing a stratum - of sandy subsoil that underlies the canal and part of the field.

    -

    Whereas in the earlier crops Series II was better for alfalfa, Series - III was better for alfalfa in the later period. The writers feel - inclined to suggest that in the earlier experiments the height of the - water table had no harmful effect upon a deep-rooted crop such as - alfalfa. It is quite possible that during drier periods the higher - water table actually favored alfalfa growth on Series II. The higher - water tables of recent years have probably had a deleterious - influence, which has been especially marked on Series II, where the - water apparently comes nearer to the surface than in Series III.

    -

    Source

    - -

    Harris, J Arthur and Scofield, CS. (1920). - Permanence of differences in the plats of an experimental field. - Jour. Agr. Res, 20, 335-356. - https://naldc.nal.usda.gov/catalog/IND43966236

    -

    Harris, J Arthur and Scofield, CS. (1928). - Further studies on the permanence of differences in the plots of an - experimental field. - Jour. Agr. Res, 36, 15--40. - https://naldc.nal.usda.gov/catalog/IND43967538

    - -

    Examples

    - 
    # \dontrun{
-library(agridat)
-
-data(harris.multi.uniformity)
-dat <- harris.multi.uniformity
-# Combine year/crop into 'harvest'
-dat <- transform(dat, harv = factor(paste0(year,".",crop)))
-# Convert 1911 from tons to pounds
-dat$yield[dat$year==1911] <- 340 * dat$yield[dat$year==1911]
-
-# Average yields. Harris 1928, table 2.
-aggregate(yield~harv, dat, mean)
-
    #>              harv     yield
-#> 1  1911.sugarbeet 4181.5565
-#> 2   1912.alfalfa1  356.5435
-#> 3   1913.alfalfa1  541.4130
-#> 4   1913.alfalfa2  483.2609
-#> 5   1914.alfalfa1  489.1304
-#> 6   1914.alfalfa2  499.3478
-#> 7   1914.alfalfa3  471.9565
-#> 8       1915.corn  522.5870
-#> 9       1916.corn  396.1522
-#> 10       1917.oat  555.8043
-#> 11     1917.straw  521.5435
-#> 12    1918.silage 3175.4348
-#> 13    1919.barley  358.1957
-#> 14     1919.straw  230.5000
-#> 15    1920.silage 2394.1304
-#> 16    1921.barley  525.6087
-#> 17     1921.straw  324.8261
-#> 18  1922.alfalfa3  379.4565
-#> 19  1923.alfalfa1  604.1304
-#> 20  1923.alfalfa3  300.2174
-#> 21  1924.alfalfa2  642.3913
-#> 22  1924.alfalfa3  357.6087
-#> 23    1925.silage 4267.9348
    
-# Corrgram
-libs(reshape2,corrgram)
-
    #> 
-#> Attaching package: 'corrgram'
    #> The following object is masked from 'package:lattice':
-#> 
-#>     panel.fill
    mat <- acast(dat, series+plot~harv, value.var='yield')
-corrgram(mat, main="harris.multi.uniformity - correlation of crop yields")
-
    
-
-# Compare to Harris 1928, table 4.  More positive than negative correlations.
-# densityplot(as.vector(cor(mat)), xlab="correlations",
-#             main="harris.multi.uniformity")
-
-  
-# Standardize yields for each year
-mats <- scale(mat)
-# Melt and re-name columns so we can make field maps.  Obvious spatial
-# patterns that persist over years
-d2 <- melt(mats)
-names(d2) <- c('ord','harv','yield')
-d2$series <- as.numeric(substring(d2$ord,1,1))
-d2$plot <- as.numeric(substring(d2$ord,3))
-
-# Series 2 is on the east side, so switch 2 and 3 for correct plotting
-d2$xord <- 5 - dat$series
-# Note that for alfalfa, higher-yielding plots in 1912-1914 were
-# lower-yielding in 1922-1923.
-libs(desplot)
-desplot(d2, yield ~ xord*plot|harv,
-        aspect=536/639, flip=TRUE, # true aspect
-        main="harris.multi.uniformity")
-
    
-# Crude fertility map by averaging across years shows probable
-# sub-surface water effects
-agg <- aggregate(yield ~ xord + plot, data=d2, mean)
-desplot(agg, yield ~ xord + plot,
-        aspect=536/639, # true aspect
-        main="harris.multi.uniformity fertility")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/harris.wateruse-1.png b/docs/reference/harris.wateruse-1.png deleted file mode 100644 index fb774d1e..00000000 Binary files a/docs/reference/harris.wateruse-1.png and /dev/null differ diff --git a/docs/reference/harris.wateruse-2.png b/docs/reference/harris.wateruse-2.png deleted file mode 100644 index e72258fd..00000000 Binary files a/docs/reference/harris.wateruse-2.png and /dev/null differ diff --git a/docs/reference/harris.wateruse-3.png b/docs/reference/harris.wateruse-3.png deleted file mode 100644 index f5e489df..00000000 Binary files a/docs/reference/harris.wateruse-3.png and /dev/null differ diff --git a/docs/reference/harris.wateruse-4.png b/docs/reference/harris.wateruse-4.png deleted file mode 100644 index 1f71c1a7..00000000 Binary files a/docs/reference/harris.wateruse-4.png and /dev/null differ diff --git a/docs/reference/harris.wateruse.html b/docs/reference/harris.wateruse.html deleted file mode 100644 index 6ad2eacc..00000000 --- a/docs/reference/harris.wateruse.html +++ /dev/null @@ -1,392 +0,0 @@ - - - - - - - - -Water use by horticultural trees — harris.wateruse • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Water use by horticultural trees

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    Water use by horticultural trees

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    - - - -

    Format

    - -

    A data frame with 1040 observations on the following 6 variables.

    -
    species

    species factor, 2 levels

    -
    age

    age factor, 2 levels

    -
    tree

    tree factor, 40 (non-consecutive) levels

    -
    day

    day, numeric

    -
    water

    water use, numeric

    - -
    - -

    Details

    - -

    Ten trees in each of four groups (two species, by two ages) were - assessed for water usage, approximately every five days.

    -

    Missing values are included for the benefit of asreml, which needs a - 'balanced' data set due to the kronecker-like syntax of the R matrix.

    -

    Used with permission of Roger Harris at Virginia Polytechnic.

    -

    Source

    - -

    Schabenberger, Oliver and Francis J. Pierce. 2002. - Contemporary Statistical Models for the Plant and Soil Sciences. - CRC Press. Page 512.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(harris.wateruse)
-  dat <- harris.wateruse
-
-  # Compare to Schabenberger & Pierce, fig 7.23
-  libs(latticeExtra)
-
    #> 
-#> Attaching package: 'latticeExtra'
    #> The following object is masked from 'package:corrgram':
-#> 
-#>     panel.ellipse
      useOuterStrips(xyplot(water ~ day|species*age,dat, as.table=TRUE,
-                        group=tree, type=c('p','smooth'),
-                        main="harris.wateruse 2 species, 2 ages (10 trees each)"))
-
    
-
-  # Note that measurements on day 268 are all below the trend line and
-  # thus considered outliers.  Delete them.
-  dat <- subset(dat, day!=268)
-
-
-  # Schabenberger figure 7.24
-  xyplot(water ~ day|tree,dat, subset=age=="A2" & species=="S2",
-         as.table=TRUE, type=c('p','smooth'),
-         ylab="Water use profiles of individual trees",
-         main="harris.wateruse (Age 2, Species 2)")
-
      
-  # Rescale day for nicer output, and convergence issues, add quadratic term
-  dat <- transform(dat, ti=day/100)
-  dat <- transform(dat, ti2=ti*ti)
-
-  # Start with a subgroup: age 2, species 2
-  d22 <- droplevels(subset(dat, age=="A2" & species=="S2"))
-
-  # ----- Model 1, for subgroup A2,S2
-
-  # First, a fixed quadratic that is common to all trees, plus
-  # a random quadratic deviation for each tree.
-  
-  ## Schabenberger, Output 7.26
-  ## proc mixed;
-  ##   class tree;
-  ##   model water = ti ti*ti / s;
-  ##   random intercept ti ti*ti/subject=tree;
-
-  libs(nlme,lucid)
-
    #> 
-#> Attaching package: 'nlme'
    #> The following object is masked from 'package:lme4':
-#> 
-#>     lmList
      ## We use pdDiag() to get uncorrelated random effects
-  m1n <- lme(water ~ 1 + ti + ti2, data=d22, na.action=na.omit,
-             random = list(tree=pdDiag(~1+ti+ti2)))
-  # vc(m1n)
-  ##       effect variance    stddev
-  ##  (Intercept)   0.2691 0.5188
-  ##           ti   0      0.0000144
-  ##          ti2   0      0.0000039
-  ##     Residual   0.1472 0.3837
-
-  if(0){  
-    # Various other models with lme4 & asreml
-    
-    libs(lme4, lucid)
-    m1l <- lmer(water ~ 1 + ti + ti2 + (1|tree) +
-                  (0+ti|tree) + (0+ti2|tree), data=d22)
-    # vc(m1l)
-    ##      grp        var1 var2   vcov  sdcor
-    ##     tree (Intercept) <NA> 0.2691 0.5188
-    ##   tree.1          ti <NA> 0      0
-    ##   tree.2         ti2 <NA> 0      0
-    ## Residual        <NA> <NA> 0.1472 0.3837
-
-
-    # Once the overall quadratic trend has been removed, there is not
-    # too much evidence for consecutive observations being correlated
-    ## d22r <- subset(d22, !is.na(water))
-    ## d22r$res <- resid(m1n)
-    ## xyplot(res ~ day|tree,d22r,
-    ##        as.table=TRUE, type=c('p','smooth'),
-    ##        ylab="residual",
-    ##        main="harris.wateruse - Residuals of individual trees")
-    ## op <- par(mfrow=c(4,3))
-    ## tapply(d22r$res, d22r$tree, acf)
-    ## par(op)
-    
-    # ----- Model 2, add correlation of consecutive measurements
-    
-    ## Schabenberger (page 516) adds correlation.
-    ## Note how the fixed quadratic model is on the "ti = day/100" scale
-    ## and the correlated observations are on the "day" scale.  The
-    ## only impact this has on the fitted model is to increase the
-    ## correlation parameter by a factor of 100, which was likely
-    ## done to get better convergence.
-    
-    ## proc mixed data=age2sp2;
-    ##   class tree;
-    ##   model water = ti ti*ti / s ;
-    ##   random intercept /subject=tree s;
-    ##   repeated /subject=tree type=sp(exp)(day);
-
-    ## Same as SAS, use ti for quadratic, day for correlation
-    m2l <- lme(water ~ 1 + ti + ti2, data=d22,
-               random = ~ 1|tree,
-               cor = corExp(form=~ day|tree),
-               na.action=na.omit)
-    m2l # Match output 7.27.  Same fixef, ranef, variances, exp corr
-
-    # vc(m2l)
-    ##       effect variance stddev
-    ##  (Intercept)   0.2656 0.5154
-    ##     Residual   0.1541 0.3926
-
-    # ---
-
-    ## Now use asreml.  When I tried rcov=~tree:exp(ti),
-    ## the estimated parameter value was on the 'boundary', i.e. 0.
-    ## Changing rcov to the 'day' scale produced a sensible estimate
-    ## that matched SAS.
-    ## Note: SAS and asreml use different parameterizations for the correlation
-    ## SAS uses exp(-d/phi) and asreml uses phi^d.
-    ## SAS reports 3.79, asreml reports 0.77, and exp(-1/3.7945) = 0.7683274
-    ## Note: normally a quadratic would be included as 'pol(day,2)'
-
-    libs(asreml)
-    d22 <- d22[order(d22$tree, d22$day),]
-    m2a <- asreml(water ~ 1 + ti + ti2,
-                  data=d22,
-                  random = ~ tree,
-                  rcov=~tree:exp(day))
-    # code below is for asremlr 4
-    ## m2a <- asreml(water ~ 1 + ti + ti2,
-    ##               data=d22,
-    ##               random = ~ tree,
-    ##               resid= ~tree:exp(day))
-
-    vc(m2a)
-    ##         effect component std.error z.ratio constr
-    ##  tree!tree.var    0.2656   0.1301      2      pos
-    ##     R!variance    0.1541   0.01611     9.6    pos
-    ##      R!day.pow    0.7683   0.04191    18    uncon
-
-
-    # ----- Model 3. Full model for all species/ages.  Schabenberger p. 518
-
-    ## /* Continuous AR(1) autocorrelations included      */
-    ## proc mixed data=wateruse;
-    ##   class age species tree;
-    ##   model water = age*species age*species*ti age*species*ti*ti / noint s;
-    ##   random intercept ti / subject=age*species*tree s;
-    ##   repeated / subject=age*species*tree type=sp(exp)(day);
-    
-
-    m3l <- lme(water ~ 0 + age:species + age:species:ti + age:species:ti2,
-               data=dat, na.action=na.omit,
-               random = list(tree=pdDiag(~1+ti)),
-               cor = corExp(form=~ day|tree) )
-
-    m3l # Match Schabenberger output 7.27.  Same fixef, ranef, variances, exp corr
-
-    # vc(m3l)
-    ##       effect variance stddev
-    ##  (Intercept)  0.1549  0.3936
-    ##           ti  0.02785 0.1669
-    ##     Residual  0.16    0.4
-
-    # --- asreml
-    
-    dat <- dat[order(dat$tree,dat$day),]
-    m3a <- asreml(water ~ 0 + age:species + age:species:ti + age:species:ti2,
-                  data=dat,
-                  random = ~ age:species:tree + age:species:tree:ti,
-                  rcov = ~ tree:exp(day) )
-    # code for asremlr 4
-    ## m3a <- asreml(water ~ 0 + age:species + age:species:ti + age:species:ti2,
-    ##               data=dat,
-    ##               random = ~ age:species:tree + age:species:tree:ti,
-    ##               resid = ~ tree:exp(day) )
-    
-    # vc(m3a) # Note: day.pow = .8091 = exp(-1/4.7217)
-    ##                       effect component std.error z.ratio constr
-    ##     age:species:tree!age.var   0.1549   0.07192      2.2    pos
-    ##  age:species:tree:ti!age.var   0.02785  0.01343      2.1    pos
-    ##                   R!variance   0.16     0.008917    18      pos
-    ##                    R!day.pow   0.8091   0.01581     51    uncon
-
-  }
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/harrison.priors-1.png b/docs/reference/harrison.priors-1.png deleted file mode 100644 index c40f34d6..00000000 Binary files a/docs/reference/harrison.priors-1.png and /dev/null differ diff --git a/docs/reference/harrison.priors-2.png b/docs/reference/harrison.priors-2.png deleted file mode 100644 index ab2cba84..00000000 Binary files a/docs/reference/harrison.priors-2.png and /dev/null differ diff --git a/docs/reference/harrison.priors.html b/docs/reference/harrison.priors.html deleted file mode 100644 index 465dbc19..00000000 --- a/docs/reference/harrison.priors.html +++ /dev/null @@ -1,286 +0,0 @@ - - - - - - - - -Ranges of analytes in soybean from other authors — harrison.priors • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Ranges of analytes in soybean from other authors

    - - -
    - -
    -

    Ranges of analytes in soybean from other authors

    -
    - - - -

    Format

    - -

    A data frame with 80 observations on the following 5 variables.

    -
    source

    Source document

    -
    substance

    Analyte substance

    -
    min

    minimum amount (numeric)

    -
    max

    maximum analyte amount (numeric)

    -
    number

    number of substances

    - -
    - -

    Details

    - - - -

    Harrison et al. show how to construct an informative Bayesian prior - from previously-published ranges of concentration for several - analytes.

    -

    The units for daidzein, genistein, and glycitein are micrograms per - gram.

    -

    The raffinose and stachyose units were converted to a common 'percent' - scale.

    -

    The author names in the 'source' variable are shortened forms of the - citations in the supplemental information of Harrison et al.

    -

    Source

    - -

    Jay M. Harrison, Matthew L. Breeze, Kristina H. Berman, George - G. Harrigan. 2013. - Bayesian statistical approaches to compositional analyses of transgenic - crops 2. Application and validation of informative prior distributions. - Regulatory Toxicology and Pharmacology, 65, 251-258. - https://doi.org/10.1016/j.yrtph.2012.12.002

    -

    Data retrieved from the Supplemental Information of this source.

    -

    References

    - -

    Jay M. Harrison, Derek Culp, George G. Harrigan. 2013. - Bayesian MCMC analyses for regulatory assessments of safety in food composition - Proceedings of the 24th Conference on Applied Statistics in - Agriculture (2012).

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(harrison.priors)
-dat <- harrison.priors
-
-d1 <- subset(dat, substance=="daidzein")
-
-# Stack the data to 'tall' format and calculate empirical cdf
-d1t <- with(d1, data.frame(xx = c(min, max), yy=c(1/(number+1), number/(number+1))))
-
-# Harrison 2012 Example 4: Common prior distribution
-# Harrison uses the minimum and maximum levels of daidzein from previous
-# studies as the first and last order statistics of a lognormal
-# distribution, and finds the best-fit lognormal distribution.
-
-m0 <- mean(log(d1t$xx)) # 6.37
-s0 <- sd(log(d1t$xx)) # .833
-mod <- nls(yy ~ plnorm(xx, meanlog, sdlog), data=d1t,
-           start=list(meanlog=m0, sdlog=s0))
-coef(mod) # Matches Harrison 2012
-
    #>   meanlog     sdlog 
-#> 6.4187829 0.6081558 
    ##   meanlog     sdlog
-## 6.4187829 0.6081558
-
-plot(yy~xx, data=d1t, xlim=c(0,2000), ylim=c(0,1),
-     main="harrison.priors - Common prior", xlab="daidzein level", ylab="CDF")
-
    mlog <- coef(mod)[1] # 6.4
-slog <- coef(mod)[2] # .61
-xvals <- seq(0, 2000, length=100)
-lines(xvals, plnorm(xvals, meanlog=mlog, sdlog=slog))
-
    
-
-d1a <- d1
-d1a$source <- as.character(d1a$source)
-d1a[19,'source'] <- "(All)" # Add a blank row for the densitystrip
-d1
-
    #>       source substance    min     max number
-#> 1  Hutabarat  daidzein 343.00 1400.00      4
-#> 2    Primomo  daidzein 434.00  696.00      7
-#> 3   Morrison  daidzein 696.00 1230.00     14
-#> 4   Berman-U  daidzein 198.95 1273.94     21
-#> 5   Berman-A  daidzein 361.48 1458.24     21
-#> 6   Lundry-U  daidzein 274.88 1526.23     13
-#> 7   Lundry-A  daidzein 224.03 1571.91     14
-#> 8       Devi  daidzein 215.00  353.00      7
-#> 9     Seguin  daidzein 194.00  600.00     20
-#> 10    Zhou-1  daidzein 218.20 1530.30     25
-#> 11    Zhou-2  daidzein 174.80 1235.90     16
-#> 12    Zhou-3  daidzein 293.20 1168.40     12
-#> 13    Zhou-4  daidzein 417.40 1424.30     12
-#> 14    Zhou-5  daidzein 228.10 1084.40     12
-#> 15    Zhou-6  daidzein 336.90 1837.50     12
-#> 16    Zhou-7  daidzein 128.50 1424.80     12
-#> 17    Zhou-8  daidzein 594.90 1529.10     12
-#> 18    Zhou-9  daidzein 347.60 1504.80     10
    libs(latticeExtra)
-# Plot the range for each source, a density curve (with arbitary
-# vertical scale) for the common prior distribution, and a density
-# strip by stacking the individual bands and using transparency
-segplot(factor(source) ~ min+max, d1a,
-        main="harrison.priors",xlab="daidzein level",ylab="source") +
-  xyplot(5000*dlnorm(xvals, mlog, slog)~xvals, type='l') +
-  segplot(factor(rep(1,18)) ~ min+max, d1, 4, level=d1$number,
-          col.regions="gray20", alpha=.1)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hartman.tomato.uniformity-1.png b/docs/reference/hartman.tomato.uniformity-1.png deleted file mode 100644 index 424dc8a6..00000000 Binary files a/docs/reference/hartman.tomato.uniformity-1.png and /dev/null differ diff --git a/docs/reference/hartman.tomato.uniformity.html b/docs/reference/hartman.tomato.uniformity.html deleted file mode 100644 index 8943f391..00000000 --- a/docs/reference/hartman.tomato.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trial of tomato — hartman.tomato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of tomato

    - - -
    - -
    -

    Uniformity trial of tomato in Indiana

    -
    - - 
    data("hartman.tomato.uniformity")
    - - -

    Format

    - -

    A data frame with 384 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds per plot

    - -
    - -

    Details

    - -

    Grown in Indiana in 1941. - Plants were spaced 3 feet apart in rows 6 feet apart, 330 feet - long. Each row was divided into 3 sections of 34 plants sparated by - strips 12 feet long to provide roadways for vehicles.

    -

    Each row was divided into 4-plant plots, with 8 plots in each section of - row and with one plant left as a guard at the end of each section.

    -

    There were 49 plants missing out of 3072 total plants, but these have been ignored.

    -

    Note, the data given in Table 1 of Hartman are for 8-plant plots!

    -

    Field width: 3 sections (34 plants * 3 feet) + 2 roads * 12 feet = 330 feet.

    -

    Field length: 32 rows * 6 feet = 192 feet

    -

    As oriented on the page, plots were, on average, 330/12=27.5. feet wide, 6 feet tall.

    -

    Discussion notes from Hartman.

    -

    Total yield is 26001 pounds. Hartman says the yield of the field was - 10.24 tons per acre, which we can verify:

    -

    26001 lb/field * (1/384 field/plot) * (1/(24*6) plot/ft2) * (43560 ft2/acre) * (1/2000 tons/lb) = 10.24 tons/acre

    -

    The rows on the top/bottom (north/south) were intended as guard rows, - and had yields similar to the other rows, suggesting that competition - between rows did not exist. For comparing varieties, 96*6 foot plots - work well.

    -

    Source

    - -

    J. D. Hartman and E. C. Stair (1942). - Proceedings Of The American Society For Horticultural Science, 41, 315-320. - https://archive.org/details/in.ernet.dli.2015.240678

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(hartman.tomato.uniformity)
-libs(desplot)
-desplot(hartman.tomato.uniformity, yield ~ col*row, 
-        flip=TRUE, aspect=192/330, # true aspect
-        main="hartman.tomato.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/harvey.lsmeans-1.png b/docs/reference/harvey.lsmeans-1.png deleted file mode 100644 index c4f368e0..00000000 Binary files a/docs/reference/harvey.lsmeans-1.png and /dev/null differ diff --git a/docs/reference/harvey.lsmeans.html b/docs/reference/harvey.lsmeans.html deleted file mode 100644 index 2796cf45..00000000 --- a/docs/reference/harvey.lsmeans.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Average daily gain of 65 steers for 3 lines, 9 sires.

    - - -
    - -
    -

    Average daily gain of 65 steers for 3 lines, 9 sires.

    -
    - - 
    data("harvey.lsmeans")
    - - -

    Format

    - -

    A data frame with 65 observations on the following 7 variables.

    -
    line

    line of the dam

    -
    sire

    sire

    -
    damage

    age class of the dam

    -
    calf

    calf number

    -
    weanage

    calf age at weaning

    -
    weight

    calf weight at start of feeding

    -
    adg

    average daily gain

    - -
    - -

    Details

    - -

    The average daily gain 'adg' for each of 65 Hereford steers.

    -

    The calf age at weaning and initial weight at the beginning of the - test feeding is also given.

    -

    The steers were fed for the same length of time in the feed lot.

    -

    It is assumed that each calf has a unique dam and there are no twins - or repeat matings.

    -

    Harvey (1960) is one of the earliest papers presenting least squares - means (lsmeans).

    -

    Source

    - - - -

    Harvey, Walter R. (1960). - Least-squares Analysis of Data with Unequal Subclass Numbers. - Technical Report ARS No 20-8. USDA, Agricultural Research Service. - Page 101-102.

    -

    Reprinted as ARS H-4, 1975. - https://archive.org/details/leastsquaresanal04harv

    -

    References

    - -

    Also appears in the 'dmm' package as 'harv101.df' See that package - vignette for a complete analysis of the data.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(harvey.lsmeans)
-dat = harvey.lsmeans
-
-libs(lattice)
-dotplot(adg ~ sire|line,dat,
-        main="harvey.lsmeans", xlab="sire", ylab="average daily gain")
-
    
-# Model suggested by Harvey on page 103
-m0 <- lm(adg ~ 1 + line + sire + damage + line:damage + weanage +
-         weight, data=dat)
-
-# Due to contrast settings, it can be hard to compare model coefficients to Harvey,
-# but note the slopes of the continuous covariates match Harvey p. 107, where his
-# b is weanage, d is weight
-# coef(m0)
-#       weanage       weight 
-#  -0.008154879  0.001970446
-  
-# A quick attempt to reproduce table 4 of Harvey, p. 109. Not right.
-# libs(emmeans)
-# emmeans(m0,c('line','sire','damage'))
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/harville.lamb-1.png b/docs/reference/harville.lamb-1.png deleted file mode 100644 index cb5c3da8..00000000 Binary files a/docs/reference/harville.lamb-1.png and /dev/null differ diff --git a/docs/reference/harville.lamb.html b/docs/reference/harville.lamb.html deleted file mode 100644 index 3e93d20e..00000000 --- a/docs/reference/harville.lamb.html +++ /dev/null @@ -1,239 +0,0 @@ - - - - - - - - -Birth weight of lambs from different lines/sires — harville.lamb • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Birth weight of lambs from different lines/sires

    - - -
    - -
    -

    Birth weight of lambs from different lines/sires

    -
    - - 
    data("harville.lamb")
    - - -

    Format

    - -

    A data frame with 62 observations on the following 4 variables.

    -
    line

    genotype line number

    -
    sire

    sire number

    -
    damage

    dam age, class 1,2,3

    -
    weight

    lamb birth weight

    - -
    - -

    Details

    - -

    Weight at birth of 62 lambs. There were 5 distinct lines.

    -

    Some sires had multiple lambs. Each dam had one lamb.

    -

    The age of the dam is a category: 1 (1-2 years), 2 (2-3 years) or 3 (over 3 years).

    -

    Note: Jiang, gives the data in table 1.2, but there is a small error. - Jiang has a weight 9.0 for sire 31, line 3, age 3. The correct value is 9.5.

    -

    Source

    - -

    David A. Harville and Alan P. Fenech (1985). - Confidence Intervals for a Variance Ratio, or for Heritability, in an - Unbalanced Mixed Linear Model. - Biometrics, 41, 137-152. - https://doi.org/10.2307/2530650

    -

    References

    - -

    Jiming Jiang, - Linear and Generalized Linear Mixed Models and Their Applications. Table 1.2.

    -

    Andre I. Khuri, Linear Model Methodology. Table 11.5. Page 368. - https://books.google.com/books?id=UfDvCAAAQBAJ&pg=PA164

    -

    Daniel Gianola, Keith Hammond. - Advances in Statistical Methods for Genetic Improvement of - Livestock. Table 8.1, page 165.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(harville.lamb)
-dat <- harville.lamb
-dat <- transform(dat, line=factor(line), sire=factor(sire), damage=factor(damage))
-
-library(lattice)
-bwplot(weight ~ line, dat,
-       main="harville.lamb",
-       xlab="line", ylab="birth weights")
-
    
-if(0){
-  libs(lme4, lucid)
-    
-  m1 <- lmer(weight ~  -1 + line + damage + (1|sire), data=dat)
-  summary(m1)
-  vc(m1) # Khuri reports variances 0.5171, 2.9616
-  ##      grp        var1 var2   vcov  sdcor
-  ##     sire (Intercept) <NA> 0.5171 0.7191
-  ## Residual        <NA> <NA> 2.962  1.721 
-  }
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hayman.tobacco-1.png b/docs/reference/hayman.tobacco-1.png deleted file mode 100644 index 999fdfbb..00000000 Binary files a/docs/reference/hayman.tobacco-1.png and /dev/null differ diff --git a/docs/reference/hayman.tobacco-2.png b/docs/reference/hayman.tobacco-2.png deleted file mode 100644 index 75576c68..00000000 Binary files a/docs/reference/hayman.tobacco-2.png and /dev/null differ diff --git a/docs/reference/hayman.tobacco.html b/docs/reference/hayman.tobacco.html deleted file mode 100644 index b9940cdf..00000000 --- a/docs/reference/hayman.tobacco.html +++ /dev/null @@ -1,291 +0,0 @@ - -Diallel cross of Aztec tobacco — hayman.tobacco • agridat - - -
    -
    - - - -
    -
    -
    -

    Diallel cross of Aztec tobacco

    - - -
    - -
    -

    Diallel cross of Aztec tobacco in 2 reps

    -
    - - -
    -

    Format

    - - -

    year
    -

    year

    - -
    block
    -

    block factor, 2 levels

    - -
    male
    -

    male parent, 8 levels

    - -
    female
    -

    female parent

    - -
    day
    -

    mean flowering time (days)

    - - -
    -
    -

    Details

    - - -

    Data was collected in 1951 (Hayman 1954a) and 1952 (Hayman 1954b).

    -

    In each year there were 8 varieties of Aztec tobacco, Nicotiana rustica L..

    -

    Each cross/self was represented by 10 progeny, in two plots of 5 - plants each. The data are the mean flowering time per plot.

    -

    Note, the 1951 data as published in Hayman (1954a) Table 5 contain - "10 times the mean flowering time". The data here have been divided - by 10 so as to be comparable with the 1952 data.

    -

    Hayman (1954b) says "Table 2 lists...three characters from a diallel - cross of Nicotiana rustica varieties which was repeated for three years." - This seems to indicate that the varieties are the same in 1951 and - 1952. Calculating the GCA effects separately for 1951 and 1952 and - then comparing these estimates shows that they are highly correlated.

    -
    -
    -

    Source

    - - -

    B. I. Hayman (1954a). The Analysis of Variance of Diallel Tables. - Biometrics, 10, 235-244. Table 5, page 241. - https://doi.org/10.2307/3001877

    -

    Hayman, B.I. (1954b). The theory and analysis of diallel crosses. - Genetics, 39, 789-809. Table 3, page 805. - https://www.genetics.org/content/39/6/789.full.pdf

    -
    -
    -

    References

    - - -

    # For 1951 data

    -

    Mohring, Melchinger, Piepho. (2011). - REML-Based Diallel Analysis. - Crop Science, 51, 470-478.

    -

    # For 1952 data

    -

    C. Clark Cockerham and B. S. Weir. (1977). - Quadratic analyses of reciprocal crosses. - Biometrics 33, 187-203. Appendix C.

    -

    Andrea Onofri, Niccolo Terzaroli, Luigi Russi (2020). - Linear models for diallel crosses: A review with R functions. - Theoretical and Applied Genetics. - https://doi.org/10.1007/s00122-020-03716-8

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  
-  # 1951 data.  Fit the first REML model of Mohring 2011 Supplement.  
-  data(hayman.tobacco)
-  dat1 <- subset(hayman.tobacco, year==1951)
-
-  
-  # Hayman's model
-  # dat1 <- subset(hayman.tobacco, year==1951)
-  # libs(lmDiallel)
-  # m1  <- lm.diallel(day ~ male+female, Block=block, data=dat1, fct="HAYMAN2")
-  # anova(m1) # Similar to table 7 of Hayman 1954a
-  ## Response: day
-  ##                Df  Sum Sq Mean Sq F value    Pr(>F)    
-  ## Block           1    1.42    1.42  0.3416   0.56100    
-  ## Mean Dom. Dev.  1  307.97  307.97 73.8840 3.259e-12 ***
-  ## GCA             7 2777.17  396.74 95.1805 < 2.2e-16 ***
-  ## Dom. Dev.       7  341.53   48.79 11.7050 1.957e-09 ***
-  ## SCA            20  372.89   18.64  4.4729 2.560e-06 ***
-  ## RGCA            7   67.39    9.63  2.3097   0.03671 *  
-  ## RSCA           21  123.73    5.89  1.4135   0.14668    
-  ## Residuals      63  262.60                              
-
-  
-  # Griffing's model
-  # https://www.statforbiology.com/2021/stat_met_diallel_griffing/
-  # dat1 <- subset(hayman.tobacco, year==1951)
-  # libs(lmDiallel)
-  # contrasts(dat1$block) <- "contr.sum"
-  # dmod1 and dmod2 are the same model with different syntax
-  # dmod1 <- lm(day ~ block + GCA(male, female) + tSCA(male, female) +
-  #               REC(male, female) , data = dat1)
-  # dmod2 <- lm.diallel(day ~ male + female, Block=block, 
-  #                     data = dat1, fct = "GRIFFING1")
-  # anova(dmod1)
-  # anova(dmod2)
-  ## Response: day
-  ##             Df  Sum Sq Mean Sq F value    Pr(>F)    
-  ## Block        1    1.42    1.42  0.3416   0.56100    
-  ## GCA          7 2777.17  396.74 95.1805 < 2.2e-16 ***
-  ## SCA         28 1022.38   36.51  8.7599 6.656e-13 ***
-  ## Reciprocals 28  191.12    6.83  1.6375   0.05369 .  
-  ## Residuals   63  262.60                              
-
-  
-  # Make a factor 'comb' in which G1xG2 is the same cross as G2xG1
-  dat1 <- transform(dat1,
-                    comb =
-                      ifelse(as.character(male) < as.character(female),
-                             paste0(male,female), paste0(female,male)))
-  # 'dr' is the direction of the cross, 0 for self
-  dat1$dr <- 1
-  dat1 <- transform(dat1,
-                    dr = ifelse(as.character(male) < as.character(female), -1, dr))
-  dat1 <- transform(dat1,
-                    dr = ifelse(as.character(male) == as.character(female), 0, dr))
-  
-  # asreml r version 3 & 4 code for Mixed Griffing.
-  # Mohring Table 2, column 2 (after dividing by 10^2) gives variances:
-  # GCA 12.77, SCA 11.09, RSCA .65, Error 4.23.
-  # Mohring Supplement ASREML code part1 model is:
-  # y ~ mu r !r mother and(father) combination combination.dr
-  # Note that the levels of 'male' and 'female' are the same, so the
-  # and(female) term tells asreml to use the same levels (or, equivalently,
-  # fix the correlation of the male/female levels to be 1.
-  # The block effect is minimial and therefore ignored.
-  ## libs(asreml, lucid)
-  ## m1 <- asreml(day~1, data=dat1,
-  ##              random = ~ male + and(female) + comb + comb:dr)
-  ## vc(m1)
-  ##           effect component std.error z.ratio      con
-  ##    male!male.var   12.77      7.502      1.7 Positive
-  ##    comb!comb.var   11.11      3.353      3.3 Positive
-  ## comb:dr!comb.var    0.6603    0.4926     1.3 Positive
-  ##       R!variance    4.185     0.7449     5.6 Positive
-  
-  # ----------
-  
-  # 1952 data. Reproduce table 3 and figure 2 of Hayman 1954b.
-  
-  dat2 <- subset(hayman.tobacco, year==1952)
-  
-  # Does flowering date follow a gamma distn?  Maybe.
-  libs(lattice)
-  densityplot(~day, data=dat2, main="hayman.tobacco",
-              xlab="flowering date")
-
-  
-  
-  d1 <- subset(dat2, block=='B1')
-  d2 <- subset(dat2, block=='B2')
-  
-  libs(reshape2)
-  m1 <- acast(d1, male~female, value.var='day')
-  m2 <- acast(d2, male~female, value.var='day')
-  
-  mn1 <- (m1+t(m1))/2
-  mn2 <- (m2+t(m2))/2
-  
-  # Variance and covariance of 'rth' offspring
-  vr1 <- apply(mn1, 1, var)
-  vr2 <- apply(mn2, 1, var)
-  wr1 <- apply(mn1, 1, cov, diag(mn1))
-  wr2 <- apply(mn2, 1, cov, diag(mn2))
-  # Remove row names to prevent a mild warning
-  rownames(mn1) <- rownames(mn2) <- NULL
-  summ <- data.frame(rbind(mn1,mn2))
-  summ$block <- rep(c('B1','B2'), each=8)
-  summ$vr <- c(vr1,vr2)
-  summ$wr <- c(wr1,wr2)
-  summ$male <- rep(1:8,2) # Vr and Wr match Hayman table 3
-  
-  with(summ, plot(wr~vr, type='n', main="hayman.tobacco"))
-  with(summ, text(vr, wr, male)) # Match Hayman figure 2
-  abline(0,1,col="gray")
-
-  # Hayman notes that 1 and 3 do not lie along the line,
-  # so modifies them and re-analyzes.
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/hazell.vegetables-1.png b/docs/reference/hazell.vegetables-1.png deleted file mode 100644 index 47286c4d..00000000 Binary files a/docs/reference/hazell.vegetables-1.png and /dev/null differ diff --git a/docs/reference/hazell.vegetables-2.png b/docs/reference/hazell.vegetables-2.png deleted file mode 100644 index ff2afa8e..00000000 Binary files a/docs/reference/hazell.vegetables-2.png and /dev/null differ diff --git a/docs/reference/hazell.vegetables.html b/docs/reference/hazell.vegetables.html deleted file mode 100644 index 3c291314..00000000 --- a/docs/reference/hazell.vegetables.html +++ /dev/null @@ -1,300 +0,0 @@ - - - - - - - - -Gross profit for 4 vegetable crops in 6 years — hazell.vegetables • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Gross profit for 4 vegetable crops in 6 years

    - - -
    - -
    -

    Gross profit for 4 vegetable crops in 6 years

    -
    - - 
    data("hazell.vegetables")
    - - -

    Format

    - -

    A data frame with 6 observations on the following 5 variables.

    -
    year

    year factor, 6 levels

    -
    carrot

    Carrot profit, dollars/acre

    -
    celery

    Celery profit, dollars/acre

    -
    cucumber

    Cucumber profit, dollars/acre

    -
    pepper

    Pepper profit, dollars/acre

    - -
    - -

    Details

    - -

    The values in the table are gross profits (loss) in dollars per acre. - The criteria in the example below are (1) total acres < 200, (2) total - labor < 10000, (3) crop rotation.

    -

    The example shows how to use linear programming to maximize expected profit.

    -

    Source

    - -

    P.B.R. Hazell, (1971). - A linear alternative to quadratic and semivariance programming for farm planning - under uncertainty. - Am. J. Agric. Econ., 53, 53-62. - https://doi.org/10.2307/3180297

    -

    References

    - -

    Carlos Romero, Tahir Rehman. (2003). - Multiple Criteria Analysis for Agricultural Decisions. Elsevier.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(hazell.vegetables)
-  dat <- hazell.vegetables
-  
-  libs(lattice)
-  xyplot(carrot+celery+cucumber+pepper ~ year,dat,
-         ylab="yearly profit by crop",
-         type='b', auto.key=list(columns=4),
-         panel.hline=0)
-
    
-  # optimal strategy for planting crops (calculated below)
-  dat2 <- apply(dat[,-1], 1, function(x) x*c(0, 27.5, 100, 72.5))/1000
-  colnames(dat2) <- rownames(dat)
-  barplot(dat2, legend.text=c("     0 carrot", "27.5 celery", " 100 cucumber", "72.5 pepper"),
-          xlim=c(0,7), ylim=c(-5,120),
-          col=c('orange','green','forestgreen','red'),
-          xlab="year", ylab="Gross profit, $1000",
-          main="hazell.vegetables - retrospective profit from optimal strategy",
-          args.legend=list(title="acres, crop"))
-
    
-  libs(linprog)
-
    #> Loading required package: lpSolve
      # colMeans(dat[ , -1])
-  # 252.8333 442.6667 283.8333 515.8333
- 
-  # cvec = avg across-years profit per acre for each crop
-  cvec <- c(253, 443, 284, 516)
-  
-  # Maximize c'x for Ax=b
-  A <- rbind(c(1,1,1,1), c(25,36,27,87), c(-1,1,-1,1))
-  colnames(A) <- names(cvec) <- c("carrot","celery","cucumber","pepper")
-  rownames(A) <- c('land','labor','rotation')
-
-  # bvec criteria = (1) total acres < 200, (2) total labor < 10000,
-  # (3) crop rotation.
-
-  bvec <- c(200,10000,0)
-  const.dir <- c("<=","<=","<=")
-
-  m1 <- solveLP(cvec, bvec, A, maximum=TRUE, const.dir=const.dir, lpSolve=TRUE)
-  # m1$solution # optimal number of acres for each crop
-  #   carrot    celery  cucumber    pepper
-  #  0.00000  27.45098 100.00000  72.54902
-  
-  # Average income for this plan
-  ## sum(cvec * m1$solution)
-  ## [1] 77996.08
-
-  # Year-to-year income for this plan
-  ## as.matrix(dat[,-1]) 
-  ##           [,1]
-  ## [1,]  80492.16
-  ## [2,]  80431.37
-  ## [3,]  81884.31
-  ## [4,] 106868.63
-  ## [5,]  37558.82
-  ## [6,]  80513.73
-
-  # optimum allocation that minimizes year-to-year income variability.
-  # brute-force search
-
-  # For generality, assume we have unequal probabilities for each year.
-  probs <- c(.15, .20, .20, .15, .15, .15)
-  # Randomly allocate crops to 200 acres, 100,000 times
-  #set.seed(1)
-  mat <- matrix(runif(4*100000), ncol=4)
-  mat <- 200*sweep(mat, 1, rowSums(mat), "/")
-  # each row is one strategy, showing profit for each of the six years
-  # profit <- mat 
-  profit <- tcrossprod(mat, as.matrix(dat[,-1])) # Each row is profit, columns are years
-  # calculate weighted variance using year probabilities
-  wtvar <- apply(profit, 1, function(x) cov.wt(as.data.frame(x), wt=probs)$cov)
-  # five best planting allocations that minimizes the weighted variance
-  ix <- order(wtvar)[1:5]
-  mat[ix,]
-
    #>          [,1]     [,2]     [,3]     [,4]
-#> [1,] 69.78990 27.78428 87.63774 14.78808
-#> [2,] 72.46053 28.76455 82.99749 15.77743
-#> [3,] 68.78445 28.07023 86.57654 16.56877
-#> [4,] 73.12205 29.29729 82.24620 15.33445
-#> [5,] 70.28423 29.73192 86.69204 13.29181
      ## carrot celery cucumber pepper
-  ##          [,1]     [,2]     [,3]     [,4]
-  ## [1,] 71.26439 28.09259 85.04644 15.59657
-  ## [2,] 72.04428 27.53299 84.29760 16.12512
-  ## [3,] 72.16332 27.35147 84.16669 16.31853
-  ## [4,] 72.14622 29.24590 84.12452 14.48335
-  ## [5,] 68.95226 27.39246 88.61828 15.03700
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/heady.fertilizer-1.png b/docs/reference/heady.fertilizer-1.png deleted file mode 100644 index 7bbe6bea..00000000 Binary files a/docs/reference/heady.fertilizer-1.png and /dev/null differ diff --git a/docs/reference/heady.fertilizer.html b/docs/reference/heady.fertilizer.html deleted file mode 100644 index 622cefe2..00000000 --- a/docs/reference/heady.fertilizer.html +++ /dev/null @@ -1,365 +0,0 @@ - - - - - - - - -Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Yield of corn, alfalfa, clover with two fertilizers

    - - -
    - -
    -

    Yield of corn, alfalfa, clover with two fertilizers

    -
    - - 
    data("heady.fertilizer")
    - - -

    Format

    - -

    A data frame with 81 observations on the following 3 variables.

    -
    crop

    crop

    -
    rep

    replicate (not block)

    -
    P

    phosphorous, pounds/acre

    -
    K

    potassium, pounds/acre

    -
    N

    nitrogen, pounds/acre

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Heady et al. fit two-variable semi-polynomial response surfaces for - each crop.

    -

    Clover and alfalfa yields are in tons/acre. - The clover and alfalfa experiments were grown in 1952.

    -

    Corn yields are given as bu/acre. - The corn experiments were grown in 1952 and 1953. The same test plots - were used in 1953 and in 1952, but no fertilizer was - applied in 1953--any response in yield is due to residual fertilizer - from 1952.

    -

    All experiments used an incomplete factorial design. - Not all treatment combinations were present.

    -

    Source

    - -

    Earl O. Heady, John T. Pesek, William G. Brown. (1955). - Crop Response Surfaces and Economic Optima in Fertilizer Use. - Agricultural Experiment Station, Iowa State College. Research bulletin - 424. Pages 330-332. - https://lib.dr.iastate.edu/cgi/viewcontent.cgi?filename=12&article=1032&context=ag_researchbulletins&type=additional

    -

    References

    - -

    Pesek, John and Heady, Earl O. 1956. - A two nutrient-response function with determination of economic optima - for the rate and grade of fertilizer for alfalfa. - Soil Science Society of America Journal, 20, 240-246. - https://doi.org/10.2136/sssaj1956.03615995002000020025x

    - -

    Examples

    - 
    
-library(agridat)
-
-data(heady.fertilizer)
-dat <- heady.fertilizer
-
-libs(lattice)
-xyplot(yield ~ P|crop, data=dat, scales=list(relation="free"),
-       groups=factor(paste(dat$N,dat$K)), auto.key=list(columns=5),
-       main="heady.fertilizer", xlab="Phosphorous")
-
    
-# Corn. Matches Heady, p. 292
-d1 <- subset(dat, crop=="corn")
-m1 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d1)
-summary(m1)
-
    #> 
-#> Call:
-#> lm(formula = yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N * P), 
-#>     data = d1)
-#> 
-#> Residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -39.486  -8.091   1.188   9.740  30.194 
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) -5.69442    6.62729  -0.859    0.392    
-#> N           -0.31622    0.03996  -7.913 2.35e-12 ***
-#> P           -0.41749    0.03996 -10.447  < 2e-16 ***
-#> sqrt(N)      6.35320    0.86815   7.318 4.70e-11 ***
-#> sqrt(P)      8.51766    0.86815   9.811  < 2e-16 ***
-#> sqrt(N * P)  0.34096    0.03854   8.847 1.92e-14 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 13.57 on 108 degrees of freedom
-#>   (48 observations deleted due to missingness)
-#> Multiple R-squared:  0.9181,	Adjusted R-squared:  0.9143 
-#> F-statistic: 242.2 on 5 and 108 DF,  p-value: < 2.2e-16
-#> 
    
-
-# Alfalfa. Matches Heady, p. 292.  Also Pesek equation 3, p. 241
-d2 <- subset(dat, crop=="alfalfa")
-m2 <- lm(yield ~ K + P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d2)
-summary(m2)
-
    #> 
-#> Call:
-#> lm(formula = yield ~ K + P + sqrt(K) + sqrt(P) + sqrt(K * P), 
-#>     data = d2)
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -0.73355 -0.16627  0.01388  0.15149  0.61572 
-#> 
-#> Coefficients:
-#>               Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept)  1.8735521  0.1222501  15.326  < 2e-16 ***
-#> K           -0.0013943  0.0007371  -1.891 0.061237 .  
-#> P           -0.0050195  0.0007371  -6.810 5.74e-10 ***
-#> sqrt(K)      0.0617458  0.0160142   3.856 0.000196 ***
-#> sqrt(P)      0.1735383  0.0160142  10.837  < 2e-16 ***
-#> sqrt(K * P) -0.0014402  0.0007109  -2.026 0.045237 *  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 0.2502 on 108 degrees of freedom
-#>   (48 observations deleted due to missingness)
-#> Multiple R-squared:  0.7732,	Adjusted R-squared:  0.7627 
-#> F-statistic: 73.64 on 5 and 108 DF,  p-value: < 2.2e-16
-#> 
    ## Coefficients:
-##               Estimate Std. Error t value Pr(>|t|)
-## (Intercept)  1.8735521  0.1222501  15.326  < 2e-16 ***
-## K           -0.0013943  0.0007371  -1.891 0.061237 .
-## P           -0.0050195  0.0007371  -6.810 5.74e-10 ***
-## sqrt(K)      0.0617458  0.0160142   3.856 0.000196 ***
-## sqrt(P)      0.1735383  0.0160142  10.837  < 2e-16 ***
-## sqrt(K * P) -0.0014402  0.0007109  -2.026 0.045237 *
-
-
-# Clover. Matches Heady, p. 292.
-d3 <- subset(dat, crop=="clover")
-m3 <- lm(yield ~ P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d3)
-summary(m3)
-
    #> 
-#> Call:
-#> lm(formula = yield ~ P + sqrt(K) + sqrt(P) + sqrt(K * P), data = d3)
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -0.57245 -0.16915 -0.02067  0.14439  0.69123 
-#> 
-#> Coefficients:
-#>               Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept)  2.4698101  0.1133703  21.785  < 2e-16 ***
-#> P           -0.0039131  0.0007097  -5.514 2.38e-07 ***
-#> sqrt(K)      0.0266867  0.0086756   3.076  0.00265 ** 
-#> sqrt(P)      0.1272024  0.0154128   8.253 3.92e-13 ***
-#> sqrt(K * P) -0.0009570  0.0006859  -1.395  0.16578    
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 0.2415 on 109 degrees of freedom
-#>   (48 observations deleted due to missingness)
-#> Multiple R-squared:  0.644,	Adjusted R-squared:  0.631 
-#> F-statistic:  49.3 on 4 and 109 DF,  p-value: < 2.2e-16
-#> 
    
-# Corn with residual fertilizer. Matches Heady eq 56, p. 322.
-d4 <- subset(dat, crop=="corn2")
-m4 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d4)
-summary(m4)
-
    #> 
-#> Call:
-#> lm(formula = yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N * P), 
-#>     data = d4)
-#> 
-#> Residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -27.349  -6.885  -1.070   6.857  38.863 
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) 18.31681    5.09183   3.597 0.000487 ***
-#> N            0.09482    0.03070   3.088 0.002558 ** 
-#> P           -0.04405    0.03070  -1.435 0.154262    
-#> sqrt(N)     -2.10479    0.66701  -3.156 0.002076 ** 
-#> sqrt(P)      0.23531    0.66701   0.353 0.724935    
-#> sqrt(N * P)  0.21933    0.02961   7.407 3.01e-11 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 10.42 on 108 degrees of freedom
-#>   (48 observations deleted due to missingness)
-#> Multiple R-squared:  0.7709,	Adjusted R-squared:  0.7603 
-#> F-statistic:  72.7 on 5 and 108 DF,  p-value: < 2.2e-16
-#> 
    
-# \dontrun{
-  libs(rgl)
-  with(d1, plot3d(N,P,yield))
-  with(d2, plot3d(K,P,yield))
-  with(d3, plot3d(K,P,yield))
-  with(d4, plot3d(N,P,yield)) # Mostly linear in both N and P
-  rgl.close()
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/heath.raddish.uniformity-1.png b/docs/reference/heath.raddish.uniformity-1.png deleted file mode 100644 index bc8308fc..00000000 Binary files a/docs/reference/heath.raddish.uniformity-1.png and /dev/null differ diff --git a/docs/reference/heath.raddish.uniformity.html b/docs/reference/heath.raddish.uniformity.html deleted file mode 100644 index caae865a..00000000 --- a/docs/reference/heath.raddish.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trial of raddish — heath.raddish.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of raddish

    - - -
    - -
    -

    Uniformity trial of raddish in four containers.

    -
    - - 
    data("heath.raddish.uniformity")
    - - -

    Format

    - -

    A data frame with 400 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    block

    block

    -
    yield

    weight per plant

    - -
    - -

    Details

    - -

    Weight of 399 radish plants grown at 1in x 1in spacing in four plastic basins. -Seed wetted 1968-02-15, planted 1968-02-17, harvested 1968-03-26.

    -

    Heath said, Most of the large plants were round the edges...one important source of variation might have been competition for light.

    -

    Source

    - -

    O.V.S. Heath (1970). - Investigation by Experiment. Table 1. - https://archive.org/details/investigationbye0000heat

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  require(agridat)
-  data(heath.raddish.uniformity)
-  dat <- heath.raddish.uniformity
-  libs(desplot, dplyr)
-
    #> 
-#> Attaching package: 'dplyr'
    #> The following object is masked from 'package:nlme':
-#> 
-#>     collapse
    #> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
    #> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
      desplot(dat, yield ~ col*row|block,
-          aspect=1,
-          main="heath.raddish.uniformity")
-
    
-  # Indicator for border/interior plants
-  dat <- mutate(dat,
-                inner = row > 1 & row < 10 & col >  1 & col < 10)
-  # Heath has 5.80 and 9.63 (we assume this is a typo of 9.36)
-  dat <- group_by(dat, inner)
-  summarize(dat, mean=mean(yield, na.rm=TRUE))
-
    #> `summarise()` ungrouping output (override with `.groups` argument)
    #> # A tibble: 2 x 2
-#>   inner  mean
-#>   <lgl> <dbl>
-#> 1 FALSE  9.36
-#> 2 TRUE   5.80
      # Interior plots are significantly lower yielding
-  anova(aov(yield ~ block + inner, dat))
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>            Df Sum Sq Mean Sq F value    Pr(>F)    
-#> block       3   30.4   10.15  0.5055    0.6787    
-#> inner       1 1166.3 1166.30 58.0944 1.883e-13 ***
-#> Residuals 394 7909.9   20.08                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      # lattice::bwplot(yield ~ inner, dat, horiz=0)
-  # similar to Heath fig 2-2
-  # lattice::histogram( ~ yield|inner, dat, layout=c(1,2), n=20)
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/henderson.milkfat-1.png b/docs/reference/henderson.milkfat-1.png deleted file mode 100644 index a95af0ab..00000000 Binary files a/docs/reference/henderson.milkfat-1.png and /dev/null differ diff --git a/docs/reference/henderson.milkfat.html b/docs/reference/henderson.milkfat.html deleted file mode 100644 index 1c1397af..00000000 --- a/docs/reference/henderson.milkfat.html +++ /dev/null @@ -1,254 +0,0 @@ - - - - - - - - -Milk fat yields for a single cow — henderson.milkfat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Milk fat yields for a single cow

    - - -
    - -
    -

    Average daily fat yields (kg/day) from milk from a single cow for each of 35 weeks.

    -
    - - - -

    Format

    - -

    A data frame with 35 observations on the following 2 variables.

    -
    week

    week, numeric

    -
    yield

    yield, kg/day

    - -
    - -

    Source

    - -

    Charles McCulloch. Workshop on Generalized Linear Mixed Models.

    -

    Used with permission of Charles McCulloch and Harold Henderson.

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(henderson.milkfat)
-dat <- henderson.milkfat
-
-plot(yield~week, data=dat, cex = 0.8, ylim=c(0,.9),
-     main="henderson.milkfat", xlab = "Week",
-     ylab = "Fat yield (kg/day)")
-
    
-# Yield ~ a * t^b * exp(g*t) # where t is time
-m1 <- nls(yield ~ alpha * week^beta * exp(gamma * week),
-          data=dat,
-          start=list(alpha=.1, beta=.1, gamma=.1))
-
-# Or, take logs and fit a linear model
-# log(yield) ~ log(alpha) + beta*log(t)  + gamma*t
-m2 <- lm(log(yield) ~ 1 + log(week) + week, dat)
-
-# Or, use glm and a link to do the transform
-m3 <- glm(yield ~ 1 + log(week) + week, quasi(link = "log"), dat)
-
-# Note: m2 has E[log(y)] = log(alpha) + beta*log(t)  + gamma*t
-#  and m3 has  log(E[y]) = log(alpha) + beta*log(t)  + gamma*t
-
-# Generalized additive models
-libs("mgcv")
-
    #> This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
    #> 
-#> Attaching package: 'mgcv'
    #> The following object is masked from 'package:spaMM':
-#> 
-#>     negbin
    m4 <- gam(log(yield) ~ s(week), gaussian, dat)
-m5 <- gam(yield ~ s(week), quasi(link = "log"), dat)
-
-# Model predictions
-pdat <- data.frame(week = seq(1, 35, by = 0.1))
-pdat <- transform(pdat, p1 = predict(m1, pdat),
-                  p2 = exp(predict(m2, pdat)), # back transform
-                  p3 = predict(m3, pdat, type="resp"), # response scale
-                  p4 = exp(predict(m4, pdat)),
-                  p5 = predict(m5, pdat, type="response"))
-
-# Compare fits
-with(pdat, {
-  lines(week, p1)
-  lines(week, p2, col = "red", lty="dotted")
-  lines(week, p3, col = "red", lty="dashed")
-  lines(week, p4, col = "blue", lty = "dashed")
-  lines(week, p5, col = "blue")
-})
-
    
-legend("topright",
-  c("obs", "lm, log-transformed", "glm, log-link",
-    "gam, log-transformed", "gam, log-link"),
-  lty = c("solid", "dotted", "dashed", "dashed", "solid"),
-  col = c("black", "red", "red", "blue", "blue"),
-  cex = 0.8, bty = "n")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hernandez.nitrogen-1.png b/docs/reference/hernandez.nitrogen-1.png deleted file mode 100644 index ee1621d7..00000000 Binary files a/docs/reference/hernandez.nitrogen-1.png and /dev/null differ diff --git a/docs/reference/hernandez.nitrogen.html b/docs/reference/hernandez.nitrogen.html deleted file mode 100644 index d860aca6..00000000 --- a/docs/reference/hernandez.nitrogen.html +++ /dev/null @@ -1,331 +0,0 @@ - - - - - - - - -Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn with nitrogen fertilizer at 5 sites.

    - - -
    - -
    -

    Corn response to nitrogen fertilizer at 5 sites.

    -
    - - - -

    Format

    - -

    A data frame with 136 observations on the following 5 variables.

    -
    site

    site factor, 5 levels

    -
    loc

    location name

    -
    rep

    rep, 4 levels

    -
    nitro

    nitrogen, kg/ha

    -
    yield

    yield, Mg/ha

    - -
    - -

    Details

    - -

    Experiment was conducted in 2006 at 5 sites in Minnesota.

    -

    Source

    - -

    Hernandez, J.A. and Mulla, D.J. 2008. - Estimating uncertainty of economically optimum fertilizer rates, - Agronomy Journal, 100, 1221-1229. - https://doi.org/10.2134/agronj2007.0273

    -

    Electronic data kindly supplied by Jose Hernandez.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(hernandez.nitrogen)
-  dat <- hernandez.nitrogen
-  cprice <- 118.1 # $118.1/Mg or $3/bu
-  nprice <- 0.6615 # $0.66/kg N or $0.30/lb N
-  
-  # Hernandez optimized yield with a constraint on the ratio of the prices.
-  # Simpler to just calculate the income and optimize that.
-  dat <- transform(dat, inc = yield * cprice - nitro * nprice)
-  libs(lattice)
-  xyplot(inc ~ nitro|site, dat, groups=rep, auto.key=list(columns=4),
-         xlab="nitrogen", ylab="income", main="hernandez.nitrogen")
-
      
-  # Site 5 only
-  dat1 <- subset(dat, site=='S5')
-  
-  # When we optimize on income, a simple quadratic model works just fine,
-  # and matches the results of the nls model below.
-  # Note, 'poly(nitro)' gives weird coefs
-  lm1 <- lm(inc ~ 1 + nitro + I(nitro^2), data=dat1) 
-  c1 <- coef(lm1)
-  -c1[2] / (2*c1[3])
-
    #>    nitro 
-#> 191.7198 
      ##    nitro
-  ## 191.7198    # Optimum nitrogen is 192 for site 5
-  
-  
-  # Use the delta method to get a conf int
-  libs("car")
-
    #> Loading required package: carData
    #> Registered S3 methods overwritten by 'car':
-#>   method                          from
-#>   influence.merMod                lme4
-#>   cooks.distance.influence.merMod lme4
-#>   dfbeta.influence.merMod         lme4
-#>   dfbetas.influence.merMod        lme4
    #> 
-#> Attaching package: 'car'
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     recode
      del1 <- deltaMethod(lm1, "-b1/(2*b2)", parameterNames= paste("b", 0:2, sep=""))
-  # Simple Wald-type conf int for optimum
-  del1$Est +  c(-1,1) * del1$SE * qt(1-.1/2, nrow(dat1)-length(coef(lm1)))
-
    #> [1] 118.9329 264.5067
      ## 118.9329 264.5067
-  
-  
-  # Nonlinear regression
-  # Reparameterize b0 + b1*x + b2*x^2 using th2 = -b1/2b2 so that th2 is optimum
-  nls1 <- nls(inc ~ th11- (2*th2*th12)*nitro + th12*nitro^2,
-              data = dat1, start = list(th11 = 5, th2 = 150, th12 =-0.1),)
-  summary(nls1)
-
    #> 
-#> Formula: inc ~ th11 - (2 * th2 * th12) * nitro + th12 * nitro^2
-#> 
-#> Parameters:
-#>        Estimate Std. Error t value Pr(>|t|)    
-#> th11  1.009e+03  4.228e+01  23.871  < 2e-16 ***
-#> th2   1.917e+02  4.230e+01   4.532 0.000182 ***
-#> th12 -1.766e-02  6.762e-03  -2.612 0.016288 *  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 93.29 on 21 degrees of freedom
-#> 
-#> Number of iterations to convergence: 2 
-#> Achieved convergence tolerance: 1.789e-07
-#> 
      # Wald conf int
-  wald <- function(object, alpha=0.1){
-    nobs <- length(resid(object))
-    npar <- length(coef(object))
-    est <- coef(object)
-    stderr <- summary(object)$parameters[,2]
-    tval <- qt(1-alpha/2, nobs-npar)
-    ci <- cbind(est - tval * stderr, est + tval * stderr)
-    colnames(ci) <- paste(round(100*c(alpha/2, 1-alpha/2), 1), "pct", sep= "")
-    return(ci)
-  }
-  round(wald(nls1),2)
-
    #>        5pct   95pct
-#> th11 936.44 1081.93
-#> th2  118.93  264.51
-#> th12  -0.03   -0.01
      ##          5
-  ## th11 936.44 1081.93
-  ## th2  118.93  264.51   # th2 is the optimum
-  ## th12  -0.03   -0.01
-  
-  
-  # Likelihood conf int
-  libs(MASS)
-
    #> 
-#> Attaching package: 'MASS'
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     select
    #> The following object is masked from 'package:asreml':
-#> 
-#>     oats
      round(confint(nls1, "th2", level = 0.9),2)
-
    #> Waiting for profiling to be done...
    #>     5%    95% 
-#> 147.96 401.65 
      ##       5
-  ## 147.96 401.65
-  
-  
-  # Bootstrap conf int
-  libs(boot)
-
    #> 
-#> Attaching package: 'boot'
    #> The following object is masked from 'package:car':
-#> 
-#>     logit
    #> The following object is masked from 'package:lattice':
-#> 
-#>     melanoma
      dat1$fit <- fitted(nls1)
-  bootfun <- function(rs, i) { # bootstrap the residuals
-    dat1$y <- dat1$fit + rs[i]
-    coef(nls(y ~ th11- (2*th2*th12)*nitro + th12*nitro^2, dat1,
-             start = coef(nls1) ))
-  }
-  res1 <- scale(resid(nls1), scale = FALSE) # remove the mean.  Why? It is close to 0.
-  set.seed(5) # Sometime the bootstrap fails, but this seed works
-  boot1 <- boot(res1, bootfun, R = 500)
-  boot.ci(boot1, index = 2, type = c("perc"), conf = 0.9)
-
    #> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
-#> Based on 500 bootstrap replicates
-#> 
-#> CALL : 
-#> boot.ci(boot.out = boot1, conf = 0.9, type = c("perc"), index = 2)
-#> 
-#> Intervals : 
-#> Level     Percentile     
-#> 90%   (150.3, 359.7 )  
-#> Calculations and Intervals on Original Scale
      ## Level     Percentile
-  ## 90
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hessling.argentina-1.png b/docs/reference/hessling.argentina-1.png deleted file mode 100644 index 690f2a26..00000000 Binary files a/docs/reference/hessling.argentina-1.png and /dev/null differ diff --git a/docs/reference/hessling.argentina-2.png b/docs/reference/hessling.argentina-2.png deleted file mode 100644 index a438ed83..00000000 Binary files a/docs/reference/hessling.argentina-2.png and /dev/null differ diff --git a/docs/reference/hessling.argentina.html b/docs/reference/hessling.argentina.html deleted file mode 100644 index ec1e242d..00000000 --- a/docs/reference/hessling.argentina.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - - -Relation between wheat yield and weather in Argentina — hessling.argentina • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Relation between wheat yield and weather in Argentina

    - - -
    - -
    -

    Relation between wheat yield and weather in Argentina

    -
    - - - -

    Format

    - -

    A data frame with 30 observations on the following 15 variables.

    -
    yield

    average yield, kg/ha

    -
    year

    year

    -
    p05

    precipitation (mm) in May

    -
    p06

    precip in June

    -
    p07

    precip in July

    -
    p08

    precip in August

    -
    p09

    precip in Septempber

    -
    p10

    precip in October

    -
    p11

    precip in November

    -
    p12

    precip in December

    -
    t06

    june temperature deviation from normal, deg Celsius

    -
    t07

    july temp deviation

    -
    t08

    august temp deviation

    -
    t09

    september temp deviation

    -
    t10

    october temp deviation

    -
    t11

    november temp deviation

    - -
    - -

    Details

    - -

    In Argentina wheat is typically sown May to August. Harvest begins in - November or December.

    -

    Source

    - -

    N. A. Hessling, 1922. - Relations between the weather and the yield of wheat in the - Argentine republic, Monthly Weather Review, 50, 302-308. - https://doi.org/10.1175/1520-0493(1922)50<302:RBTWAT>2.0.CO;2

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(hessling.argentina)
-dat <- hessling.argentina
-
-# Fig 1 of Hessling.  Use avg Aug-Nov temp to predict yield
-dat <- transform(dat, avetmp=(t08+t09+t10+t11)/4) # Avg temp
-m0 <- lm(yield ~ avetmp, dat)
-plot(yield~year, dat, ylim=c(100,1500), type='l',
-main="hessling.argentina: observed (black) and predicted yield (blue)")
-
    lines(fitted(m0)~year, dat, col="blue")
-
    
-# A modern, PLS approach
-libs(pls)
-
    #> 
-#> Attaching package: 'pls'
    #> The following object is masked from 'package:stats':
-#> 
-#>     loadings
    yld <- dat[,"yield",drop=FALSE]
-yld <- as.matrix(sweep(yld, 2, colMeans(yld)))
-cov <- dat[,c("p06","p07","p08","p09","p10","p11", "t08","t09","t10","t11")]
-cov <- as.matrix(scale(cov))
-m2 <- plsr(yld~cov)
-
-# biplot(m2, which="x", var.axes=TRUE, main="hessling.argentina")
-
-
-libs(corrgram)
-corrgram(dat, main="hessling.argentina - correlations of yield and covariates")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hildebrand.systems-1.png b/docs/reference/hildebrand.systems-1.png deleted file mode 100644 index ccc3b503..00000000 Binary files a/docs/reference/hildebrand.systems-1.png and /dev/null differ diff --git a/docs/reference/hildebrand.systems-2.png b/docs/reference/hildebrand.systems-2.png deleted file mode 100644 index 367abf17..00000000 Binary files a/docs/reference/hildebrand.systems-2.png and /dev/null differ diff --git a/docs/reference/hildebrand.systems.html b/docs/reference/hildebrand.systems.html deleted file mode 100644 index 85f2f768..00000000 --- a/docs/reference/hildebrand.systems.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - - -Multi-environment trial of maize for four cropping systems — hildebrand.systems • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Multi-environment trial of maize for four cropping systems

    - - -
    - -
    -

    Maize yields for four cropping systems at 14 on-farm trials.

    -
    - - - -

    Format

    - -

    A data frame with 56 observations on the following 4 variables.

    -
    village

    village, 2 levels

    -
    farm

    farm, 14 levels

    -
    system

    cropping system

    -
    yield

    yield, t/ha

    - -
    - -

    Details

    - -

    Yields from 14 on-farm trials in Phalombe Project region of - south-eastern Malawi. The farms were located near two different - villages.

    -

    On each farm, four different cropping systems were tested. The - systems were: - LM = Local Maize, LMF = Local Maize with Fertilizer, - CCA = Improved Composite, CCAF = Improved Composite with Fertilizer.

    -

    Source

    - -

    P. E. Hildebrand, 1984. - Modified Stability Analysis of Farmer Managed, On-Farm Trials. - Agronomy Journal, 76, 271--274. - https://doi.org/10.2134/agronj1984.00021962007600020023x

    -

    References

    - -

    H. P. Piepho, 1998. - Methods for Comparing the Yield Stability of Cropping Systems. - Journal of Agronomy and Crop Science, 180, 193--213. - https://doi.org/10.1111/j.1439-037X.1998.tb00526.x

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(hildebrand.systems)
-  dat <- hildebrand.systems
-
-  # Piepho 1998 Fig 1
-  libs(lattice)
-  dotplot(yield ~ system, dat, groups=village, auto.key=TRUE,
-          main="hildebrand.systems", xlab="cropping system by village")
-
    
-
-  # Plot of risk of 'failure' of System 2 vs System 1
-  s11 = .30;  s22 <- .92; s12 = .34
-  mu1 = 1.35; mu2 = 2.70
-  lambda <- seq(from=0, to=5, length=20)
-  system1 <- pnorm((lambda-mu1)/sqrt(s11))
-  system2 <- pnorm((lambda-mu2)/sqrt(s22))
-
-  # A simpler view
-  plot(lambda, system1, type="l", xlim=c(0,5), ylim=c(0,1),
-       xlab="Yield level", ylab="Prob(yield < level)",
-       main="hildebrand.systems - risk of failure for each system")
-
      lines(lambda, system2, col="red")
-
      
-  # Prob of system 1 outperforming system 2. Table 8
-  pnorm((mu1-mu2)/sqrt(s11+s22-2*s12))
-
    #> [1] 0.03309629
      # .0331
-
-  # ----------
-
-  libs(asreml,lucid) # asreml4
-
-  # Environmental variance model, unstructured correlations
-  
-  dat <- dat[order(dat$system, dat$farm),]
-  m1 <- asreml(yield ~ system, data=dat,
-               resid = ~us(system):farm)
-
    #> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:45 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -10.2291           1.0     52 17:08:45    0.0
-#>  2       -6.9482           1.0     52 17:08:45    0.0
-#>  3       -3.8775           1.0     52 17:08:45    0.0
-#>  4       -2.3455           1.0     52 17:08:45    0.0
-#>  5       -2.2515           1.0     52 17:08:45    0.0
-#>  6       -2.2510           1.0     52 17:08:45    0.0
      
-  # Means, table 5
-  ## predict(m1, data=dat, classify="system")$pvals
-  ##  system pred.value std.error  est.stat
-  ##     CCA      1.164    0.2816 Estimable
-  ##    CCAF      2.657    0.3747 Estimable
-  ##      LM      1.35     0.1463 Estimable
-  ##     LMF      2.7      0.2561 Estimable
-  
-  # Variances, table 5
-  # vc(m1)[c(2,4,7,11),]
-  ##              effect component std.error z.ratio constr
-  ##    R!system.CCA:CCA    1.11      0.4354     2.5    pos
-  ##  R!system.CCAF:CCAF    1.966     0.771      2.5    pos
-  ##      R!system.LM:LM    0.2996    0.1175     2.5    pos
-  ##    R!system.LMF:LMF    0.9185    0.3603     2.5    pos
-  
-  # Stability variance model
-  m2 <- asreml(yield ~ system, data=dat,
-               random = ~ farm,
-               resid = ~ dsum( ~ units|system))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:46 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -22.5014           1.0     52 17:08:46    0.0
-#>  2      -18.0904           1.0     52 17:08:46    0.0
-#>  3      -15.9713           1.0     52 17:08:46    0.0 (1 restrained)
-#>  4      -15.2537           1.0     52 17:08:46    0.0 (1 restrained)
-#>  5      -15.1232           1.0     52 17:08:46    0.0 (1 restrained)
-#>  6      -15.1214           1.0     52 17:08:46    0.0 (1 restrained)
-#>  7      -15.1214           1.0     52 17:08:46    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      m2 <- update(m2)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:46 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -15.1214           1.0     52 17:08:46    0.0
-#>  2      -15.1214           1.0     52 17:08:46    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      # predict(m2, data=dat, classify="system")$pvals
-  
-  ## # Variances, table 6
-  # vc(m2)
-  ##        effect component std.error z.ratio bound 
-  ##          farm 0.2998      0.1187   2.5        P 0  
-  ##  system_CCA!R 0.4133      0.1699   2.4        P 0  
-  ## system_CCAF!R 1.265       0.5152   2.5        P 0  
-  ##   system_LM!R 0.0003805   0.05538  0.0069     P 1.5
-  ##  system_LMF!R 0.5294      0.2295   2.3        P 0  
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/holland.arthropods-1.png b/docs/reference/holland.arthropods-1.png deleted file mode 100644 index 581757bf..00000000 Binary files a/docs/reference/holland.arthropods-1.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-2.png b/docs/reference/holland.arthropods-2.png deleted file mode 100644 index 5494d6b8..00000000 Binary files a/docs/reference/holland.arthropods-2.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-3.png b/docs/reference/holland.arthropods-3.png deleted file mode 100644 index 904769eb..00000000 Binary files a/docs/reference/holland.arthropods-3.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-4.png b/docs/reference/holland.arthropods-4.png deleted file mode 100644 index d3a27dda..00000000 Binary files a/docs/reference/holland.arthropods-4.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-5.png b/docs/reference/holland.arthropods-5.png deleted file mode 100644 index 870d6e8f..00000000 Binary files a/docs/reference/holland.arthropods-5.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-6.png b/docs/reference/holland.arthropods-6.png deleted file mode 100644 index 16e0e0ca..00000000 Binary files a/docs/reference/holland.arthropods-6.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-7.png b/docs/reference/holland.arthropods-7.png deleted file mode 100644 index b3e7f7f9..00000000 Binary files a/docs/reference/holland.arthropods-7.png and /dev/null differ diff --git a/docs/reference/holland.arthropods-8.png b/docs/reference/holland.arthropods-8.png deleted file mode 100644 index b3e7f7f9..00000000 Binary files a/docs/reference/holland.arthropods-8.png and /dev/null differ diff --git a/docs/reference/holland.arthropods.html b/docs/reference/holland.arthropods.html deleted file mode 100644 index efd8f56f..00000000 --- a/docs/reference/holland.arthropods.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Counts of arthropods in a grid-sampled wheat field — holland.arthropods • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Counts of arthropods in a grid-sampled wheat field

    - - -
    - -
    -

    Counts of arthropods in a grid-sampled wheat field

    -
    - - 
    data("holland.arthropods")
    - - -

    Format

    - -

    A data frame with 63 observations on the following 8 variables.

    -
    row

    row

    -
    col

    column

    -
    n.brevicollis

    species counts

    -
    linyphiidae

    species counts

    -
    collembola

    species counts

    -
    carabidae

    species counts

    -
    lycosidae

    species counts

    -
    weedcover

    percent weed cover

    - -
    - -

    Details

    - -

    Arthropods were sampled at 30m x 30m grid in a wheat field near - Wimborne, Dorest, UK on 6 dates in Jun/Jul 1996. Arthropod counts were - aggregated across the 6 dates.

    -

    Holland et al. used SADIE (Spatial Analysis by Distance Indices) to - look for spatial patterns. Significant patterns were found for - N. brevicollis, Carabidae, Lycosidae. The Lycosidae counts were also - significantly associated with weed cover.

    -

    Used with permission of John Holland.

    -

    Source

    - -

    Holland J. M., Perry J. N., Winder, L. (1999). - The within-field spatial and temporal distribution of arthropods - within winter wheat. - Bulletin of Entomological Research, 89: 499-513. Figure 3 (large - grid in 1996). - https://doi.org/10.1017/S0007485399000656

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(holland.arthropods)
-dat <- holland.arthropods
-
-
-  # use log count to make it possible to have same scale for insects
-  libs(reshape2, lattice)
-  grays <- colorRampPalette(c("#d9d9d9","#252525"))
-  dat2 <- melt(dat, id.var=c('row','col'))
-  contourplot(log(value) ~ col*row|variable, dat2,
-              col.regions=grays(7), region=TRUE,
-              main="holland.arthropods - log counts in winter wheat")
-
    
-
-if(0){
-  # individual species
-  libs(lattice)
-  grays <- colorRampPalette(c("#d9d9d9","#252525"))
-  contourplot(linyphiidae ~ col*row, dat, at=c(0,40,80,120,160,200), region=TRUE,
-              col.regions=grays(5),
-              main="holland.arthropods - linyphiidae counts in winter wheat")
-  contourplot(n.brevicollis ~ col*row, dat, region=TRUE)
-  contourplot(linyphiidae~ col*row, dat, region=TRUE)
-  contourplot(collembola ~ col*row, dat, region=TRUE)
-  contourplot(carabidae ~ col*row, dat, region=TRUE)
-  contourplot(lycosidae ~ col*row, dat, region=TRUE)
-  contourplot(weedcover ~ col*row, dat, region=TRUE)
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/holshouser.splitstrip-1.png b/docs/reference/holshouser.splitstrip-1.png deleted file mode 100644 index c46fcd46..00000000 Binary files a/docs/reference/holshouser.splitstrip-1.png and /dev/null differ diff --git a/docs/reference/holshouser.splitstrip-2.png b/docs/reference/holshouser.splitstrip-2.png deleted file mode 100644 index 64a25492..00000000 Binary files a/docs/reference/holshouser.splitstrip-2.png and /dev/null differ diff --git a/docs/reference/holshouser.splitstrip-3.png b/docs/reference/holshouser.splitstrip-3.png deleted file mode 100644 index 53281af4..00000000 Binary files a/docs/reference/holshouser.splitstrip-3.png and /dev/null differ diff --git a/docs/reference/holshouser.splitstrip.html b/docs/reference/holshouser.splitstrip.html deleted file mode 100644 index 97bd8491..00000000 --- a/docs/reference/holshouser.splitstrip.html +++ /dev/null @@ -1,277 +0,0 @@ - - - - - - - - -Split-strip-plot of soybeans — holshouser.splitstrip • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Split-strip-plot of soybeans

    - - -
    - -
    -

    Split-strip-plot of soybeans

    -
    - - - -

    Format

    - -

    A data frame with 160 observations on the following 8 variables.

    -
    block

    block factor, 4 levels

    -
    plot

    plot number

    -
    cultivar

    cultivar factor, 4 levels

    -
    spacing

    row spacing

    -
    pop

    population (thousand per acre)

    -
    yield

    yield

    -
    row

    row

    -
    col

    column

    - -
    - -

    Details

    - -

    Within each block, cultivars were whole plots. Withing whole plots, - spacing was applied in strips vertically, and population was applied - in strips horizontally.

    -

    Used with permission of David Holshouser at Virginia Polytechnic.

    -

    Source

    - -

    Schabenberger, Oliver and Francis J. Pierce. 2002. - Contemporary Statistical Models for the Plant and Soil Sciences - CRC Press, Boca Raton, FL. Page 493.

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(holshouser.splitstrip)
-dat <- holshouser.splitstrip
-dat$spacing <- factor(dat$spacing)
-dat$pop <- factor(dat$pop)
-
-# Experiment layout and field trends
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          out1=block, # unknown aspect
-          main="holshouser.splitstrip")
-
      desplot(dat, spacing ~ col*row,
-          out1=block, out2=cultivar, # unknown aspect
-          col=cultivar, text=pop, cex=.8, shorten='none', col.regions=c('wheat','white'),
-          main="holshouser.splitstrip experiment design")
-
    
-
-# Overall main effects and interactions
-  libs(HH)
-
    #> Loading required package: grid
    #> Loading required package: multcomp
    #> Loading required package: mvtnorm
    #> Loading required package: survival
    #> 
-#> Attaching package: 'survival'
    #> The following object is masked from 'package:boot':
-#> 
-#>     aml
    #> The following object is masked from 'package:asreml':
-#> 
-#>     rats
    #> Loading required package: TH.data
    #> 
-#> Attaching package: 'TH.data'
    #> The following object is masked from 'package:MASS':
-#> 
-#>     geyser
    #> Loading required package: gridExtra
    #> 
-#> Attaching package: 'gridExtra'
    #> The following object is masked from 'package:dplyr':
-#> 
-#>     combine
    #> 
-#> Attaching package: 'HH'
    #> The following object is masked from 'package:boot':
-#> 
-#>     logit
    #> The following objects are masked from 'package:car':
-#> 
-#>     logit, vif
      interaction2wt(yield~cultivar*spacing*pop, dat,
-                 x.between=0, y.between=0,
-                 main="holshouser.splitstrip")
-
    
-
-  ## Schabenberger's SAS model, page 497
-  ## proc mixed data=splitstripplot;
-  ##   class block cultivar pop spacing;
-  ##   model yield = cultivar spacing spacing*cultivar pop pop*cultivar
-  ##                 spacing*pop spacing*pop*cultivar / ddfm=satterth;
-  ##   random block block*cultivar block*cultivar*spacing block*cultivar*pop;
-  ## run;
-
-  
-  ## Now lme4. This design has five error terms--four are explicitly given.
-  libs(lme4)
-  libs(lucid)
-  m1 <- lmer(yield ~ cultivar * spacing * pop +
-               (1|block) + (1|block:cultivar) + (1|block:cultivar:spacing) +
-               (1|block:cultivar:pop), data=dat)
-  vc(m1) ## Variances match Schabenberger, page 498.
-
    #>                     grp        var1 var2   vcov  sdcor
-#>      block:cultivar:pop (Intercept) <NA> 2.421  1.556 
-#>  block:cultivar:spacing (Intercept) <NA> 1.244  1.116 
-#>          block:cultivar (Intercept) <NA> 0.4523 0.6725
-#>                   block (Intercept) <NA> 3.037  1.743 
-#>                Residual        <NA> <NA> 3.928  1.982 
      ##                    grp        var1 var2   vcov  sdcor
-  ##     block:cultivar:pop (Intercept) <NA> 2.421  1.556
-  ## block:cultivar:spacing (Intercept) <NA> 1.244  1.116
-  ##         block:cultivar (Intercept) <NA> 0.4523 0.6725
-  ##                  block (Intercept) <NA> 3.037  1.743
-  ##               Residual        <NA> <NA> 3.928  1.982
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/holtsmark.timothy.uniformity-1.png b/docs/reference/holtsmark.timothy.uniformity-1.png deleted file mode 100644 index 7c4638a7..00000000 Binary files a/docs/reference/holtsmark.timothy.uniformity-1.png and /dev/null differ diff --git a/docs/reference/holtsmark.timothy.uniformity.html b/docs/reference/holtsmark.timothy.uniformity.html deleted file mode 100644 index 15913192..00000000 --- a/docs/reference/holtsmark.timothy.uniformity.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - - -Uniformity trial of timothy — holtsmark.timothy.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of timothy

    - - -
    - -
    -

    Uniformity trial of timothy hay circa 1905

    -
    - - 
    data("holtsmark.timothy.uniformity")
    - - -

    Format

    - -

    A data frame with 240 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per plot, kg

    - -
    - -

    Details

    - - - -

    Field width: 40 plots * 5 m = 200 m.

    -

    Field length: 6 plots * 5 m = 30 m

    -

    Holtsmark & Larsen used this trial to compare standard deviations of - different sized plots (combined from smaller plots).

    -

    Source

    - -

    Holtsmark, G and Larsen, BR (1905). - Om Muligheder for at indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. - Tidsskrift for Landbrugets Planteavl. 12, 330-351. (In Danish) - https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 - https://dca.au.dk/publikationer/historiske/planteavl/

    -

    Uber die Fehler, welche bei Feldversuchen, durch die Ungleichartigkeit des Bodens bedingt werden. - Die Landwirtschaftlichen Versuchs-Stationen, 65, 1--22. (In German) - https://books.google.com/books?id=eXA2AQAAMAAJ&pg=PA1

    -

    References

    - -

    Theodor Roemer (1920). Der Feldversuch.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(holtsmark.timothy.uniformity)
-dat <- holtsmark.timothy.uniformity
-
-# Define diagonal 'check' plots like Holtsmark does
-dat <- transform(dat,
-                 check = ifelse(floor((row+col)/3)==(row+col)/3, "C", ""))
-                 
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, text=check, show.key=FALSE,
-        aspect=30/200, # true aspect
-        main="holtsmark.timothy.uniformity")
-
    
-# sd(dat$yield) # 2.92 matches Holtsmark p. 348
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/huehn.wheat-1.png b/docs/reference/huehn.wheat-1.png deleted file mode 100644 index fab28377..00000000 Binary files a/docs/reference/huehn.wheat-1.png and /dev/null differ diff --git a/docs/reference/huehn.wheat.html b/docs/reference/huehn.wheat.html deleted file mode 100644 index 5fc4764b..00000000 --- a/docs/reference/huehn.wheat.html +++ /dev/null @@ -1,277 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat to illustrate stability statistics

    - - -
    - -
    -

    Multi-environment trial to illustrate stability statistics

    -
    - - 
    data("huehn.wheat")
    - - -

    Format

    - -

    A data frame with 200 observations on the following 3 variables.

    -
    gen

    genotype

    -
    env

    environment

    -
    yield

    yield dt/ha

    - -
    - -

    Details

    - -

    Yields for a winter-wheat trial of 20 genotypes at 10 environments.

    -

    Note: Huehn 1979 does not use genotype-centered data when calculating - stability statistics.

    -

    Source

    - -

    Manfred Huehn (1979). - Beitrage zur Erfassung der phanotypischen Stabilitat I. - Vorschlag einiger auf Ranginformationen beruhenden Stabilitatsparameter. - EDV in Medizin und Biologie, 10 (4), 112-117. Table 1. - https://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979

    -

    References

    - -

    Nassar, R and Huehn, M. (1987). - Studies on estimation of phenotypic stability: Tests of significance - for nonparametric measures of phenotypic stability. - Biometrics, 43, 45-53.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(huehn.wheat)
-  dat <- huehn.wheat
-  
-  # Nassar & Huehn, p. 51 "there is no evidence for differences in stability
-  # among the 20 varieties".
-  libs(gge)
-
    #> 
-#> Attaching package: 'gge'
    #> The following object is masked from 'package:desplot':
-#> 
-#>     RedGrayBlue
      m1 <- gge(dat, yield ~ gen*env)
-  biplot(m1, main="huehn.wheat")
-
      
-  libs(reshape2)
-  datm <- acast(dat, gen~env, value.var='yield')
-
-  apply(datm,1,mean) # Gen means match Huehn 1979 table 1
-
    #>   Ack712  Beun781  Breu737  Brnd758  Brnd759   Caribo   Cbc710 Diplomat 
-#>    65.11    74.87    63.29    67.29    67.55    69.04    71.03    67.10 
-#>  Doer750  Firl777  Firl779  Firl780  Frah743  Jubilar  Loch744  Loch745 
-#>    68.60    66.60    64.31    69.63    70.89    66.45    70.46    70.72 
-#>     Pem2     Pem3  Ruem711  Stru721 
-#>    71.72    72.15    69.06    71.13 
      apply(datm,2,mean) # Env means
-
    #>    E01    E02    E03    E04    E05    E06    E07    E08    E09    E10 
-#> 81.770 80.845 66.745 67.540 70.915 51.450 61.095 80.660 52.835 74.645 
      apply(datm, 2, rank) # Ranks match Huehn table 1
-
    #>           E01  E02  E03  E04  E05  E06 E07  E08  E09 E10
-#> Ack712    6.5  7.0  9.0  8.0 11.0  4.0  12  1.0  1.0   5
-#> Beun781  19.0 20.0 17.0 11.5 20.0  7.0  20 18.0 12.5  20
-#> Breu737   1.0  2.0  1.0  7.0  9.0  2.0   7  4.0  2.0   2
-#> Brnd758   3.0  9.0  8.0  1.0 17.0 12.0   1 20.0 19.0   6
-#> Brnd759  17.0  1.0  3.0  3.0 12.0  5.0   4  8.0 17.0  13
-#> Caribo    6.5 16.0 11.0 18.0  2.0 13.5  11 17.0 10.0   7
-#> Cbc710    9.5 18.0 15.0 16.0 19.0 15.0  19  3.0  4.0  18
-#> Diplomat  8.0 14.0  4.5  2.0  4.0 16.0   5 12.0  7.0  11
-#> Doer750  20.0 10.5  2.0 13.0  6.5  9.0  16  6.0  6.0   3
-#> Firl777   5.0 10.5 10.0 10.0  1.0 11.0   3  7.0  3.0  12
-#> Firl779   2.0  5.0  4.5  4.0  3.0 18.0   2 13.0  5.0   1
-#> Firl780   9.5 13.0 14.0 17.0  8.0 20.0   9  9.5 12.5   9
-#> Frah743  18.0  6.0  6.0 11.5 18.0  3.0  14 16.0 20.0  16
-#> Jubilar   4.0  8.0  7.0  6.0  5.0 10.0  10  2.0 11.0   4
-#> Loch744  13.0  4.0 19.0 15.0 15.5  1.0  17 15.0 16.0  10
-#> Loch745  12.0 12.0 16.0  9.0  6.5  8.0  18 14.0 18.0  15
-#> Pem2     14.0 15.0 18.0 14.0 10.0 17.0   8 19.0  8.0  19
-#> Pem3     15.0 17.0 20.0 19.0 15.5 13.5  13 11.0  9.0  17
-#> Ruem711  11.0  3.0 12.0  5.0 14.0 19.0   6  5.0 15.0  14
-#> Stru721  16.0 19.0 13.0 20.0 13.0  6.0  15  9.5 14.0   8
    
-  # Huehn 1979 did not use genotype-centered data, and his definition
-  # of S2 is different from later papers.
-
-  # I'm not sure where 'huehn' function is found
-  # apply(huehn(datm, corrected=FALSE), 2, round,2) # S1 matches Huehn
-  ##          MeanRank   S1
-  ## Jubilar      6.70 3.62
-  ## Diplomat     8.35 5.61
-  ## Caribo      11.20 6.07
-  ## Cbc710      13.65 6.70
-
-  # Very close match to Nassar & Huehn 1987 table 4.
-  # apply(huehn(datm, corrected=TRUE), 2, round,2)
-  ##          MeanRank   S1   Z1    S2   Z2
-  ## Jubilar      10.2 4.00 5.51 11.29 4.29
-  ## Diplomat     11.0 6.31 0.09 27.78 0.27
-  ## Caribo       10.6 6.98 0.08 34.49 0.01
-  ## Cbc710       10.9 8.16 1.78 47.21 1.73
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hughes.grapes-1.png b/docs/reference/hughes.grapes-1.png deleted file mode 100644 index c1141696..00000000 Binary files a/docs/reference/hughes.grapes-1.png and /dev/null differ diff --git a/docs/reference/hughes.grapes.html b/docs/reference/hughes.grapes.html deleted file mode 100644 index ce73f103..00000000 --- a/docs/reference/hughes.grapes.html +++ /dev/null @@ -1,341 +0,0 @@ - - - - - - - - -RCB experiment of grape, disease incidence — hughes.grapes • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of grape, disease incidence

    - - -
    - -
    -

    Disease incidence on grape leaves in RCB experiment with 6 different - treatments.

    -
    - - - -

    Format

    - -

    A data frame with 270 observations on the following 6 variables.

    -
    block

    Block factor, 1-3

    -
    trt

    Treatment factor, 1-6

    -
    vine

    Vine factor, 1-3

    -
    shoot

    Shoot factor, 1-5

    -
    diseased

    Number of diseased leaves per shoot

    -
    total

    Number of total leaves per shoot

    - -
    - -

    Details

    - - - -

    These data come from a study of downy mildew on grapes. The - experiment was conducted at Wooster, Ohio, on the experimental farm of - the Ohio Agricultural Research and Development Center, Ohio State - University.

    -

    There were 3 blocks with 6 treatments. Treatment 1 is - the unsprayed control. On 30 Sep 1990, disease incidence was - measured. For each plot, 5 randomly chosen shoots on each of the 3 - vines were observed. The canopy was closed and shoots could be - intertwined. On each shoot, the total number of leaves and the number - of infected leaves were recorded.

    - -

    Used with permission of Larry Madden.

    -

    Source

    - -

    Hughes, G. and Madden, LV. 1995. - Some methods allowing for aggregated patterns of disease incidence in - the analysis of data from designed experiments. - Plant Pathology, 44, 927--943. - https://doi.org/10.1111/j.1365-3059.1995.tb02651.x

    -

    References

    - -

    Hans-Pieter Piepho. 1999. - Analysing disease incidence data from designed experiments by - generalized linear mixed models. - Plant Pathology, 48, 668--684. - https://doi.org/10.1046/j.1365-3059.1999.00383.x

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(hughes.grapes)
-dat <- hughes.grapes
-
-dat <- transform(dat, rate = diseased/total, plot=trt:block)
-
-  # Trt 1 has higher rate, more variable, Trt 3 lower rate, less variable
-  libs(lattice)
-  foo <- bwplot(rate ~ vine|block*trt, dat, main="hughes.grapes",
-                xlab="vine")
-  libs(latticeExtra)
-  useOuterStrips(foo)
-
    
-
-  # Table 1 of Piepho 1999
-  tapply(dat$rate, dat$trt, mean) # trt 1 does not match Piepho
-
    #>         T1         T2         T3         T4         T5         T6 
-#> 0.67389080 0.07451458 0.02532236 0.04157511 0.03550864 0.07506862 
      tapply(dat$rate, dat$trt, max)
-
    #>        T1        T2        T3        T4        T5        T6 
-#> 1.0000000 0.4545455 0.1000000 0.2727273 0.3750000 0.4666667 
    
-
-  # Piepho model 3.  Binomial data.  May not be exactly the same model
-  
-  # Use the binomial count data with lme4
-  libs(lme4)
-  m1 <- glmer(cbind(diseased, total-diseased) ~ trt + block + (1|plot/vine),
-              data=dat, family=binomial)
-
    #> boundary (singular) fit: see ?isSingular
      m1
-
    #> Generalized linear mixed model fit by maximum likelihood (Laplace
-#>   Approximation) [glmerMod]
-#>  Family: binomial  ( logit )
-#> Formula: cbind(diseased, total - diseased) ~ trt + block + (1 | plot/vine)
-#>    Data: dat
-#>       AIC       BIC    logLik  deviance  df.resid 
-#>  750.1527  786.1369 -365.0763  730.1527       260 
-#> Random effects:
-#>  Groups    Name        Std.Dev. 
-#>  vine:plot (Intercept) 2.335e-01
-#>  plot      (Intercept) 1.693e-05
-#> Number of obs: 270, groups:  vine:plot, 54; plot, 18
-#> Fixed Effects:
-#> (Intercept)        trtT2        trtT3        trtT4        trtT5        trtT6  
-#>     0.65868     -3.24235     -4.31193     -3.88553     -4.05726     -3.25889  
-#>     blockB2      blockB3  
-#>    -0.09745      0.21429  
-#> convergence code 0; 0 optimizer warnings; 1 lme4 warnings 
    
-  # Switch from binomial counts to bernoulli data
-  libs(aod)
-
    #> 
-#> Attaching package: 'aod'
    #> The following object is masked from 'package:survival':
-#> 
-#>     rats
    #> The following object is masked from 'package:mgcv':
-#> 
-#>     negbin
    #> The following object is masked from 'package:asreml':
-#> 
-#>     rats
    #> The following object is masked from 'package:spaMM':
-#> 
-#>     negbin
      bdat <- splitbin(cbind(diseased, total-diseased) ~ block+trt+plot+vine+shoot,
-                   data=dat)$tab
-
    #> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
-#>   Consider formula(paste(x, collapse = " ")) instead.
      names(bdat)[2] <- 'y'
-  
-  # Using lme4
-  m2 <- glmer(y ~ trt + block + (1|plot/vine), data=bdat, family=binomial)
-
    #> boundary (singular) fit: see ?isSingular
      m2
-
    #> Generalized linear mixed model fit by maximum likelihood (Laplace
-#>   Approximation) [glmerMod]
-#>  Family: binomial  ( logit )
-#> Formula: y ~ trt + block + (1 | plot/vine)
-#>    Data: bdat
-#>       AIC       BIC    logLik  deviance  df.resid 
-#> 1802.4155 1863.6065 -891.2077 1782.4155      3348 
-#> Random effects:
-#>  Groups    Name        Std.Dev. 
-#>  vine:plot (Intercept) 2.335e-01
-#>  plot      (Intercept) 2.337e-05
-#> Number of obs: 3358, groups:  vine:plot, 54; plot, 18
-#> Fixed Effects:
-#> (Intercept)        trtT2        trtT3        trtT4        trtT5        trtT6  
-#>     0.65870     -3.24234     -4.31194     -3.88552     -4.05723     -3.25895  
-#>     blockB2      blockB3  
-#>    -0.09749      0.21426  
-#> convergence code 0; 0 optimizer warnings; 1 lme4 warnings 
      
-  # Now using MASS:::glmmPQL
-  libs(MASS)
-  m3 <- glmmPQL(y ~ trt + block, data=bdat,
-                random=~1|plot/vine, family=binomial)
-
    #> iteration 1
    #> iteration 2
    #> iteration 3
      m3
-
    #> Linear mixed-effects model fit by maximum likelihood
-#>   Data: bdat 
-#>   Log-likelihood: NA
-#>   Fixed: y ~ trt + block 
-#> (Intercept)       trtT2       trtT3       trtT4       trtT5       trtT6 
-#>  0.65474283 -3.21865859 -4.28325379 -3.85830210 -4.02992042 -3.23565097 
-#>     blockB2     blockB3 
-#> -0.09687143  0.21091878 
-#> 
-#> Random effects:
-#>  Formula: ~1 | plot
-#>          (Intercept)
-#> StdDev: 0.0004264538
-#> 
-#>  Formula: ~1 | vine %in% plot
-#>         (Intercept)  Residual
-#> StdDev:   0.2354825 0.9890316
-#> 
-#> Variance function:
-#>  Structure: fixed weights
-#>  Formula: ~invwt 
-#> Number of Observations: 3358
-#> Number of Groups: 
-#>           plot vine %in% plot 
-#>             18             54 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/hunter.corn-1.png b/docs/reference/hunter.corn-1.png deleted file mode 100644 index 0ecde5b8..00000000 Binary files a/docs/reference/hunter.corn-1.png and /dev/null differ diff --git a/docs/reference/hunter.corn.html b/docs/reference/hunter.corn.html deleted file mode 100644 index c323269f..00000000 --- a/docs/reference/hunter.corn.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - -Multi-environment trial of corn with nitrogen fertilizer — hunter.corn • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn with nitrogen fertilizer

    - - -
    - -
    -

    Corn yield response to nitrogen

    -
    - - - -

    Format

    - -

    A data frame with 54 observations on the following 4 variables.

    -
    nitro

    nitrogen fertilizer, pound/acre

    -
    year

    year

    -
    loc

    location

    -
    yield

    yield, bu/ac

    - -
    - -

    Details

    - -

    Experiments were conducted in eastern Oregon during the years - 1950-1952.

    -

    Planting rates varied from 15,000 to 21,000 planter per acre.

    -

    Source

    - -

    Albert S. Hunter, John A. Yungen (1955). The Influence of - Variations in Fertility Levels Upon the Yield and Protein - Content of Field Corn in Eastern Oregon. - Soil Science Society of America Journal, 19, 214-218. - https://doi.org/10.2136/sssaj1955.03615995001900020027x

    -

    References

    - -

    James Leo Paschal, Burton Leroy French (1956). - A method of economic analysis applied to nitrogen fertilizer rate - experiments on irrigated corn. - Tech Bull 1141. United States Dept of Agriculture. - books.google.com/books?id=gAdZtsEziCcC&pg=PP1

    - -

    Examples

    - 
    
-library(agridat)
-data(hunter.corn)
-dat <- hunter.corn
-
-dat <- transform(dat, env=factor(paste(loc,year)))
-
-libs(lattice)
-xyplot(yield~nitro|env, dat, type='b',
-main="hunter.corn - nitrogen response curves")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/igue.sugarcane.uniformity-1.png b/docs/reference/igue.sugarcane.uniformity-1.png deleted file mode 100644 index 711045b1..00000000 Binary files a/docs/reference/igue.sugarcane.uniformity-1.png and /dev/null differ diff --git a/docs/reference/igue.sugarcane.uniformity.html b/docs/reference/igue.sugarcane.uniformity.html deleted file mode 100644 index 2d7fdeb3..00000000 --- a/docs/reference/igue.sugarcane.uniformity.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Uniformity trial with sugarcane — igue.sugarcane.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial with sugarcane

    - - -
    - -
    -

    Uniformity trial with sugarcane in Brazil, 1982.

    -
    - - 
    data("igue.sugarcane.uniformity")
    - - -

    Format

    - -

    A data frame with 1512 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, kg/plot

    - -
    - -

    Details

    - -

    A uniformity trial with sugarcane in the state of Sao Paulo, Brazil, in 1982. - The field was 40 rows, each 90 m long, with 1.5 m between rows.

    -

    Field width: 36 plots * 1.5 m = 54 m

    -

    Field length: 42 plots * 2 m = 84 m

    -

    Source

    - -

    Toshio Igue, Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). - Tamanho e forma de parcela experimental para cana-de-acucar (Plot size and shape for sugar cane experiments). - Bragantia, 50, 163-180. Appendix, page 169-170. - https://dx.doi.org/10.1590/S0006-87051991000100016

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-data(igue.sugarcane.uniformity)
-dat <- igue.sugarcane.uniformity
-
-# match Igue CV top row of page 171
-sd(dat$yield)/mean(dat$yield) # 16.4
-
    #> [1] 0.1638649
    
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, tick=TRUE, aspect=(42*2)/(36*1.5),
-        main="igue.sugarcane.uniformity")
-
      
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ilri.sheep-1.png b/docs/reference/ilri.sheep-1.png deleted file mode 100644 index 99ff2824..00000000 Binary files a/docs/reference/ilri.sheep-1.png and /dev/null differ diff --git a/docs/reference/ilri.sheep-2.png b/docs/reference/ilri.sheep-2.png deleted file mode 100644 index ed2fc4b2..00000000 Binary files a/docs/reference/ilri.sheep-2.png and /dev/null differ diff --git a/docs/reference/ilri.sheep.html b/docs/reference/ilri.sheep.html deleted file mode 100644 index 7046ee57..00000000 --- a/docs/reference/ilri.sheep.html +++ /dev/null @@ -1,349 +0,0 @@ - -Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep • agridat - - -
    -
    - - - -
    -
    -
    -

    Birth weight and weaning weight of Dorper x Red Maasi lambs

    - - -
    - -
    -

    Birth weight and weaning weight of 882 lambs from a partial diallel - cross of Dorper and Red Maasi breeds.

    -
    - - -
    -

    Format

    -

    A data frame with 882 observations on the following 12 variables.

    year
    -

    year of lamb birth, 1991-1996

    - -
    lamb
    -

    lamb id

    - -
    sex
    -

    sex of lamb, M=Male/F=Female

    - -
    gen
    -

    genotype, DD, DR, RD, RR

    - -
    birthwt
    -

    weight of lamb at birth, kg

    - -
    weanwt
    -

    weight of lamb at weaning, kg

    - -
    weanage
    -

    age of lamb at weaning, days

    - -
    ewe
    -

    ewe id

    - -
    ewegen
    -

    ewe genotype: D, R

    - -
    damage
    -

    ewe (dam) age in years

    - -
    ram
    -

    ram id

    - -
    ramgen
    -

    ram genotype: D, R

    - - -
    -
    -

    Details

    - - -

    Red Maasai sheep in East Africa are perceived to be resistant to - certain parasites. ILRI decided in 1990 to investigate the degree of - resistance exhibited by this Red Maasai breed and initiated a study in - Kenya. A susceptible breed, the Dorper, - was chosen to provide a direct comparison with the Red Maasai. The - Dorper is well-adapted to this area and is also larger than the Red - Maasai, and this makes these sheep attractive to farmers.

    -

    Throughout six years from 1991 to 1996 Dorper (D), Red Maasai (R) and - Red Maasai x Dorper crossed ewes were mated to Red Maasai and Dorper - rams to produce a number of different lamb genotypes. For the purposes - of this example, only the following four offspring genotypes are - considered (Sire x Dam): D x D, D x R, R x D and R x R.

    -

    Records are missing in 182 of the lambs, mostly because of earlier death.

    -
    -
    -

    Source

    -

    Mixed model analysis for the estimation of components of genetic - variation in lamb weaning weight. International Livestock Research Institute. - Permanent link: https://hdl.handle.net/10568/10364 - https://biometrics.ilri.org/CS/case - Retrieved Dec 2011.

    -

    Licensed with Creative Commons BY-NC-SA 3.0 Unported license.

    -
    -
    -

    References

    -

    Baker, RL and Nagda, S. and Rodriguez-Zas, SL and Southey, BR and - Audho, JO and Aduda, EO and Thorpe, W. (2003). - Resistance and resilience to gastro-intestinal nematode parasites and - relationships with productivity of Red Maasai, Dorper and Red Maasai x - Dorper crossbred lambs in the sub-humid tropics. - Animal Science, 76, 119-136. - https://doi.org/10.1017/S1357729800053388

    -

    Gota Morota, Hao Cheng, Dianne Cook, Emi Tanaka (2021). - ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in - the era of data-rich animal science. - Journal of Animal Science, Volume 99, Issue 2, February 2021, skaa402. - https://doi.org/10.1093/jas/skaa402

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(ilri.sheep)
-  dat <- ilri.sheep
-  dat <- transform(dat, lamb=factor(lamb), ewe=factor(ewe), ram=factor(ram),
-                   year=factor(year))
-  # dl is linear covariate, same as damage, but truncated to [2,8]
-  dat <- within(dat, {
-    dl <- damage
-    dl <- ifelse(dl < 3, 2, dl)
-    dl <- ifelse(dl > 7, 8, dl)
-    dq <- dl^2
-  })
-
-  dat <- subset(dat, !is.na(weanage))
-
-  # EDA
-  libs(lattice)
-  ## bwplot(weanwt ~ year, dat, main="ilri.sheep", xlab="year", ylab="Wean weight",
-  ##        panel=panel.violin) # Year effect
-  bwplot(weanwt ~ factor(dl), dat,
-         main="ilri.sheep", xlab="Dam age", ylab="Wean weight") # Dam age effect
-
-  # bwplot(weanwt ~ gen, dat,
-  #        main="ilri.sheep", xlab="Genotype", ylab="Wean weight") # Genotype differences
-  xyplot(weanwt ~ weanage, dat, type=c('p','smooth'),
-         main="ilri.sheep", xlab="Wean age", ylab="Wean weight") # Age covariate
-
-
-  # case study page 4.18
-  lm1 <- lm(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen, data=dat)
-  summary(lm1)
-#> 
-#> Call:
-#> lm(formula = weanwt ~ year + sex + weanage + dl + dq + ewegen + 
-#>     ramgen, data = dat)
-#> 
-#> Residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -7.4037 -1.3274 -0.0109  1.4403  7.7063 
-#> 
-#> Coefficients:
-#>              Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept)  0.274005   1.065133   0.257  0.79706    
-#> year92      -1.565831   0.292949  -5.345 1.23e-07 ***
-#> year93      -1.095781   0.275268  -3.981 7.60e-05 ***
-#> year94      -2.832501   0.357504  -7.923 9.34e-15 ***
-#> year95      -3.228367   0.343630  -9.395  < 2e-16 ***
-#> year96      -2.351101   0.389751  -6.032 2.64e-09 ***
-#> sexM         0.477910   0.169498   2.820  0.00495 ** 
-#> weanage      0.070217   0.008856   7.928 8.97e-15 ***
-#> dl           2.726355   0.315012   8.655  < 2e-16 ***
-#> dq          -0.268882   0.034007  -7.907 1.05e-14 ***
-#> ewegenR     -0.585536   0.236554  -2.475  0.01355 *  
-#> ramgenR     -0.442866   0.172768  -2.563  0.01058 *  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 2.221 on 688 degrees of freedom
-#>   (7 observations deleted due to missingness)
-#> Multiple R-squared:  0.3835,	Adjusted R-squared:  0.3736 
-#> F-statistic:  38.9 on 11 and 688 DF,  p-value: < 2.2e-16
-#> 
-  anova(lm1)
-#> Analysis of Variance Table
-#> 
-#> Response: weanwt
-#>            Df Sum Sq Mean Sq F value    Pr(>F)    
-#> year        5 1208.1  241.63 48.9853 < 2.2e-16 ***
-#> sex         1   56.0   55.98 11.3494 0.0007968 ***
-#> weanage     1  344.2  344.21 69.7804 3.651e-16 ***
-#> dl          1  151.5  151.51 30.7160 4.258e-08 ***
-#> dq          1  275.8  275.79 55.9115 2.316e-13 ***
-#> ewegen      1   42.7   42.69  8.6548 0.0033717 ** 
-#> ramgen      1   32.4   32.41  6.5708 0.0105780 *  
-#> Residuals 688 3393.7    4.93                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-  # ----------
-
-  libs(lme4)
-#> 
-#> Attaching package: 'lme4'
-#> The following object is masked from 'package:nlme':
-#> 
-#>     lmList
-  lme1 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
-                 (1|ewe) + (1|ram), data=dat)
-  print(lme1, corr=FALSE)
-#> Linear mixed model fit by REML ['lmerMod']
-#> Formula: weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1 |  
-#>     ewe) + (1 | ram)
-#>    Data: dat
-#> REML criterion at convergence: 3081.555
-#> Random effects:
-#>  Groups   Name        Std.Dev.
-#>  ewe      (Intercept) 1.207   
-#>  ram      (Intercept) 0.258   
-#>  Residual             1.851   
-#> Number of obs: 700, groups:  ewe, 358; ram, 74
-#> Fixed Effects:
-#> (Intercept)       year92       year93       year94       year95       year96  
-#>     0.18579     -1.57090     -1.07663     -3.00251     -3.28832     -2.45008  
-#>        sexM      weanage           dl           dq      ewegenR      ramgenR  
-#>     0.40381      0.06593      2.92232     -0.28997     -0.45429     -0.41304  
-  lme2 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
-                 (1|ewe), data=dat)
-  lme3 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen +
-                 (1|ram), data=dat)
-  anova(lme1, lme2,  lme3)
-#> refitting model(s) with ML (instead of REML)
-#> Data: dat
-#> Models:
-#> lme2: weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1 | ewe)
-#> lme3: weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1 | ram)
-#> lme1: weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1 | ewe) + (1 | ram)
-#>      npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)    
-#> lme2   14 3081.0 3144.7 -1526.5   3053.0                         
-#> lme3   14 3119.4 3183.1 -1545.7   3091.4  0.000  0               
-#> lme1   15 3082.6 3150.9 -1526.3   3052.6 38.735  1  4.853e-10 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-  # ----------
-
-  libs(asreml,lucid)
-  # case study page 4.20
-  m1 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen,
-               data=dat)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -927.900       4.93270    688 15:17:37    0.0
-  # wald(m1)
-  
-  # case study page 4.26
-  m2 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen,
-               random = ~ ram + ewe, data=dat)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:37 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -920.939       4.16476    688 15:17:38    0.0
-#>  2      -914.393       3.95256    688 15:17:38    0.0
-#>  3      -909.836       3.69363    688 15:17:38    0.0
-#>  4      -908.663       3.51028    688 15:17:38    0.0
-#>  5      -908.548       3.43112    688 15:17:38    0.0
-#>  6      -908.548       3.42731    688 15:17:38    0.0
-  # wald(m2)
-  
-  # case study page 4.37, year means
-  # predict(m2, data=dat, classify="year")
-  ##   year predicted.value standard.error est.status
-  ## 1   91       12.638564      0.2363652  Estimable
-  ## 2   92       11.067659      0.2285252  Estimable
-  ## 3   93       11.561932      0.1809891  Estimable
-  ## 4   94        9.636058      0.2505478  Estimable
-  ## 5   95        9.350247      0.2346849  Estimable
-  ## 6   96       10.188482      0.2755387  Estimable
-    
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/immer.sugarbeet.uniformity-1.png b/docs/reference/immer.sugarbeet.uniformity-1.png deleted file mode 100644 index afb523e4..00000000 Binary files a/docs/reference/immer.sugarbeet.uniformity-1.png and /dev/null differ diff --git a/docs/reference/immer.sugarbeet.uniformity-2.png b/docs/reference/immer.sugarbeet.uniformity-2.png deleted file mode 100644 index 51cd9b6a..00000000 Binary files a/docs/reference/immer.sugarbeet.uniformity-2.png and /dev/null differ diff --git a/docs/reference/immer.sugarbeet.uniformity-3.png b/docs/reference/immer.sugarbeet.uniformity-3.png deleted file mode 100644 index dd0ddca1..00000000 Binary files a/docs/reference/immer.sugarbeet.uniformity-3.png and /dev/null differ diff --git a/docs/reference/immer.sugarbeet.uniformity-4.png b/docs/reference/immer.sugarbeet.uniformity-4.png deleted file mode 100644 index a1fcb34d..00000000 Binary files a/docs/reference/immer.sugarbeet.uniformity-4.png and /dev/null differ diff --git a/docs/reference/immer.sugarbeet.uniformity.html b/docs/reference/immer.sugarbeet.uniformity.html deleted file mode 100644 index 8bd006c9..00000000 --- a/docs/reference/immer.sugarbeet.uniformity.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sugarbeets, measurements of yield, sugar, purity

    - - -
    - -
    -

    Uniformity trial of sugarbeets, at Minnesota, 1930, with measurements - of yield, sugar, purity.

    -
    - - - -

    Format

    - -

    A data frame with 600 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds

    -
    sugar

    sugar percentage

    -
    purity

    apparent purity

    - -
    - -

    Details

    - - - -

    Beets were planted in rows 22 inches apart, thinned to 1 plant per - row. At harvest, the rows were marked into segments 33 feet long with - 2 foot alleys between ends of plots. The harvested area was 60 rows - 350 feet long.

    -

    Field width: 10 plots * 33 feet + 9 alleys * 2 feet = 348 feet

    -

    Field length: 60 plots/rows * 22 in/row / 12 in/feet = 110 feet

    -

    Planted in 1930. Field conditions were uniform. Beets were planted - in rows 22 inches apart. After thinning, one beet was left in each - 12-inch unit. At harvest, the field was marked out in plot 33 feet - long, with a 2-foot alley between plots to minimize carryover from the - harvester. A sample of 10 beets was taken uniformly (approximately - every third beet) and measured for sugar percentage and apparent - purity. The beets were counted at weighing time and the yields were - calculated on the basis of 33 beets per plot.

    -

    Immer found that aggregating the data from one row to two - resulted in a dramatic reduction in the standard error (for yield).

    -

    Source

    - -

    F. R. Immer. 1932. - Size and shape of plot in relation to field experiments with sugar beets. - Jour. Agr. Research, 44, 649--668. - https://naldc.nal.usda.gov/download/IND43968078/PDF

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(immer.sugarbeet.uniformity)
-  dat <- immer.sugarbeet.uniformity
-
-  # Immer numbers rows from the top
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          aspect=110/348, tick=TRUE, flip=TRUE, # true aspect
-          main="immer.sugarbeet.uniformity - yield")
-
      desplot(dat, sugar~col*row,
-          aspect=110/348, tick=TRUE, flip=TRUE,
-          main="immer.sugarbeet.uniformity - sugar")
-
      desplot(dat, purity~col*row,
-          aspect=110/348, tick=TRUE, flip=TRUE,
-          main="immer.sugarbeet.uniformity - purity")
-
      
-  pairs(dat[,c('yield','sugar','purity')],
-        main="immer.sugarbeet.uniformity")
-
    
-# } 
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 46b58ca4..00000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,1412 +0,0 @@ - -Function reference • agridat - - -
    -
    - - - -
    -
    -
    -

    Reference

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
    -

    aastveit.barley.covs aastveit.barley.height

    -

    Barley heights and environmental covariates in Norway

    -

    acorsi.grayleafspot

    -

    Multi-environment trial evaluating 36 maize genotypes in 9 locations

    -

    adugna.sorghum

    -

    Multi-environment trial of sorghum at 3 locations across 5 years

    -

    agridat

    -

    Datasets from agricultural experiments

    -

    allcroft.lodging

    -

    Multi-environment trial of cereal with lodging data

    -

    alwan.lamb

    -

    For the 34 sheep sires, the number of lambs in each of 5 foot shape classes.

    -

    ansari.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    arankacami.groundnut.uniformity

    -

    Uniformity trial of groundnut

    -

    archbold.apple

    -

    Split-split plot experiment of apple trees

    -

    ars.earlywhitecorn96

    -

    Multi-environment trial of early white food corn

    -

    australia.soybean

    -

    Multi-environment trial of soybean in Australia

    -

    baker.barley.uniformity

    -

    Uniformity trials of barley, 10 years on same ground

    -

    baker.strawberry.uniformity

    -

    Uniformity trial of strawberry

    -

    baker.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    bancroft.peanut.uniformity

    -

    Uniformity trial of peanuts

    -

    barrero.maize

    -

    Multi-environment trial of maize in Texas.

    -

    batchelor.uniformity

    -

    Uniformity trials of apples, lemons, oranges, and walnuts

    -

    battese.survey

    -

    Survey and satellite data for corn and soy areas in Iowa

    -

    beall.webworms

    -

    Counts of webworms in a beet field, with insecticide treatments.

    -

    beaven.barley

    -

    Yields of 8 barley varieties in 1913 as used by Student.

    -

    becker.chicken

    -

    Mating crosses of chickens

    -

    besag.bayesian

    -

    RCB experiment of spring barley in United Kingdom

    -

    besag.beans

    -

    Competition experiment in beans with height measurements

    -

    besag.checks

    -

    Check variety yields in winter wheat.

    -

    besag.elbatan

    -

    RCB experiment of wheat, 50 varieties in 3 blocks with strong - spatial trend.

    -

    besag.endive

    -

    Presence of footroot disease in an endive field

    -

    besag.met

    -

    Multi-environment trial of corn, incomplete-block design

    -

    besag.triticale

    -

    Four-way factorial agronomic experiment in triticale

    -

    blackman.wheat

    -

    Multi-environment trial of wheat, conventional and semi-dwarf varieties

    -

    bliss.borers

    -

    Corn borer infestation under four treatments

    -

    bond.diallel

    -

    Diallel cross of winter beans

    -

    bose.multi.uniformity

    -

    Uniformity trials of barley, wheat, lentils

    -

    box.cork

    -

    Weight of cork samples on four sides of trees

    -

    bradley.multi.uniformity

    -

    Uniformity trial of 4 crops on the same land

    -

    brandle.rape

    -

    Multi-environment trial of rape in Manitoba

    -

    brandt.switchback

    -

    Switchback experiment on dairy cattle, milk yield for two treatments

    -

    bridges.cucumber

    -

    Multi-environment trial of cucumbers in a latin square design

    -

    broadbalk.wheat

    -

    Long term wheat yields on Broadbalk fields at Rothamsted.

    -

    bryan.corn.uniformity

    -

    Uniformity trial of corn at 3 locations in Iowa.

    -

    buntaran.wheat

    -

    Multi-environment trial of wheat in Sweden in 2016.

    -

    burgueno.alpha

    -

    Incomplete block alpha design

    -

    burgueno.rowcol

    -

    Row-column design

    -

    burgueno.unreplicated

    -

    Field experiment with unreplicated genotypes plus one repeated check.

    -

    butron.maize

    -

    Multi-environment trial of maize with pedigrees

    -

    byers.apple

    -

    Diameters of apples

    -

    caribbean.maize

    -

    Multi-environment trial of maize with fertilization

    -

    carlson.germination

    -

    Germination of alfalfa seeds at various salt concentrations

    -

    carmer.density

    -

    Nonlinear maize yield-density model

    -

    cate.potassium

    -

    Relative cotton yield for different soil potassium concentrations

    -

    chinloy.fractionalfactorial

    -

    Sugarcane fractional factorial 1/3 3^5

    -

    christidis.competition

    -

    Competition between varieties in cotton

    -

    christidis.cotton.uniformity

    -

    Uniformity trial of cotton

    -

    christidis.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    cleveland.soil

    -

    Soil resistivity in a field

    -

    cochran.beets

    -

    Yield and number of plants in a sugarbeet fertilizer experiment

    -

    cochran.bib

    -

    Multi-environment trial of corn, balanced incomplete block design

    -

    cochran.crd

    -

    Potato scab infection with sulfur treatments

    -

    cochran.eelworms

    -

    Counts of eelworms before and after fumigant treatments

    -

    cochran.factorial

    -

    Factorial experiment in beans

    -

    cochran.latin

    -

    Latin square design in wheat

    -

    cochran.lattice

    -

    Balanced lattice experiment in cotton

    -

    cochran.wireworms

    -

    Wireworms controlled by fumigants in a latin square

    -

    connolly.potato

    -

    Potato yields in single-drill plots

    -

    cornelius.maize

    -

    Multi-environment trial of maize for 9 cultivars at 20 locations.

    -

    corsten.interaction

    -

    Multi-environment trial of corn

    -

    cox.stripsplit

    -

    Strip-split-plot of barley with fertilizer, calcium, and soil factors.

    -

    cramer.cucumber

    -

    Cucumber yields and quantitative traits

    -

    crampton.pig

    -

    Weight gain in pigs for different treatments

    -

    crossa.wheat

    -

    Multi-environment trial of wheat for 18 genotypes at 25 locations

    -

    crowder.seeds

    -

    Germination of Orobanche seeds for two genotypes and two treatments.

    -

    cullis.earlygen

    -

    Early generation variety trial in wheat

    -

    damesa.maize

    -

    Incomplete-block experiment of maize in Ethiopia.

    -

    darwin.maize

    -

    Darwin's maize data of crossed/inbred plant heights

    -

    dasilva.maize

    -

    Multi-environment trial of maize

    -

    dasilva.soybean.uniformity

    -

    Uniformity trial of soybean

    -

    davidian.soybean

    -

    Growth of soybean varieties in 3 years

    -

    davies.pasture.uniformity

    -

    Uniformity trial of pasture.

    -

    day.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    denis.missing

    -

    Multi-environment trial with structured missing values

    -

    denis.ryegrass

    -

    Multi-environment trial of perennial ryegrass in France

    -

    depalluel.sheep

    -

    Latin square of four breeds of sheep with four diets

    -

    devries.pine

    -

    Graeco-Latin Square experiment in pine

    -

    digby.jointregression

    -

    Multi-environment trial of wheat

    -

    diggle.cow

    -

    Bodyweight of cows in a 2-by-2 factorial experiment

    -

    draper.safflower.uniformity

    -

    Uniformity trial of safflower

    -

    durban.competition

    -

    Sugar beet yields with competition effects

    -

    durban.rowcol

    -

    Row-column experiment of spring barley, many varieties

    -

    durban.splitplot

    -

    Split-plot experiment of barley with fungicide treatments

    -

    eden.nonnormal

    -

    Height of barley plants in a study of non-normal data

    -

    eden.potato

    -

    Potato yields in response to potash and nitrogen fertilizer

    -

    eden.tea.uniformity

    -

    Uniformity trial of tea

    -

    edwards.oats

    -

    Multi-environment trial of oats in United States, 5 locations, 7 years.

    -

    engelstad.nitro

    -

    Multi-environment trial of corn with nitrogen fertilizer

    -

    fan.stability

    -

    Multi-environment trial of maize hybrids in China

    -

    federer.diagcheck

    -

    Wheat experiment with diagonal checks

    -

    federer.tobacco

    -

    RCB of tobacco, height plants exposed to radiation

    -

    fisher.barley

    -

    Multi-environment trial of 5 barley varieties, 6 locations, 2 years

    -

    fisher.latin

    -

    Latin square experiment on mangolds

    -

    foulley.calving

    -

    Calving difficulty by calf sex and age of dam

    -

    fox.wheat

    -

    Multi-environment trial of wheat, 22 varieties at 14 sites in Australia

    -

    garber.multi.uniformity

    -

    Uniformity trials of oat hay and wheat grain

    -

    gartner.corn

    -

    Yield monitor data from a corn field in Minnesota

    -

    gathmann.bt

    -

    Impact of Bt corn on non-target species

    -

    gauch.soy

    -

    Multi-environment trial of soybeans in New York, 1977 to 1988

    -

    george.wheat

    -

    Multi-location/year breeding trial in California

    -

    giles.wheat

    -

    Straw length and ear emergence for wheat genotypes.

    -

    gilmour.serpentine

    -

    Wheat yield in South Australia with serpentine row/col effects

    -

    gilmour.slatehall

    -

    Slate Hall Farm 1978

    -

    gomez.fractionalfactorial

    -

    Rice fractional factorial experiment 1/2 2^6.

    -

    gomez.groupsplit

    -

    Group balanced split-plot design in rice

    -

    gomez.heterogeneity

    -

    RCB experiment of rice, heterogeneity of regressions

    -

    gomez.heteroskedastic

    -

    RCB experiment of rice, heteroskedastic varieties

    -

    gomez.multilocsplitplot

    -

    Multi-environment trial of rice, split-plot design

    -

    gomez.nitrogen

    -

    Soil nitrogen at three times for eight fertilizer treatments

    -

    gomez.nonnormal1

    -

    Insecticide treatment effectiveness

    -

    gomez.nonnormal2

    -

    RCB experiment of rice, measuring white heads

    -

    gomez.nonnormal3

    -

    RCB experiment of rice, 12 varieties with leafhopper survival

    -

    gomez.rice.uniformity

    -

    Uniformity trial of rice

    -

    gomez.seedrate

    -

    RCB experiment of rice, 6 densities

    -

    gomez.splitplot.subsample

    -

    Split-plot experiment of rice, with subsamples

    -

    gomez.splitsplit

    -

    Split-split-plot experiment of rice

    -

    gomez.stripplot

    -

    Strip-plot experiment of rice

    -

    gomez.stripsplitplot

    -

    Strip-split-plot experiment of rice

    -

    gomez.wetdry

    -

    Rice yield in wet & dry seasons with nitrogen fertilizer treatments

    -

    gotway.hessianfly

    -

    Hessian fly damage to wheat varieties

    -

    goulden.barley.uniformity

    -

    Uniformity trial of barley

    -

    goulden.eggs

    -

    Sample of egg weights on 24 consecutive days

    -

    goulden.latin

    -

    Latin square experiment for testing fungicide

    -

    goulden.splitsplit

    -

    Split-split-plot experiment of wheat

    -

    graybill.heteroskedastic

    -

    Multi-environment trial of wheat varieties with heteroskedastic yields

    -

    gregory.cotton

    -

    Factorial experiment of cotton in Sudan.

    -

    grover.diallel

    -

    Diallel 6x6

    -

    grover.rcb.subsample

    -

    Rice RCB with subsamples

    -

    gumpertz.pepper

    -

    Phytophtera disease incidence in a pepper field

    -

    hadasch.lettuce

    -

    Lettuce resistance to downy mildew resistance (with marker data)

    -

    hanks.sprinkler

    -

    Wheat yields in a line-source sprinkler experiment

    -

    hanover.whitepine

    -

    Mating crosses of white pine trees

    -

    harris.multi.uniformity

    -

    Uniformity trials with multiple crops, 15 years on the same land

    -

    harris.wateruse

    -

    Water use by horticultural trees

    -

    harrison.priors

    -

    Ranges of analytes in soybean from other authors

    -

    hartman.tomato.uniformity

    -

    Uniformity trial of tomato

    -

    harvey.lsmeans

    -

    Average daily gain of 65 steers for 3 lines, 9 sires.

    -

    harville.lamb

    -

    Birth weight of lambs from different lines/sires

    -

    hayman.tobacco

    -

    Diallel cross of Aztec tobacco

    -

    hazell.vegetables

    -

    Gross profit for 4 vegetable crops in 6 years

    -

    heady.fertilizer

    -

    Yield of corn, alfalfa, clover with two fertilizers

    -

    heath.raddish.uniformity

    -

    Uniformity trial of raddish

    -

    henderson.milkfat

    -

    Milk fat yields for a single cow

    -

    hernandez.nitrogen

    -

    Multi-environment trial of corn with nitrogen fertilizer at 5 sites.

    -

    hessling.argentina

    -

    Relation between wheat yield and weather in Argentina

    -

    hildebrand.systems

    -

    Multi-environment trial of maize for four cropping systems

    -

    holland.arthropods

    -

    Counts of arthropods in a grid-sampled wheat field

    -

    holshouser.splitstrip

    -

    Split-strip-plot of soybeans

    -

    holtsmark.timothy.uniformity

    -

    Uniformity trial of timothy

    -

    huehn.wheat

    -

    Multi-environment trial of wheat to illustrate stability statistics

    -

    hughes.grapes

    -

    RCB experiment of grape, disease incidence

    -

    hunter.corn

    -

    Multi-environment trial of corn with nitrogen fertilizer

    -

    igue.sugarcane.uniformity

    -

    Uniformity trial with sugarcane

    -

    ilri.sheep

    -

    Birth weight and weaning weight of Dorper x Red Maasi lambs

    -

    immer.sugarbeet.uniformity

    -

    Uniformity trial of sugarbeets, measurements of yield, sugar, purity

    -

    ivins.herbs

    -

    Percent ground cover of herbage species and nettles.

    -

    iyer.wheat.uniformity

    -

    Uniformity trials of wheat in India

    -

    jansen.apple

    -

    Infestation of apple shoots by apple canker.

    -

    jansen.carrot

    -

    Infestation of carrots by fly larvae

    -

    jansen.strawberry

    -

    Ordered disease ratings of strawberry crosses.

    -

    jayaraman.bamboo

    -

    Bamboo progeny trial

    -

    jenkyn.mildew

    -

    Yields from treatment for mildew control

    -

    john.alpha

    -

    Alpha lattice design of spring oats

    -

    johnson.blight

    -

    Potato blight due to weather in Prosser, Washington

    -

    johnson.douglasfir

    -

    A study of small-plots of old-growth Douglas Fir in Oregon.

    -

    jones.corn.uniformity

    -

    Uniformity trial of corn.

    -

    kadam.millet.uniformity

    -

    Uniformity trial of millet

    -

    kalamkar.potato.uniformity

    -

    Uniformity trial of potatoes

    -

    kalamkar.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    kang.maize

    -

    Multi-environment trial of maize in Louisianna at 4 locs in 3 years

    -

    kang.peanut

    -

    Multi-environment trial of peanuts for 10 genotypes in 15 environments

    -

    karcher.turfgrass

    -

    Turfgrass ratings for different treatments

    -

    kayad.alfalfa

    -

    Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia.

    -

    keen.potatodamage

    -

    Damage to potato tubers from lifting rods.

    -

    kempton.barley.uniformity

    -

    Uniformity trial of barley

    -

    kempton.competition

    -

    Sugar beet trial with competition effects

    -

    kempton.rowcol

    -

    Row-column experiment of wheat

    -

    kempton.slatehall

    -

    Slate Hall Farm 1976 spring wheat

    -

    kenward.cattle

    -

    Repeated measurement of weights of calves with two treatments.

    -

    kerr.sugarcane.uniformity

    -

    Uniformity trials of sugarcane, 4 fields

    -

    khin.rice.uniformity

    -

    Uniformity trial of rice

    -

    kiesselbach.oats.uniformity

    -

    Uniformity trial of oats

    -

    kreusler.maize

    -

    Growth of maize plants in Germany during 1875-1878

    -

    kristensen.barley.uniformity

    -

    Uniformity trial of barley

    -

    kulkarni.sorghum.uniformity

    -

    Uniformity trial of sorghum

    -

    lambert.soiltemp

    -

    Average monthly soil temperature near Zurich

    -

    lander.multi.uniformity

    -

    Uniformity trials of wheat and chari, 4 years on the same land.

    -

    lasrosas.corn

    -

    Yield monitor data for a corn field in Argentina with variable nitrogen.

    -

    lavoranti.eucalyptus

    -

    Height of Eucalyptus trees in southern Brazil

    -

    laycock.tea.uniformity

    -

    Uniformity trials of tea

    -

    lee.potatoblight

    -

    Repeated measurements of resistance to potato blight

    -

    lehmann.millet.uniformity

    -

    Uniformity trial of millet in India

    -

    lehner.soybeanmold

    -

    Yield, white mold, and sclerotia for soybeans in Brazil

    -

    lessman.sorghum.uniformity

    -

    Uniformity trial of sorghum

    -

    li.millet.uniformity

    -

    Uniformity trial of millet

    -

    libs()

    -

    Load multiple packages and install if needed - -The 'agridat' package uses dozens of packages in the examples for each dataset. -The 'libs' function provides a simple way to load multiple packages at once, -and can install any missing packages on-the-fly. - -This is very similar to the `pacman::p_load` function.

    -

    lillemo.wheat

    -

    Multi-environment trial of wheat susceptibile to powdery mildew

    -

    lin.superiority

    -

    Multi-environment trial of 33 barley genotypes in 12 locations

    -

    lin.unbalanced

    -

    Multi-environment trial of 33 barley genotypes in 18 locations

    -

    linder.wheat

    -

    Multi-environment trial of wheat in Switzerland

    -

    little.splitblock

    -

    Split-block experiment of sugar beets

    -

    loesell.bean.uniformity

    -

    Uniformity trial of white pea beans

    -

    lonnquist.maize

    -

    Multi-environment trial of maize, half diallel

    -

    lord.rice.uniformity

    -

    Uniformity trial of rice

    -

    love.cotton.uniformity

    -

    Uniformity trial of cotton

    -

    lu.stability

    -

    Multi-environment trial of maize, to illustrate stability statistics

    -

    lucas.switchback

    -

    Switchback experiment on dairy cattle, milk yield for 3 treatments

    -

    lyon.potato.uniformity

    -

    Uniformity trial of potatoes

    -

    lyons.wheat

    -

    Multi-environment trial of winter wheat at 12 sites in 4 years.

    -

    magistad.pineapple.uniformity

    -

    Uniformity trial of pineapple

    -

    masood.rice.uniformity

    -

    Uniformity trial of rice

    -

    mcclelland.corn.uniformity

    -

    Uniformity trial of corn

    -

    mcconway.turnip

    -

    RCB experiment of turnips

    -

    mcleod.barley

    -

    Multi-environment trial of barley in South Canterbury with yield and yield components

    -

    mead.cauliflower

    -

    Leaves for cauliflower plants at different times

    -

    mead.cowpea.maize

    -

    Intercropping experiment of maize/cowpea

    -

    mead.germination

    -

    Seed germination with different temperatures/concentrations

    -

    mead.lamb

    -

    Number of lambs born to 3 breeds on 3 farms

    -

    mead.strawberry

    -

    RCB experiment of strawberry

    -

    mead.turnip

    -

    Density/spacing experiment for turnips in 3 blocks.

    -

    mercer.mangold.uniformity

    -

    Uniformity trial of mangolds

    -

    mercer.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    miguez.biomass

    -

    Biomass of 3 crops in Greece

    -

    minnesota.barley.weather

    -

    Monthly weather at 6 sites in Minnesota 1927-1936.

    -

    minnesota.barley.yield

    -

    Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936.

    -

    montgomery.wheat.uniformity

    -

    Uniformity trial of wheat, 2 years on the same land

    -

    moore.uniformity

    -

    Uniformity trials of pole beans, bush beans, sweet corn, carrots, - spring and fall cauliflower

    -

    nagai.strawberry.uniformity

    -

    Uniformity trial of strawberry

    -

    nair.turmeric.uniformity

    -

    Uniformity trial of turmeric.

    -

    narain.sorghum.uniformity

    -

    Uniformity trial of sorghum

    -

    nass.barley nass.corn nass.cotton nass.hay nass.sorghum nass.wheat nass.rice nass.soybean

    -

    U.S. historical crop yields by state

    -

    nebraska.farmincome

    -

    Nebraska farm income in 2007 by county

    -

    nonnecke.peas.uniformity

    -

    Uniformity trial of canning peas

    -

    nonnecke.sweetcorn.uniformity

    -

    Uniformity trial of sweet corn

    -

    obsi.potato.uniformity

    -

    Uniformity trial of potato in Africa 2001

    -

    odland.soybean.uniformity

    -

    Uniformity trials of soy hay and soybeans

    -

    omer.sorghum

    -

    Multi-environment trial of sorghum, 6 environments

    -

    onofri.winterwheat

    -

    Multi-environment trial of winter wheat, 7 years

    -

    ortiz.tomato.covs ortiz.tomato.yield

    -

    Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates

    -

    pacheco.soybean

    -

    Multi-environment trial of soybean in Brazil.

    -

    paez.coffee.uniformity

    -

    Uniformity trial of coffee

    -

    panse.cotton.uniformity

    -

    Uniformity trial of cotton

    -

    parker.orange.uniformity

    -

    Uniformity trial of oranges

    -

    patterson.switchback

    -

    Switchback experiment on dairy cattle, milk yield for 4 treatments

    -

    pearce.apple

    -

    Apple tree yields for 6 treatments with covariate

    -

    pearl.kernels

    -

    Counts of yellow/white and sweet/starchy maize kernels by 15 observers

    -

    pederson.lettuce.repeated

    -

    Repeated measurements of lettuce growth

    -

    perry.springwheat

    -

    Multi-environment trial of wheat cultivars introduced 1860-1982.

    -

    petersen.sorghum.cowpea

    -

    Intercropping experiment of sorghum/cowpea

    -

    piepho.barley.uniformity

    -

    Uniformity trial of barley

    -

    piepho.cocksfoot

    -

    Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years

    -

    polson.safflower.uniformity

    -

    Uniformity trial of safflower

    -

    ratkowsky.onions

    -

    Onion yields for different densities at two locations

    -

    reid.grasses

    -

    Yields of four grasses for a wide range of nitrogen fertilizer

    -

    ridout.appleshoots

    -

    Root counts for propagated columnar apple shoots.

    -

    robinson.peanut.uniformity

    -

    Uniformity trial of peanuts

    -

    rothamsted.brussels

    -

    RCB experiment of brussels sprouts, 9 fertilizer treatments

    -

    rothamsted.oats

    -

    RCB experiment of oats, straw and grain, 9 fertilizer treatments

    -

    ryder.groundnut

    -

    RCB experiment of groundut, wet and dry yields

    -

    salmon.bunt

    -

    Fungus infection in varieties of wheat

    -

    sawyer.multi.uniformity

    -

    Uniformity trials of wheat, swedes, oats, 3 years on the same land

    -

    sayer.sugarcane.uniformity

    -

    Uniformity trial of sugarcane in India, 1933 & 1934.

    -

    senshu.rice

    -

    Multi-environment trial of rice, with solar radiation and temperature

    -

    shafi.tomato.uniformity

    -

    Uniformity trial of tomato

    -

    shafii.rapeseed

    -

    Multi-environment trial of rapeseed in U.S.

    -

    shaw.oats

    -

    Multi-environment trial of oats in India

    -

    silva.cotton

    -

    Number of cotton bolls for different levels of defoliation.

    -

    sinclair.clover

    -

    Clover yields in a factorial fertilizer experiment

    -

    smith.beans.uniformity

    -

    Uniformity trials of beans, 2 species in 2 years

    -

    smith.corn.uniformity

    -

    Uniformity trial of corn, 3 years on same ground

    -

    snedecor.asparagus

    -

    Asparagus yields for different cutting treatments

    -

    snijders.fusarium

    -

    Fusarium infection in wheat varieties

    -

    stephens.sorghum.uniformity

    -

    Uniformity trial of sorghum silage

    -

    steptoe.morex.pheno

    -

    Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex

    -

    stickler.sorghum.uniformity

    -

    Uniformity trial of sorghum

    -

    stirret.borers

    -

    Corn borer control by application of fungal spores.

    -

    streibig.competition

    -

    Competition experiment between barley and sinapis.

    -

    strickland.apple.uniformity

    -

    Uniformity trial in apple

    -

    strickland.grape.uniformity

    -

    Uniformity trial of grape

    -

    strickland.peach.uniformity

    -

    Uniformity trial of peach

    -

    strickland.tomato.uniformity

    -

    Uniformity trial of tomato

    -

    stroup.nin

    -

    RCB experiment of wheat at the Nebraska Intrastate Nursery

    -

    stroup.splitplot

    -

    Split-plot experiment of simulated data

    -

    student.barley

    -

    Multi-environment trial of barley

    -

    tai.potato

    -

    Multi-environment trial of potato

    -

    talbot.potato.traits talbot.potato.yield

    -

    Multi-environment trial of potato in UK, yields and trait scores at 12 locations

    -

    theobald.barley

    -

    Multi-environment trial of barley, multiple years & fertilizer levels

    -

    theobald.covariate

    -

    Multi-environment trial of corn silage, Year * Loc * Variety with covariate

    -

    thompson.cornsoy

    -

    Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation

    -

    turner.herbicide

    -

    Herbicide control of larkspur

    -

    urquhart.feedlot

    -

    Weight gain calves in a feedlot

    -

    usgs.herbicides

    -

    Concentrations of herbicides in streams in the United States

    -

    vaneeuwijk.drymatter

    -

    Multi-environment trial of maize, dry matter content

    -

    vaneeuwijk.fusarium

    -

    Infection of wheat varieties by Fusarium strains from 1990 to 1993

    -

    vaneeuwijk.nematodes

    -

    Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations.

    -

    vargas.txe.covs vargas.txe.yield

    -

    Treatment x environment interaction in agronomy trials

    -

    vargas.wheat1.covs vargas.wheat1.traits

    -

    Wheat yields in 7 years with genetic and environment covariates

    -

    vargas.wheat2.covs vargas.wheat2.yield

    -

    Multi-environment trial of wheat with environmental covariates

    -

    verbyla.lupin

    -

    Multi-environment trial of lupin, multiple varieties and densities

    -

    vold.longterm

    -

    Long-term barley yields at different fertilizer levels

    -

    vsn.lupin3

    -

    Multi-environment trial of lupin, early generation trial

    -

    wallace.iowaland

    -

    Iowa farmland values by county in 1925

    -

    walsh.cottonprice

    -

    Acres and price of cotton 1910-1943

    -

    wassom.brome.uniformity

    -

    Uniformity trials of bromegrass

    -

    waynick.soil

    -

    Soil nitrogen and carbon in two fields

    -

    wedderburn.barley

    -

    Multi-environment trial of barley, percent of leaves affected by leaf blotch

    -

    weiss.incblock

    -

    Soybean balanced incomplete block experiment

    -

    weiss.lattice

    -

    Lattice experiment in soybeans.

    -

    welch.bermudagrass

    -

    Factorial experiment of bermuda grass, N, P, K fertilizers

    -

    wheatley.carrot

    -

    Insecticide treatments for carrot fly larvae

    -

    wiebe.wheat.uniformity

    -

    Uniformity trial of wheat

    -

    wiedemann.safflower.uniformity

    -

    Uniformity trial of safflower

    -

    williams.barley.uniformity

    -

    Uniformity trial of barley

    -

    williams.cotton.uniformity

    -

    Uniformity trial of cotton

    -

    williams.trees

    -

    Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand

    -

    woodman.pig

    -

    Weight gain in pigs for different treatments

    -

    wyatt.multi.uniformity

    -

    Uniformity trial of oats and wheat on the same ground.

    -

    yan.winterwheat

    -

    Multi-environment trial of winter wheat in Ontario

    -

    yang.barley

    -

    Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta.

    -

    yates.missing

    -

    Factorial experiment with missing values

    -

    yates.oats

    -

    Split-plot experiment of oats

    -

    zuidhof.broiler

    -

    Daily weight, feed, egg measurements for a broiler chicken

    - - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/ivins.herbs-1.png b/docs/reference/ivins.herbs-1.png deleted file mode 100644 index c292c1e6..00000000 Binary files a/docs/reference/ivins.herbs-1.png and /dev/null differ diff --git a/docs/reference/ivins.herbs.html b/docs/reference/ivins.herbs.html deleted file mode 100644 index 14146620..00000000 --- a/docs/reference/ivins.herbs.html +++ /dev/null @@ -1,278 +0,0 @@ - - - - - - - - -Percent ground cover of herbage species and nettles. — ivins.herbs • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Percent ground cover of herbage species and nettles.

    - - -
    - -
    -

    Percent ground cover of herbage species and nettles.

    -
    - - - -

    Format

    - -

    A data frame with 78 observations on the following 4 variables.

    -
    block

    block, 6 levels

    -
    gen

    genotype, 13 levels

    -
    nettle

    percent ground cover of nettles

    -
    herb

    percent ground cover of herbage species

    - -
    - -

    Details

    - -

    On the University of Nottingham farm, 13 different strains and species - of herbage plants were sown on about 4 acres in an RCB design. Each - grass species was sown together with white clover seed.

    -

    During establishment of the herbage plants, it became apparent that - Urtica dioica (nettle) became established according to the - particular herbage plant in each plot. In particular, nettle became - established in plots sown with leguminous species and the two grass - species. The graminaceous plots had less nettles.

    -

    The data here are the percentage ground cover of nettle and herbage - plants in September 1951.

    -

    Note, some of the percent ground cover amounts were originally - reported as 'trace'. These have been arbitrarily set to 0.1 in this - data.

    -

    - - - - - - - - - - - - - - -
    genspeciesstrain
    G01Lolium perenneIrish perennial ryegrass
    G02Lolium perenneS. 23 perennial ryegrass
    G03Dactylis glomerataDanish cocksfoot
    G04Dactylis glomerataS. 143 cocksfoot
    G05Phleum pratenseAmerican timothy
    G06Phleum pratenseS. 48 timothy
    G07Festuca pratensisS. 215 meadow fescue
    G08Poa trivialisDanish rough stalked meadow grass
    G09Cynosurus cristatusNew Zealand crested dogstail
    G10Trifolium pratenseMontgomery late red clover
    G11Medicago lupulinaCommercial black medick
    G12Trifolium repensS. 100 white clover
    G13Plantago lanceolataCommercial ribwort plantain
    - - -

    Source

    - -

    Ivins, JD. (1952). - Concerning the Ecology of Urtica Dioica L., - Journal of Ecology, 40, 380-382. - https://doi.org/10.2307/2256806

    -

    References

    - -

    Ivins, JD (1950). Weeds in relation to the establishment of the Ley. - Grass and Forage Science, 5, 237--242. - https://doi.org/10.1111/j.1365-2494.1950.tb01287.x

    -

    O'Gorman, T.W. (2001). A comparison of the F-test, Friedman's test, - and several aligned rank tests for the analysis of randomized complete - blocks. - Journal of agricultural, biological, and environmental - statistics, 6, 367--378. - https://doi.org/10.1198/108571101317096578

    - -

    Examples

    - 
    
-library(agridat)
-
-data(ivins.herbs)
-dat <- ivins.herbs
-
-# Nettle is primarily established in legumes.
-libs(lattice)
-xyplot(herb~nettle|gen, dat, main="ivins.herbs - herb yield vs weeds",
-       xlab="Percent groundcover in nettles",
-       ylab="Percent groundcover in herbs")
-
    
-# O'Brien used first 7 species to test gen differences
-dat7 <- droplevels(subset(dat, is.element(gen, c('G01','G02','G03','G04','G05','G06','G07'))))
-m1 <- lm(herb ~ gen + block, data=dat7)
-anova(m1) # gen p-value is .041
-
    #> Analysis of Variance Table
-#> 
-#> Response: herb
-#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
-#> gen        6 1083.24 180.540  2.5518 0.04072 *
-#> block      5  590.69 118.138  1.6698 0.17236  
-#> Residuals 30 2122.48  70.749                  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Response: herb
-##           Df  Sum Sq Mean Sq F value  Pr(>F)
-## gen        6 1083.24 180.540  2.5518 0.04072 *
-## block      5  590.69 118.138  1.6698 0.17236
-## Residuals 30 2122.48  70.749
-
-friedman.test(herb ~ gen|block, dat7) # gen p-value .056
-
    #> 
-#> 	Friedman rank sum test
-#> 
-#> data:  herb and gen and block
-#> Friedman chi-squared = 12.286, df = 6, p-value = 0.05589
-#> 
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/iyer.wheat.uniformity-1.png b/docs/reference/iyer.wheat.uniformity-1.png deleted file mode 100644 index 5b6b63bf..00000000 Binary files a/docs/reference/iyer.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/iyer.wheat.uniformity.html b/docs/reference/iyer.wheat.uniformity.html deleted file mode 100644 index 11721c8c..00000000 --- a/docs/reference/iyer.wheat.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trials of wheat in India — iyer.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of wheat in India

    - - -
    - -
    -

    Uniformity trials of wheat in India.

    -
    - - 
    data("iyer.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 2000 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, ounces per plot

    - -
    - -

    Details

    - -

    Data collected at the Agricultural Sub-station in Karnal, India, in - April 1978. A net area of 400 ft x 125 ft was harvested by dividing it - into 80x25 units 5 ft x 5 ft after eliminating a minimum border of 3.5 - ft all around the net area.

    -

    Field width: 80 plots * 5 feet = 400 feet

    -

    Field length: 25 rows * 5 feet = 125 feet

    -

    In a second paper, Iyer used this data to compare random vs. balanced - arrangements of treatments to plots, with the conclusion that - "it is very difficult to say which [method] is better. However, there is - some tendency for the randomized arrangements to give more accurate results."

    -

    Source

    - -

    P. V. Krishna Iyer (1942). - Studies with wheat uniformity trial data. I. Size and shape of experimental plots and the relative efficiency of different layouts. - The Indian Journal of Agricultural Science, 12, 240-262. Page 259-262. - https://archive.org/stream/in.ernet.dli.2015.7638/2015.7638.The-Indian-Journal-Of-Agricultural-Science-Vol-xii-1942#page/n267/mode/2up

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(iyer.wheat.uniformity)
-dat <- iyer.wheat.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        main="iyer.wheat.uniformity", tick=TRUE, 
-        aspect=(25*5)/(80*5)) # true aspect
-
    
-# not exactly the same as Iyer table 1, p. 241
-var(subset(dat, col <= 20)$yield)
-
    #> [1] 8.808361
    var(subset(dat, col > 20 & col <= 40)$yield)
-
    #> [1] 7.884428
    var(subset(dat, col > 40 & col <= 60)$yield)
-
    #> [1] 8.613927
    var(subset(dat, col > 60)$yield)
-
    #> [1] 9.677526
      
-# cv for 1x1 whole-field
-# sd(dat$yield)/mean(dat$yield)
-# 18.3
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jansen.apple-1.png b/docs/reference/jansen.apple-1.png deleted file mode 100644 index 66239c8a..00000000 Binary files a/docs/reference/jansen.apple-1.png and /dev/null differ diff --git a/docs/reference/jansen.apple.html b/docs/reference/jansen.apple.html deleted file mode 100644 index 81c74575..00000000 --- a/docs/reference/jansen.apple.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Infestation of apple shoots by apple canker. — jansen.apple • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Infestation of apple shoots by apple canker.

    - - -
    - -
    -

    Infestation of apple shoots by apple canker.

    -
    - - 
    data("jansen.apple")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 5 variables.

    -
    inoculum

    inoculum level

    -
    gen

    genotype/variety

    -
    block

    block

    -
    y

    number of inoculations developing canker

    -
    n

    number of inoculations

    - -
    - -

    Details

    - -

    Shoots of apple trees were infected with fungus Nectria - galligena, which may cause apple canker.

    -

    The incoulum density treatment had 3 levels, measured in macroconidia - per ml.

    -

    There were 4 blocks.

    - -

    Used with permission of J. Jansen. Electronic version supplied by Miroslav Zoric.

    -

    Source

    - -

    J. Jansen & J.A. Hoekstra (1993). - The analysis of proportions in agricultural experiments by a - generalized linear mixed model. - Statistica Neerlandica, 47(3), 161-174. - https://doi.org/10.1111/j.1467-9574.1993.tb01414.x

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(jansen.apple)
-dat <- jansen.apple
-
-libs(lattice)
-xyplot(inoculum ~ y/n|gen, data=dat, group=block,
-       layout=c(3,1),
-       main="jansen.apple",
-       xlab="Proportion infected per block/inoculum",
-       ylab="Inoculum level")
-
    
-## libs(lme4)
-## # Tentative model.  Needs improvement.
-## m1 <- glmer(cbind(y,n-y) ~ gen + factor(inoculum) + (1|block),
-##             data=dat, family=binomial)
-## summary(m1)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jansen.carrot-1.png b/docs/reference/jansen.carrot-1.png deleted file mode 100644 index 7581c24b..00000000 Binary files a/docs/reference/jansen.carrot-1.png and /dev/null differ diff --git a/docs/reference/jansen.carrot.html b/docs/reference/jansen.carrot.html deleted file mode 100644 index ec67bd3e..00000000 --- a/docs/reference/jansen.carrot.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Infestation of carrots by fly larvae — jansen.carrot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Infestation of carrots by fly larvae

    - - -
    - -
    -

    Infestation of 16 carrot genotypes by fly larvae, comparing 2 - treatments in 16 blocks.

    -
    - - 
    data("jansen.carrot")
    - - -

    Format

    - -

    A data frame with 96 observations on the following 5 variables.

    -
    trt

    treatment

    -
    gen

    genotype

    -
    block

    block

    -
    n

    number of carrots sampled per plot

    -
    y

    number of carrots infested per plot

    - -
    - -

    Details

    - - - -

    This experiment was designed to compare different genotypes of carrots - with respect to their resistance to infestation by larvae of the - carrotfly.

    -

    There were 16 genotypes, 2 levels of pest-control treatments, conducted - in 3 randomized complete blocks. About 50 carrots were sampled from - each plot and evaluated. The data show the number of carrots and the - number infested by fly larvae.

    -

    Used with permission of J. Jansen. Electronic version supplied by Miroslav Zoric.

    -

    Source

    - -

    J. Jansen & J.A. Hoekstra (1993). - The analysis of proportions in agricultural experiments by a - generalized linear mixed model. - Statistica Neerlandica, 47(3), 161-174. - https://doi.org/10.1111/j.1467-9574.1993.tb01414.x

    -

    References

    - -

    None.

    - -

    Examples

    - 
    
-library(agridat)
-data(jansen.carrot)
-dat <- jansen.carrot
-
-libs(lattice)
-dotplot(gen ~ y/n, data=dat, group=trt, auto.key=TRUE,
-        main="jansen.carrot",
-        xlab="Proportion of carrots infected per block", ylab="Genotype")
-
    
-# \dontrun{
-  # Not run because CRAN wants < 5 seconds per example.  This is close.
-  libs(lme4)
-  # Tentative model.  Needs improvement.
-  m1 <- glmer(cbind(y,n-y) ~ gen*trt + (1|block),
-              data=dat, family=binomial)
-  summary(m1)
-
    #> Generalized linear mixed model fit by maximum likelihood (Laplace
-#>   Approximation) [glmerMod]
-#>  Family: binomial  ( logit )
-#> Formula: cbind(y, n - y) ~ gen * trt + (1 | block)
-#>    Data: dat
-#> 
-#>      AIC      BIC   logLik deviance df.resid 
-#>    608.9    693.5   -271.4    542.9       63 
-#> 
-#> Scaled residuals: 
-#>    Min     1Q Median     3Q    Max 
-#> -3.973 -1.016 -0.273  1.115  2.908 
-#> 
-#> Random effects:
-#>  Groups Name        Variance Std.Dev.
-#>  block  (Intercept) 0.02472  0.1572  
-#> Number of obs: 96, groups:  block, 3
-#> 
-#> Fixed effects:
-#>              Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)   1.07446    0.20722   5.185 2.16e-07 ***
-#> genG02       -0.06075    0.26611  -0.228  0.81941    
-#> genG03       -2.07415    0.26296  -7.888 3.08e-15 ***
-#> genG04       -2.79945    0.30159  -9.282  < 2e-16 ***
-#> genG05       -2.09256    0.26415  -7.922 2.34e-15 ***
-#> genG06       -2.58980    0.28306  -9.149  < 2e-16 ***
-#> genG07       -2.11827    0.26541  -7.981 1.45e-15 ***
-#> genG08       -2.52386    0.28125  -8.974  < 2e-16 ***
-#> genG09       -2.82317    0.29709  -9.503  < 2e-16 ***
-#> genG10       -2.63026    0.29208  -9.005  < 2e-16 ***
-#> genG11       -2.81654    0.30587  -9.208  < 2e-16 ***
-#> genG12       -4.42573    0.49179  -8.999  < 2e-16 ***
-#> genG13       -2.01259    0.27119  -7.421 1.16e-13 ***
-#> genG14       -3.09043    0.30759 -10.047  < 2e-16 ***
-#> genG15       -2.88159    0.29641  -9.722  < 2e-16 ***
-#> genG16       -3.79170    0.39154  -9.684  < 2e-16 ***
-#> trtT2        -1.88405    0.25129  -7.498 6.50e-14 ***
-#> genG02:trtT2  0.18457    0.36089   0.511  0.60905    
-#> genG03:trtT2  1.15294    0.38887   2.965  0.00303 ** 
-#> genG04:trtT2  2.20759    0.39970   5.523 3.33e-08 ***
-#> genG05:trtT2  1.05088    0.39904   2.634  0.00845 ** 
-#> genG06:trtT2  0.91731    0.44606   2.056  0.03973 *  
-#> genG07:trtT2  0.43275    0.43498   0.995  0.31980    
-#> genG08:trtT2  1.22717    0.42157   2.911  0.00360 ** 
-#> genG09:trtT2  1.29446    0.44172   2.931  0.00338 ** 
-#> genG10:trtT2  1.06684    0.44495   2.398  0.01650 *  
-#> genG11:trtT2  1.27707    0.46195   2.765  0.00570 ** 
-#> genG12:trtT2  1.89552    0.69089   2.744  0.00608 ** 
-#> genG13:trtT2  0.89074    0.40707   2.188  0.02866 *  
-#> genG14:trtT2  2.14280    0.42006   5.101 3.38e-07 ***
-#> genG15:trtT2  1.81864    0.41727   4.358 1.31e-05 ***
-#> genG16:trtT2  1.79019    0.56060   3.193  0.00141 ** 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #> 
-#> Correlation matrix not shown by default, as p = 32 > 12.
-#> Use print(x, correlation=TRUE)  or
-#>     vcov(x)        if you need it
      # Todo: Why are these results different from Jansen?
-  # Maybe he used ungrouped bernoulli data?  Too slow with 4700 obs  
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jansen.strawberry-1.png b/docs/reference/jansen.strawberry-1.png deleted file mode 100644 index e3478e15..00000000 Binary files a/docs/reference/jansen.strawberry-1.png and /dev/null differ diff --git a/docs/reference/jansen.strawberry.html b/docs/reference/jansen.strawberry.html deleted file mode 100644 index fcf72515..00000000 --- a/docs/reference/jansen.strawberry.html +++ /dev/null @@ -1,256 +0,0 @@ - - - - - - - - -Ordered disease ratings of strawberry crosses. — jansen.strawberry • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Ordered disease ratings of strawberry crosses.

    - - -
    - -
    -

    Ordered disease ratings of strawberry crosses.

    -
    - - 
    data("jansen.strawberry")
    - - -

    Format

    - -

    A data frame with 144 observations on the following 5 variables.

    -
    male

    male parent

    -
    female

    female parent

    -
    block

    block

    -
    category

    disease damage, C1 < C2 < C3

    -
    count

    number of plants in each category

    - -
    - -

    Details

    - - - -

    In strawberries, red core disease is caused by a fungus, - Phytophtora fragariae. This experiment evaluated different - populations for damage caused by red core disease.

    -

    There were 3 male strawberry plants and 4 DIFFERENT female strawberry - plants that were crossed to create 12 populations. - Note: Jansen labeled the male parents 1,2,3 and the female parents - 1,2,3,4. To reduce confusion, this data labels the female parents 5,6,7,8.

    -

    The experiment had four blocks with 12 plots each (one for each - population). Plots usually had 10 plants, but some plots only had 9 - plants. Each plant was assessed for damage from fungus and rated as - belonging to category C1, C2, or C3 (increasing damage).

    - -

    Used with permission of Hans Jansen.

    -

    Source

    - -

    J. Jansen, 1990. - On the statistical analysis of ordinal data when extravariation is present. - Applied Statistics, 39, 75-84, Table 1. - https://doi.org/10.2307/2347813

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(jansen.strawberry)
-dat <- jansen.strawberry
-dat <- transform(dat, category=ordered(category, levels=c('C1','C2','C3')))
-
-dtab <- xtabs(count ~ male + female + category, data=dat)
-ftable(dtab)
-
    #>             category C1 C2 C3
-#> male female                  
-#> P1   P5               6 13 20
-#>      P6               8 15 17
-#>      P7              13 10 16
-#>      P8               8 21 11
-#> P2   P5               5 13 21
-#>      P6               9 16 14
-#>      P7              16  9 15
-#>      P8              12 13 14
-#> P3   P5               5 10 24
-#>      P6              13 12 15
-#>      P7               3 14 22
-#>      P8              18  9 13
    
-mosaicplot(dtab,
-           color=c("lemonchiffon1","lightsalmon1","indianred"),
-           main="jansen.strawberry disease ratings",
-           xlab="Male parent", ylab="Female parent")
-
    
-libs(MASS,vcd)
-
    #> 
-#> Attaching package: 'vcd'
    #> The following object is masked from 'package:HH':
-#> 
-#>     odds
    #> The following object is masked from 'package:latticeExtra':
-#> 
-#>     rootogram
    # Friendly suggests a minimal model is [MF][C]
-# m1 <- loglm( ~ 1*2 + 3, dtab) # Fails, only with devtools
-# mosaic(m1)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jayaraman.bamboo.html b/docs/reference/jayaraman.bamboo.html deleted file mode 100644 index a2f3aadd..00000000 --- a/docs/reference/jayaraman.bamboo.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Bamboo progeny trial — jayaraman.bamboo • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Bamboo progeny trial

    - - -
    - -
    -

    Bamboo progeny trial in 2 locations, 3 blocks

    -
    - - 
    data("jayaraman.bamboo")
    - - -

    Format

    - -

    A data frame with 216 observations on the following 5 variables.

    -
    loc

    location factor

    -
    block

    block factor

    -
    tree

    tree factor

    -
    family

    family factor

    -
    height

    height, cm

    - -
    - -

    Details

    - -

    Data from a replicated trial of bamboo at two locations in Kerala, - India. Each location had 3 blocks. In each block were 6 families, - with 6 trees in each family.

    -

    Source

    - -

    K. Jayaraman (1999). "A Statistical Manual For Forestry Research". - Forestry Research Support Programme for Asia and the Pacific. Page 170.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  data(jayaraman.bamboo)
-
    #> Warning: data set 'jayaraman.bamboo' not found
      dat <- jayaraman.bamboo
-
    #> Error in eval(expr, envir, enclos): object 'jayaraman.bamboo' not found
    
-  # very surprising differences between locations
-  libs(lattice)
-  bwplot(height ~ family|loc, d2, main="jayaraman.bamboo")
-
    #> Error in eval(substitute(groups), data, environment(formula)): object 'd2' not found
      # match Jayarman's anova table 6.3, page 173
-  # m1 <- aov(height ~ loc+loc:block + family + family:loc + family:loc:block, data=d2)
-  # anova(m1)
-
-  # more modern approach with mixed model, match variance components needed
-  # for equation 6.9, heritability of the half-sib averages as
-  m2 <- lme4::lmer(height ~ 1 + (1|loc/block) + (1|family/loc/block), data=d2)
-
    #> Error: 'data' not found, and some variables missing from formula environment
      lucid::vc(m2)
-
    #> Error in lucid::vc(m2): object 'm2' not found
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jenkyn.mildew-1.png b/docs/reference/jenkyn.mildew-1.png deleted file mode 100644 index 20d689fd..00000000 Binary files a/docs/reference/jenkyn.mildew-1.png and /dev/null differ diff --git a/docs/reference/jenkyn.mildew-2.png b/docs/reference/jenkyn.mildew-2.png deleted file mode 100644 index 20614f15..00000000 Binary files a/docs/reference/jenkyn.mildew-2.png and /dev/null differ diff --git a/docs/reference/jenkyn.mildew-3.png b/docs/reference/jenkyn.mildew-3.png deleted file mode 100644 index 16fca179..00000000 Binary files a/docs/reference/jenkyn.mildew-3.png and /dev/null differ diff --git a/docs/reference/jenkyn.mildew.html b/docs/reference/jenkyn.mildew.html deleted file mode 100644 index 95dabd80..00000000 --- a/docs/reference/jenkyn.mildew.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Yields from treatment for mildew control — jenkyn.mildew • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Yields from treatment for mildew control

    - - -
    - -
    -

    Yields from treatment for mildew control

    -
    - - - -

    Format

    - -

    A data frame with 38 observations on the following 4 variables.

    -
    plot

    plot number

    -
    trt

    treatment factor, 4 levels

    -
    block

    block factor, 9 levels

    -
    yield

    grain yield, tons/ha

    - -
    - -

    Details

    - - - -

    There were four spray treatments: 0 (none), 1 (early), 2 (late), R - (repeated).

    -

    Each treatment occurs once between each of the 9 ordered pairs of the - other treatments.

    -

    The first and last plot are not assigned to a block.

    -

    Source

    - -

    Norman Draper and Irwin Guttman (1980). - Incorporating Overlap Effects from Neighboring Units into Response - Surface Models. - Appl Statist, 29, 128--134. - https://doi.org/10.2307/2986297

    -

    References

    - -

    Maria Durban, Christine Hackett, Iain Currie. - Blocks, Trend and Interference in Field Trials.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(jenkyn.mildew)
-dat <- jenkyn.mildew
-
-libs(lattice)
-bwplot(yield ~ trt, dat, main="jenkyn.mildew", xlab="Treatment")
-
    
-# Residuals from treatment model show obvious spatial trends
-m0 <- lm(yield ~ trt, dat)
-xyplot(resid(m0)~plot, dat, ylab="Residual",
-       main="jenkyn.mildew - treatment model")
-
    
-# The blocks explain most of the variation
-m1 <- lm(yield ~ trt + block, dat)
-xyplot(resid(m1)~plot, dat, ylab="Residual",
-       main="jenkyn.mildew - block model")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/john.alpha-1.png b/docs/reference/john.alpha-1.png deleted file mode 100644 index cdaeef68..00000000 Binary files a/docs/reference/john.alpha-1.png and /dev/null differ diff --git a/docs/reference/john.alpha.html b/docs/reference/john.alpha.html deleted file mode 100644 index b707ba83..00000000 --- a/docs/reference/john.alpha.html +++ /dev/null @@ -1,381 +0,0 @@ - -Alpha lattice design of spring oats — john.alpha • agridat - - -
    -
    - - - -
    -
    -
    -

    Alpha lattice design of spring oats

    - - -
    - -
    -

    Alpha lattice design of spring oats

    -
    - - -
    -

    Format

    -

    A data frame with 72 observations on the following 5 variables.

    plot
    -

    plot number

    - -
    rep
    -

    replicate

    - -
    block
    -

    incomplete block

    - -
    gen
    -

    genotype (variety)

    - -
    yield
    -

    dry matter yield (tonnes/ha)

    - -
    row
    -

    Row ordinate

    - -
    col
    -

    Column ordinate

    - - -
    -
    -

    Details

    - - -

    A spring oats trial grown in Craibstone, near Aberdeen. - There were 24 varieties in 3 replicates, each consisting - of 6 incomplete blocks of 4 plots. Planted in a resolvable - alpha design.

    -

    Caution: Note that the table on page 146 of John & Williams (1995) is - NOT the physical layout. The plots were laid out in a single line.

    -
    -
    -

    Source

    -

    J. A. John & E. R. Williams (1995). - Cyclic and computer generated designs. - Chapman and Hall, London. Page 146.

    -
    -
    -

    References

    -

    Piepho, H.P. and Mohring, J. (2007), - Computing heritability and selection response from unbalanced - plant breeding trials. - Genetics, 177, 1881-1888. - https://doi.org/10.1534/genetics.107.074229

    -

    Paul Schmidt, Jens Hartung, JĂśrn Bennewitz, and Hans-Peter Piepho - (2019). - Heritability in Plant Breeding on a Genotype-Difference Basis. - Genetics, 212, 991-1008. - https://doi.org/10.1534/genetics.119.302134

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(john.alpha)
-  dat <- john.alpha
-  
-  # RCB (no incomplete block)
-  m0 <- lm(yield ~ 0 + gen + rep, data=dat)
-
-  # Block fixed (intra-block analysis) (bottom of table 7.4 in John)
-  m1 <- lm(yield ~ 0 + gen + rep + rep:block, dat)
-  anova(m1)
-#> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df  Sum Sq Mean Sq  F value    Pr(>F)    
-#> gen       24 1458.83  60.785 728.2829 < 2.2e-16 ***
-#> rep        2    6.14   3.068  36.7557 6.593e-09 ***
-#> rep:block 15    3.60   0.240   2.8784  0.006255 ** 
-#> Residuals 31    2.59   0.083                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-  # Block random (combined inter-intra block analysis)
-  libs(lme4, lucid)
-  m2 <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat)
-
-  anova(m2)
-#> Analysis of Variance Table
-#>     npar Sum Sq Mean Sq  F value
-#> gen   24 380.44 15.8515 185.9959
-#> rep    2   1.57  0.7851   9.2124
-  ## Analysis of Variance Table
-  ##     Df Sum Sq Mean Sq  F value
-  ## gen 24 380.43 15.8513 185.9942
-  ## rep  2   1.57  0.7851   9.2123
-  vc(m2)
-#>        grp        var1 var2    vcov  sdcor
-#>  rep:block (Intercept) <NA> 0.06194 0.2489
-#>   Residual        <NA> <NA> 0.08523 0.2919
-  ##        grp        var1 var2    vcov  sdcor
-  ##  rep:block (Intercept) <NA> 0.06194 0.2489
-  ##   Residual        <NA> <NA> 0.08523 0.2919
-
-
-  # Variety means.  John and Williams table 7.5.  Slight, constant
-  # difference for each method as compared to John and Williams.
-  means <- data.frame(rcb=coef(m0)[1:24],
-                      ib=coef(m1)[1:24],
-                      intra=fixef(m2)[1:24])
-  head(means)
-#>             rcb       ib    intra
-#> genG01 5.201233 5.268742 5.146433
-#> genG02 4.552933 4.665389 4.517265
-#> genG03 3.381800 3.803790 3.537933
-#> genG04 4.439400 4.728175 4.528828
-#> genG05 5.103100 5.225708 5.075944
-#> genG06 4.749067 4.618234 4.575395
-  ##             rcb       ib    intra
-  ## genG01 5.201233 5.268742 5.146433
-  ## genG02 4.552933 4.665389 4.517265
-  ## genG03 3.381800 3.803790 3.537934
-  ## genG04 4.439400 4.728175 4.528828
-  ## genG05 5.103100 5.225708 5.075944
-  ## genG06 4.749067 4.618234 4.575394
-  
-  libs(lattice)
-  splom(means, main="john.alpha - means for RCB, IB, Intra-block")
-
-  
-
-  # ----------
-  # asreml4
-
-  libs(asreml,lucid)
-
-  # Heritability calculation of Piepho & Mohring, Example 1
-
-  m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:39 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       7.34791      0.221592     69 15:17:39    0.0
-#>  2      12.08376      0.156976     69 15:17:39    0.0
-#>  3      15.53184      0.110746     69 15:17:39    0.0
-#>  4      16.65758      0.089647     69 15:17:39    0.0
-#>  5      16.80904      0.081946     69 15:17:39    0.0
-#>  6      16.80985      0.081598     69 15:17:39    0.0
-  sg2 <- summary(m3)$varcomp['gen','component'] # .142902
-  
-  # Average variance of a difference of two adjusted means (BLUP)
-  
-  p3 <- predict(m3, data=dat, classify="gen", sed=TRUE)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:39 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       16.8098     0.0816175     69 15:17:39    0.0
-#>  2       16.8098     0.0816174     69 15:17:39    0.0
-#>  3       16.8098     0.0816172     69 15:17:39    0.0
-  # Matrix of pair-wise SED values, squared
-  vdiff <- p3$sed^2
-  # Average variance of two DIFFERENT means (using lower triangular of vdiff)
-  vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038
-  
-  # Note that without sed=TRUE, asreml reports square root of the average variance
-  # of a difference between the variety means, so the following gives the same value
-  # predict(m3, data=dat, classify="gen")$avsed ^ 2 # .05455038
-  
-  # Average variance of a difference of two adjusted means (BLUE)
-  m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:39 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       8.27642      0.119546     46 15:17:39    0.0
-#>  2       8.94241      0.108572     46 15:17:39    0.0
-#>  3       9.53543      0.096936     46 15:17:39    0.0
-#>  4       9.80736      0.087555     46 15:17:39    0.0
-#>  5       9.82176      0.085478     46 15:17:39    0.0
-#>  6       9.82194      0.085243     46 15:17:39    0.0
-  p4 <- predict(m4, data=dat, classify="gen", sed=TRUE)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:39 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       9.82194     0.0852263     46 15:17:39    0.0
-#>  2       9.82194     0.0852260     46 15:17:39    0.0
-#>  3       9.82194     0.0852252     46 15:17:39    0.0
-  vdiff <- p4$sed^2
-  vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875
-  # Again, could use predict(m4, data=dat, classify="gen")$avsed ^ 2
-  
-  # H^2 Ad-hoc measure of heritability
-  sg2 / (sg2 + vblue/2) # .803
-#> [1] 0.8029759
-  
-  # H^2c Similar measure proposed by Cullis.
-  1-(vblup / (2*sg2)) # .809
-#> [1] 0.8090841
-
-
-  # ----------
-  # lme4 to calculate Cullis H2
-  # https://stackoverflow.com/questions/38697477
-  
-  libs(lme4)
-  
-  cov2sed <- function(x){
-    # Convert var-cov matrix to SED matrix
-    # sed[i,j] = sqrt( x[i,i] + x[j,j]- 2*x[i,j] )
-    n <- nrow(x)
-    vars <- diag(x)
-    sed <- sqrt( matrix(vars, n, n, byrow=TRUE) +
-                   matrix(vars, n, n, byrow=FALSE) - 2*x )
-    diag(sed) <- 0
-    return(sed)
-  }
-  
-  # Same as asreml model m4. Note 'gen' must be first term
-  m5blue <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat)
-  
-  libs(emmeans)
-#> Warning: package 'emmeans' was built under R version 4.1.2
-  ls5blue <- emmeans(m5blue, "gen")
-  con <- ls5blue@linfct[,1:24] # contrast matrix for genotypes
-  # The 'con' matrix is identity diagonal, so we don't need to multiply,
-  # but do so for a generic approach
-  # sed5blue <- cov2sed(con 
-  tmp <- tcrossprod( crossprod(t(con), vcov(m5blue)[1:24,1:24]), con)
-  sed5blue <- cov2sed(tmp)
-
-  
-  # vblue Average variance of difference between genotypes
-  vblue <- mean(sed5blue[upper.tri(sed5blue)]^2)
-  vblue # .07010875 matches 'vblue' from asreml
-#> [1] 0.07010875
-  
-  # Now blups
-  m5blup <- lmer(yield ~ 0 + (1|gen) + rep + (1|rep:block), dat)
-  # Need lme4::ranef in case ordinal is loaded
-  re5 <- lme4::ranef(m5blup,condVar=TRUE)
-  vv1 <- attr(re5$gen,"postVar")  
-  vblup <- 2*mean(vv1) # .0577 not exactly same as 'vblup' above
-  vblup
-#> [1] 0.0577334
-  
-  # H^2 Ad-hoc measure of heritability
-  sg2 <- c(lme4::VarCorr(m5blup)[["gen"]])  # 0.142902
-  sg2 / (sg2 + vblue/2) # .803 matches asreml
-#> [1] 0.8030173
-
-  # H^2c Similar measure proposed by Cullis.
-  1-(vblup / 2 / sg2) # .809 from asreml, .800 from lme4
-#> [1] 0.7979969
-
-  
-  # ----------
-  # Sommer to calculate Cullis H2
-  libs(sommer)
-  m2.ran <- mmer(fixed  = yield ~ rep,
-                 random =       ~ gen + rep:block,
-                 data   = dat)
-#> Version out of date. Please update sommer to the newest version using:
-#> install.packages('sommer') in a new session
-#>  Use the 'date.warning' argument to disable the warning message.iteration    LogLik     wall    cpu(sec)   restrained
-#>     1      -19.4174   15:17:39      0           0
-#>     2      -14.1597   15:17:39      0           0
-#>     3      -12.6451   15:17:39      0           0
-#>     4      -12.5508   15:17:39      0           0
-#>     5      -12.5506   15:17:39      0           0
-  
-  vc_g     <- m2.ran$sigma$gen       # genetic variance component
-  n_g      <- n_distinct(dat$gen)    # number of genotypes
-  C22_g    <- m2.ran$PevU$gen$yield  # Prediction error variance matrix for genotypic BLUPs
-  trC22_g  <- sum(diag(C22_g))       # trace
-  # Mean variance of a difference between genotypic BLUPs. Smith eqn 26
-  # I do not see the algebraic reason for this...2
-  av2 <- 2/n_g * (trC22_g - (sum(C22_g)-trC22_g) / (n_g-1))                      
-  
-  ### H2 Cullis
-  1-(av2 / (2 * vc_g)) #0.8091
-#>           yield
-#> yield 0.8091431
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/johnson.blight-1.png b/docs/reference/johnson.blight-1.png deleted file mode 100644 index 5ecd5be1..00000000 Binary files a/docs/reference/johnson.blight-1.png and /dev/null differ diff --git a/docs/reference/johnson.blight.html b/docs/reference/johnson.blight.html deleted file mode 100644 index 6bb3c2bc..00000000 --- a/docs/reference/johnson.blight.html +++ /dev/null @@ -1,308 +0,0 @@ - - - - - - - - -Potato blight due to weather in Prosser, Washington — johnson.blight • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Potato blight due to weather in Prosser, Washington

    - - -
    - -
    -

    Potato blight due to weather in Prosser, Washington

    -
    - - - -

    Format

    - -

    A data frame with 25 observations on the following 6 variables.

    -
    year

    year

    -
    area

    area affected, hectares

    -
    blight

    blight detected, 0/1 numeric

    -
    rain.am

    number of rainy days in April and May

    -
    rain.ja

    number of rainy days in July and August

    -
    precip.m

    precipitation in May when temp > 5C, milimeters

    - -
    - -

    Details

    - -

    The variable 'blight detected' is 1 if 'area' > 0.

    -

    Source

    - -

    Johnson, D.A. and Alldredge, J.R. and Vakoch, D.L. (1996). - Potato late blight forecasting models for the semiarid environment of - south-central Washington. Phytopathology, 86, 480--484. - https://doi.org/10.1094/Phyto-86-480

    -

    References

    - -

    Vinayanand Kandala, - Logistic Regression

    - -

    Examples

    - 
    
-library(agridat)
-
-data(johnson.blight)
-dat <- johnson.blight
-
-# Define indicator for blight in previous year
-dat$blight.prev[2:25] <- dat$blight[1:24]
-dat$blight.prev[1] <- 0 # Need this to match the results of Johnson
-dat$blight.prev <- factor(dat$blight.prev)
-dat$blight <- factor(dat$blight)
-
-# Johnson et al developed two logistic models to predict outbreak of blight
-
-m1 <- glm(blight ~ blight.prev + rain.am + rain.ja, data=dat, family=binomial)
-summary(m1)
-
    #> 
-#> Call:
-#> glm(formula = blight ~ blight.prev + rain.am + rain.ja, family = binomial, 
-#>     data = dat)
-#> 
-#> Deviance Residuals: 
-#>      Min        1Q    Median        3Q       Max  
-#> -2.42093  -0.24739  -0.04458   0.51326   1.30182  
-#> 
-#> Coefficients:
-#>              Estimate Std. Error z value Pr(>|z|)  
-#> (Intercept)  -11.4699     5.5976  -2.049   0.0405 *
-#> blight.prev1   3.8796     1.8066   2.148   0.0318 *
-#> rain.am        0.7162     0.3665   1.954   0.0507 .
-#> rain.ja        0.2587     0.2468   1.048   0.2945  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for binomial family taken to be 1)
-#> 
-#>     Null deviance: 34.617  on 24  degrees of freedom
-#> Residual deviance: 13.703  on 21  degrees of freedom
-#> AIC: 21.703
-#> 
-#> Number of Fisher Scoring iterations: 7
-#> 
    ##              Estimate Std. Error z value Pr(>|z|)
-## (Intercept)  -11.4699     5.5976  -2.049   0.0405 *
-## blight.prev1   3.8796     1.8066   2.148   0.0318 *
-## rain.am        0.7162     0.3665   1.954   0.0507 .
-## rain.ja        0.2587     0.2468   1.048   0.2945
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## (Dispersion parameter for binomial family taken to be 1)
-
-##     Null deviance: 34.617  on 24  degrees of freedom
-## Residual deviance: 13.703  on 21  degrees of freedom
-## AIC: 21.703
-
-
-
-m2 <- glm(blight ~ blight.prev + rain.am + precip.m, data=dat, family=binomial)
-summary(m2)
-
    #> 
-#> Call:
-#> glm(formula = blight ~ blight.prev + rain.am + precip.m, family = binomial, 
-#>     data = dat)
-#> 
-#> Deviance Residuals: 
-#>      Min        1Q    Median        3Q       Max  
-#> -1.87484  -0.32997  -0.06489   0.54059   1.94218  
-#> 
-#> Coefficients:
-#>              Estimate Std. Error z value Pr(>|z|)  
-#> (Intercept)   -7.5483     3.8070  -1.983   0.0474 *
-#> blight.prev1   3.5526     1.6061   2.212   0.0270 *
-#> rain.am        0.6290     0.2763   2.276   0.0228 *
-#> precip.m      -0.0904     0.1144  -0.790   0.4295  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for binomial family taken to be 1)
-#> 
-#>     Null deviance: 34.617  on 24  degrees of freedom
-#> Residual deviance: 14.078  on 21  degrees of freedom
-#> AIC: 22.078
-#> 
-#> Number of Fisher Scoring iterations: 6
-#> 
    ##              Estimate Std. Error z value Pr(>|z|)
-## (Intercept)   -7.5483     3.8070  -1.983   0.0474 *
-## blight.prev1   3.5526     1.6061   2.212   0.0270 *
-## rain.am        0.6290     0.2763   2.276   0.0228 *
-## precip.m      -0.0904     0.1144  -0.790   0.4295
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-## (Dispersion parameter for binomial family taken to be 1)
-
-##     Null deviance: 34.617  on 24  degrees of freedom
-## Residual deviance: 14.078  on 21  degrees of freedom
-## AIC: 22.078
-
-libs(lattice)
-splom(dat[,c('blight','rain.am','rain.ja','precip.m')],
-      main="johnson.blight - indicator of blight")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/johnson.douglasfir-1.png b/docs/reference/johnson.douglasfir-1.png deleted file mode 100644 index a80d3fd7..00000000 Binary files a/docs/reference/johnson.douglasfir-1.png and /dev/null differ diff --git a/docs/reference/johnson.douglasfir-2.png b/docs/reference/johnson.douglasfir-2.png deleted file mode 100644 index d1b2e551..00000000 Binary files a/docs/reference/johnson.douglasfir-2.png and /dev/null differ diff --git a/docs/reference/johnson.douglasfir.html b/docs/reference/johnson.douglasfir.html deleted file mode 100644 index 9c016e86..00000000 --- a/docs/reference/johnson.douglasfir.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    A study of small-plots of old-growth Douglas Fir in Oregon.

    - - -
    - -
    -

    A study of small-plots of old-growth Douglas Fir in Oregon.

    -
    - - 
    data("johnson.douglasfir")
    - - -

    Format

    - -

    A data frame with 1600 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    volume

    volume per plot

    - -
    - -

    Details

    - -

    A study in 40 acres of old-growth Douglas-Fir near Eugene, Oregon. The area was divided into a 40-by-40 grid of plots, each 1/40 acre. The volume represents the total timber volume (Scribner Decimal C) of each 1/40 acre plot.

    -

    The authors conclude a 1-chain by 3-chain 3/10 acre rectangle was most efficient for intensive cruise work.

    -

    To convert plot volume to total volume per acre, multiply by 40 (each plot is -1/40 acre) and multiply by 10 (correction for the Scribner scale).

    -

    Source

    - -

    Floyd A. Johnson, Homer J. Hixon. (1952). -The most efficient size and shape of plot to use for cruising in old-growth Douglas-fir timber. -Jour. Forestry 50, 17-20. -https://doi.org/10.1093/jof/50.1.17

    -

    References

    - -

    None

    - -

    Examples

    - 
    
-library(agridat)
-data(johnson.douglasfir)
-dat <- johnson.douglasfir
-
-# Average volume per acre. Johnson & Hixon give 91000.
-# Transcription may have some errors...the pdf was blurry.
-mean(dat$volume) * 400
-
    #> [1] 91124.25
    # 91124 
-
-# \dontrun{
-libs(lattice)
-levelplot(volume ~ col*row, dat, main="johnson.douglasfir", aspect=1)
-
    histogram( ~ volume, data=dat, main="johnson.douglasfir")
-
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/jones.corn.uniformity-1.png b/docs/reference/jones.corn.uniformity-1.png deleted file mode 100644 index 3a8d4b41..00000000 Binary files a/docs/reference/jones.corn.uniformity-1.png and /dev/null differ diff --git a/docs/reference/jones.corn.uniformity.html b/docs/reference/jones.corn.uniformity.html deleted file mode 100644 index 429a50cd..00000000 --- a/docs/reference/jones.corn.uniformity.html +++ /dev/null @@ -1,153 +0,0 @@ - -Uniformity trial of corn. — jones.corn.uniformity • agridat - - -
    -
    - - - -
    -
    -
    -

    Uniformity trial of corn.

    - - -
    - -
    -

    Uniformity trial of corn in Iowa in 2016.

    -
    - -
    - 
    data("jones.corn.uniformity")
    -
    - -
    -

    Format

    -

    A data frame with 144 observations on the following 3 variables.

    col
    -

    column ordinate

    - -
    row
    -

    row ordinate

    - -
    yield
    -

    yield, bu/ac

    - - -
    -
    -

    Details

    -

    This data corresponds to field "ISU.SE" in the paper by Jones.

    -

    Field width: 12 columns, 4.6 meters each.

    -

    Field length: 12 rows, 3 meters each.

    -

    Electronic version provided as an online supplement. The "row" and - "col" variables in the supplement have been swapped for the - presentation of the data here in order to be more consistent with the - figures in the paper.

    -

    Used with permission of Marcus Jones.

    -
    -
    -

    Source

    -

    Jones, M., Harbur, M., & Moore, K. J. (2021). - Automating Uniformity Trials to Optimize Precision of Agronomic Field - Trials. - Agronomy, 11(6), 1254. - https://doi.org/10.3390/agronomy11061254

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-library(agridat)
-data(jones.corn.uniformity)
-dat <- jones.corn.uniformity
-library(desplot)
-# Compare to figure 5 of Jones et al.
-desplot(dat, yield ~ col*row,
-        aspect=(12*4.6)/(12*3),
-        main="jones.corn.uniformity")
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/kadam.millet.uniformity-1.png b/docs/reference/kadam.millet.uniformity-1.png deleted file mode 100644 index aaa5b0cc..00000000 Binary files a/docs/reference/kadam.millet.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kadam.millet.uniformity-2.png b/docs/reference/kadam.millet.uniformity-2.png deleted file mode 100644 index ee9f06c4..00000000 Binary files a/docs/reference/kadam.millet.uniformity-2.png and /dev/null differ diff --git a/docs/reference/kadam.millet.uniformity.html b/docs/reference/kadam.millet.uniformity.html deleted file mode 100644 index eef5b63a..00000000 --- a/docs/reference/kadam.millet.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trial of millet — kadam.millet.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of millet

    - - -
    - -
    -

    Uniformity trial of millet in India during 2 years

    -
    - - 
    data("kadam.millet.uniformity")
    - - -

    Format

    - -

    A data frame with 240 observations on the following 4 variables.

    -
    year

    year

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, ounces

    - -
    - -

    Details

    - - - -

    Uniformity trials conducted during the kharip (monsoon) seasons of - 1933 and 1934 at Kundewadi, Niphad, in the district of Nasik, - India. Bajari (pearl millet) strain 54 was used.

    -

    In 1933:

    -

    Field width: 8 plots * 16.5 feet

    -

    Field length: 10 plots * 33 feet

    -

    In 1934:

    -

    Field width: 8 plots * 16.5 feet

    -

    Field length: 20 plots * 16.5 feet

    -

    Source

    - -

    B. S. Kadam and S. M. Patel. (1937). - Studies in Field-Plot Technique With P. Typhoideum Rich. - The Empire Journal Of Experimental Agriculture, 5, 219-230. - https://archive.org/details/in.ernet.dli.2015.25282

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(kadam.millet.uniformity)
-  dat <- kadam.millet.uniformity
-  
-  # similar to Kadam fig 1
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          subset=year==1933,
-          flip=TRUE, aspect=(10*33)/(8*16.5), # true aspect
-          main="kadam.millet.uniformity 1933")
-
    
-  desplot(dat, yield ~ col*row,
-          subset=year==1934,
-          flip=TRUE, aspect=(20*16.5)/(8*16.5), # true aspect
-          main="kadam.millet.uniformity 1934")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kalamkar.potato.uniformity-1.png b/docs/reference/kalamkar.potato.uniformity-1.png deleted file mode 100644 index 362407d7..00000000 Binary files a/docs/reference/kalamkar.potato.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kalamkar.potato.uniformity.html b/docs/reference/kalamkar.potato.uniformity.html deleted file mode 100644 index 17164a4d..00000000 --- a/docs/reference/kalamkar.potato.uniformity.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - - -Uniformity trial of potatoes — kalamkar.potato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of potatoes

    - - -
    - -
    -

    Uniformity trial of potatoes at Saskatchewan, Canada, 1929.

    -
    - - 
    data("kalamkar.potato.uniformity")
    - - -

    Format

    - -

    A data frame with 576 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield of potatoes per plot

    - -
    - -

    Details

    - -

    The data is for potato yields in 96 rows, each 132 feet long, with 3 - feet between rows.

    -

    Each row was harvested as six plots, each 22 feet long. Each hill had - one seed piece. Hills were spaced 2 feet apart in each row.

    -

    Field width: 6 plots * 22 feet = 132 feet

    -

    Field length: 96 rows * 3 feet = 288 feet

    -

    Source

    - -

    Kalamkar, R.J. (1932). - Experimental Error and the Field-Plot Technique with Potatoes. - The Journal of Agricultural Science, 22, 373-385. - https://doi.org/10.1017/S0021859600053697

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(kalamkar.potato.uniformity)
-dat <- kalamkar.potato.uniformity
-
-# Similar to figure 1 of Kalamkar
-libs(desplot)
-desplot(dat, yield~col*row,
-        flip=TRUE, tick=TRUE, aspect=288/132, # true aspect
-        main="kalamkar.potato.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kalamkar.wheat.uniformity-1.png b/docs/reference/kalamkar.wheat.uniformity-1.png deleted file mode 100644 index 72671f43..00000000 Binary files a/docs/reference/kalamkar.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kalamkar.wheat.uniformity-2.png b/docs/reference/kalamkar.wheat.uniformity-2.png deleted file mode 100644 index 477a96e4..00000000 Binary files a/docs/reference/kalamkar.wheat.uniformity-2.png and /dev/null differ diff --git a/docs/reference/kalamkar.wheat.uniformity-3.png b/docs/reference/kalamkar.wheat.uniformity-3.png deleted file mode 100644 index 83f917f7..00000000 Binary files a/docs/reference/kalamkar.wheat.uniformity-3.png and /dev/null differ diff --git a/docs/reference/kalamkar.wheat.uniformity.html b/docs/reference/kalamkar.wheat.uniformity.html deleted file mode 100644 index df259409..00000000 --- a/docs/reference/kalamkar.wheat.uniformity.html +++ /dev/null @@ -1,270 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — kalamkar.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat at Rothamsted, UK in 1931.

    -
    - - 
    data("kalamkar.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 1280 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, grams/half-meter

    -
    ears

    ears per half-meter

    - -
    - -

    Details

    - - - -

    Kalamkar's paper published in 1932. Estimated crop year 1931.

    -

    Plot 18 of the Four Course Rotation Experiment, Great Hoos, at - Rothamsted, UK was used. Sown with Yeoman II wheat.

    -

    Field width = 16 segments * 0.5 meters = 8 meters.

    -

    Field length: 80 rows * 6 inches apart = 40 feet.

    -

    The grain yield and number of ears for each half-meter length were - recorded. This is quite a small field, only 1/40 acre in size.

    -

    Edge rows have higher yields. Row-end units have higher yields than - interior units. These border effects are significant. Variation - between rows is greater than variation within rows. Negative - correlation between rows may indicate competition effects.

    -

    For ears, Kalamkar discarded 4 rows from each side and 3 half-meter lengths at each end.

    -

    Kalamkar suggested using four parallel half-meter rows as a sampling unit.

    -

    Note, the Rothamsted report for 1931, page 57, says: - During the year three workers (F. R. Immer, S. H. Justensen and - R. J. Kalamkar) have taken up the question of the most efficient use - of land in experiments in which an edge row must be discarded...

    -

    Source

    - -

    Kalamkar, R. J (1932). - A Study in Sampling Technique with Wheat. - The Journal of Agricultural Science, Vol.22(4), pp.783-796. - https://doi.org/10.1017/S0021859600054599

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(kalamkar.wheat.uniformity)
-  dat <- kalamkar.wheat.uniformity
-  
-  plot(yield ~ ears, dat, main="kalamkar.wheat.uniformity")
-
      
-  # totals match Kalamkar
-  # sum(dat$yield) # 24112.5
-  # sum(dat$ears) # 25850
-  
-  libs(desplot)
-  desplot(dat, ears ~ col*row,
-          flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect
-          main="kalamkar.wheat.uniformity - ears")
-
      desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect
-          main="kalamkar.wheat.uniformity - yield")
-
      
-  # ----------
-  
-  libs(asreml,lucid) # asreml4
-    
-  # Show the negative correlation between rows
-  dat <- transform(dat,
-                   rowf=factor(row), colf=factor(col))
-  dat <- dat[order(dat$rowf, dat$colf),]
-  m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -3434.313       80.1883   1279 17:08:47    0.0
-#>  2     -3416.532       78.3967   1279 17:08:47    0.0
-#>  3     -3401.703       78.3835   1279 17:08:47    0.0
-#>  4     -3396.808       79.9191   1279 17:08:47    0.0
-#>  5     -3396.034       81.2062   1279 17:08:47    0.0
-#>  6     -3396.011       81.4745   1279 17:08:47    0.0
-#>  7     -3396.010       81.5251   1279 17:08:47    0.0
      vc(m1)
-
    #>              effect component std.error z.ratio bound %ch
-#>         rowf:colf!R  81.53      3.525      23       P 0  
-#>  rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
-#>  rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1
      ##             effect component std.error z.ratio bound pctch
-  ##        rowf:colf!R  81.53      3.525      23       P 0  
-  ## rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
-  ## rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kang.maize-1.png b/docs/reference/kang.maize-1.png deleted file mode 100644 index f1cac51b..00000000 Binary files a/docs/reference/kang.maize-1.png and /dev/null differ diff --git a/docs/reference/kang.maize.html b/docs/reference/kang.maize.html deleted file mode 100644 index 9de2078b..00000000 --- a/docs/reference/kang.maize.html +++ /dev/null @@ -1,240 +0,0 @@ - - - - - - - - -Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -

    Multi-environment trial of maize in Louisianna at 4 locs in 3 years

    - - -
    - -
    - -

    Maize yields at 4 locs in 3 years in Louisianna.

    - -
    - - 
    data("kang.maize")
    - -

    Format

    - -

    -
    gen

    genotype, 17 levels

    -
    env

    environment, 12 levels

    -
    yield

    yield, tonnes/ha

    -

    environment, 13 levels

    -
    year

    year, 85-87

    -
    loc

    location, 4 levels

    -
    - - -

    Details

    - -

    Yield trials were conducted at four locations (Alexandria, Baton - Rouge, Bossier City, and St. Joseph) in Louisiana during 1985 to 1987. - Each loc was planted as RCB design with 4 reps. Mean yields are given - in this data.

    - -

    Source

    - -

    Kang, MS and Gorman, DP. (1989). - Genotype x environment interaction in maize. - Agronomy Journal, 81, 662-664. Table 2.

    -

    Used with permission of Dan Gorman.

    - - -

    Examples

    - 
    
-library(agridat)
-data(kang.maize)
-dat <- kang.maize
-
-# Sweep out loc means, then show interaction plot.
-libs(reshape2)
-mat <- acast(dat, gen~env, value.var='yield')
-mat <- sweep(mat, 2, colMeans(mat))
-dat2 <- melt(mat)
-names(dat2) <- c('gen','env','yield')
-libs(lattice)
-xyplot(yield~env|gen, data=dat2, type='l', group=gen,
-       panel=function(x,y,...){
-         panel.abline(h=0,col="gray70")
-         panel.xyplot(x,y,...)
-       },
-       ylab="Environment-centered yield",
-       main="kang.maize - maize hybrid yields", scales=list(x=list(rot=90)))
    
-
-# Weather covariates for each environment.
-covs <- data.frame(env=c("AL85","AL86","AL87", "BR85","BR86","BR87",
-                     "BC85","BC86","BC87", "SJ85","SJ86","SJ87"),
-                   maxt=c(30.7,30.2,29.7,31.5,29.4,28.5, 31.9, 30.4,31.7, 32,29.6,28.9),
-                   mint=c(18.7,19.3,18.5, 19.7,18,17.2, 19.1,20.4,20.3, 20.4,19.1,17.5),
-                   rain=c(.2,.34,.22, .28,.36,.61, .2,.43,.2, .36,.41,.22),
-                   humid=c(82.8,91.1,85.4, 88.1,90.9,88.6, 95.4,90.4,86.7, 95.6,89.5,85))
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kang.peanut-1.png b/docs/reference/kang.peanut-1.png deleted file mode 100644 index 28defdbd..00000000 Binary files a/docs/reference/kang.peanut-1.png and /dev/null differ diff --git a/docs/reference/kang.peanut.html b/docs/reference/kang.peanut.html deleted file mode 100644 index 768ee91c..00000000 --- a/docs/reference/kang.peanut.html +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - -Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Multi-environment trial of peanuts for 10 genotypes in 15 environments

    - - -
    - -
    -

    Peanut yields for 10 genotypes in 15 environments

    -
    - - 
    data("kang.peanut")
    - - -

    Format

    - -

    A data frame with 590 observations on the following 4 variables.

    -
    gen

    genotype factor, 10 levels

    -
    rep

    replicate factor, 4 levels

    -
    yield

    yield

    -
    env

    environment factor, 15 levels

    - -
    - -

    Details

    - -

    Florman, Tegua, mf484, mf485, mf487, mf489 have a long crop cycle. - The others have a short crop cycle.

    -

    This data is also likely used in Casanoves et al 2005, - "Evaluation of Multienvironment Trials of Peanut Cultivars", but this - appears to be a slightly smaller subset (only 10 genotypes, and - perhaps only the years 96,97,98,99). Based on the d.f. in their - table 5, it appears that environment E13 was grown in 1998. - (5 loc * (4-1) = 15, but the table has 14, and 98-99 had only 3 reps - instead of 4 reps.)

    -

    Data from National Institute of Agricultural Technology, Argentina.

    -

    Source

    - -

    M. S. Kang, M. Balzarini, and J. L. L. Guerra - (2004). Genotype-by-environment - interaction". In: A. Saxton (2004). "Genetic Analysis of Complex - Traits Using SAS".

    -

    References

    - -

    Johannes Forkman, Julie Josse, Hans-Peter Piepho (2019). - Hypothesis Tests for Principal Component Analysis When Variables are Standardized. - JABES https://doi.org/10.1007/s13253-019-00355-5

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(kang.peanut)
-dat <- kang.peanut
-
-# Table 5.1 of Kang et al. (Chapter 5 of Saxton)
-libs(reshape2)
-Y0 <- acast(dat, env~gen, value.var='yield', fun=mean)
-round(Y0,2)
-
    #>     Florman Tegua manf393 mf447 mf478 mf480 mf484 mf485 mf487 mf489
-#> E01    0.80  0.96    1.24  0.95  1.37  1.41  1.16  1.12  0.87  1.11
-#> E02    2.17  2.04    1.57  1.29  2.15  3.27  1.08  0.58  1.52  0.86
-#> E03    2.43  2.58    2.47  2.34  2.19  2.19  2.64  2.24  2.30  2.20
-#> E04    2.71  2.26    1.77  1.61  2.15  2.04  2.14  1.88  1.72  2.18
-#> E05    1.13  1.14    1.55  1.86  1.98  1.61  1.71  0.85  1.24  1.21
-#> E06    3.08  3.22    2.90  2.59  2.36  2.43  3.05  2.90  2.94  2.57
-#> E07    2.81  2.88    2.96  3.41  3.20  2.96  2.91  2.53  2.73  2.90
-#> E08    1.74  1.73    2.16  1.44  2.20  0.95  2.86  2.13  1.60  2.29
-#> E09    2.16  2.44    3.30  3.01  3.37  2.53  2.73  3.00  3.18  3.25
-#> E10    4.29  4.21    3.55  3.84  3.53  3.22  4.45  4.46  4.24  4.03
-#> E11    1.82  1.71    2.16  1.88  2.09  1.91  2.53  1.86  1.71  2.27
-#> E12    5.33  4.93    4.69  4.16  4.70  3.57  5.57  5.43  4.99  4.67
-#> E13    1.18  1.32    2.24  1.63  1.54  1.15  2.45  1.78  1.54  2.00
-#> E14    4.39  4.40    4.13  3.79  4.33  3.72  4.28  3.77  4.17  4.75
-#> E15    3.41  3.45    2.22  2.46  3.09  2.61  2.81  3.15  3.84  3.54
    
-# GGE biplot of Kang, p. 82.
-libs(gge)
-m1 <- gge(dat, yield~gen*env, scale=FALSE)
-biplot(m1, flip=c(1,1), main="kang.peanut - GGE biplot")
-
    
-# Forkman 2019, fig 2
-# m2 <- gge(dat, yield~gen*env, scale=TRUE)
-# biplot(m2, main="kang.peanut - GGE biplot")
-# biplot(m2, comps=3:4, main="kang.peanut - GGE biplot")
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/karcher.turfgrass-1.png b/docs/reference/karcher.turfgrass-1.png deleted file mode 100644 index 788bcf8e..00000000 Binary files a/docs/reference/karcher.turfgrass-1.png and /dev/null differ diff --git a/docs/reference/karcher.turfgrass.html b/docs/reference/karcher.turfgrass.html deleted file mode 100644 index 4730e52d..00000000 --- a/docs/reference/karcher.turfgrass.html +++ /dev/null @@ -1,288 +0,0 @@ - - - - - - - - -Turfgrass ratings for different treatments — karcher.turfgrass • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Turfgrass ratings for different treatments

    - - -
    - -
    -

    Turfgrass ratings for different treatments

    -
    - - - -

    Format

    - -

    A data frame with 128 observations on the following 6 variables.

    -
    week

    week number

    -
    rep

    blocking factor

    -
    manage

    management factor, 4 levels

    -
    nitro

    nitrogen factor, 2 levels

    -
    rating

    turfgrass rating, 4 ordered levels

    -
    count

    number of samples for a given rating

    - -
    - -

    Details

    - -

    Turf color was assessed on a scale of Poor, Average, Good, Excellent.

    -

    The data are the number of times that a combination of management style - and nitrogen level received a particular rating across four replicates - and four sampling weeks. The eight treatments were in a completely - randomized design.

    -

    Nitrogen level 1 is 2.5 g/m^2, level 2 is 5 g/m^2.

    -

    Management 1 = N applied with no supplemental water injection.

    -

    M2 = surface applied with supplemental water injection.

    -

    M3 = nitrogen injected 7.6 cm deep

    -

    M4 = nitrogen injected 12.7 cm deep.

    -

    Source

    - -

    Schabenberger, Oliver and Francis J. Pierce. 2002. - Contemporary Statistical Models for the Plant and Soil - Sciences. CRC Press. Page 380.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(karcher.turfgrass)
-dat <- karcher.turfgrass
-
-dat$rating <- ordered(dat$rating, levels=c('Poor','Average', 'Good','Excellent'))
-
-ftable(xtabs(~manage+nitro+rating, dat)) # Table 6.19 of Schabenberger
-
    #>              rating Poor Average Good Excellent
-#> manage nitro                                   
-#> M1     N1             14       2    0         0
-#>        N2              5      11    0         0
-#> M2     N1             15       1    0         0
-#>        N2              8       8    0         0
-#> M3     N1              0       9    7         0
-#>        N2              0       2   14         0
-#> M4     N1              1      12    3         0
-#>        N2              0       4   11         1
    
-# Probably would choose management M3, nitro N2
-mosaicplot(xtabs(count ~ manage + rating + nitro, dat),
-           shade=TRUE, dir=c('h','v','h'),
-           main="karcher.turfgrass - turfgrass ratings")
-
    
-# Multinomial logistic model.  Probit Ordered Logistic Regression.
-libs(MASS)
-m1 <- polr(rating ~ nitro*manage + week, dat, weights=count, Hess=TRUE, method='logistic')
-summary(m1)
-
    #> Call:
-#> polr(formula = rating ~ nitro * manage + week, data = dat, weights = count, 
-#>     Hess = TRUE, method = "logistic")
-#> 
-#> Coefficients:
-#>                    Value Std. Error t value
-#> nitroN2           3.1565     0.8255  3.8235
-#> manageM2         -0.8728     1.0084 -0.8656
-#> manageM3          8.2088     1.2938  6.3448
-#> manageM4          6.8309     1.2595  5.4235
-#> week             -1.0952     0.1823 -6.0080
-#> nitroN2:manageM2  0.2618     1.2318  0.2126
-#> nitroN2:manageM3 -0.7183     0.9831 -0.7307
-#> nitroN2:manageM4  0.5304     1.0120  0.5242
-#> 
-#> Intercepts:
-#>                Value   Std. Error t value
-#> Poor|Average   -1.0751  0.7057    -1.5235
-#> Average|Good    5.4853  1.2123     4.5247
-#> Good|Excellent 11.6691  1.3628     8.5627
-#> 
-#> Residual Deviance: 247.6414 
-#> AIC: 269.6414 
    
-# Try to match the "predicted marginal probability distribution" of
-# Schabenberger table 6.20.  He doesn't define "marginal".
-# Are the interaction terms included before aggregation?
-# Are 'margins' calculated before/after back-transforming?
-# At what level is the covariate 'week' included?
-
-# Here is what Schabenberger presents:
-##        M1   M2   M3   M4  |   N1  N2
-## Poor  .668 .827 .001 .004 | .279 .020
-## Avg   .330 .172 .297 .525 | .712 .826
-## Good  .002 .001 .695 .008 | .008 .153
-## Exc   .000 .000 .007 .003 | .001 .001
-
-## We use week=3.5, include interactions, then average
-newd <- expand.grid(manage=levels(dat$manage), nitro=levels(dat$nitro), week=3.5)
-newd <- cbind(newd, predict(m1, newdata=newd, type='probs')) # probs)
-print(aggregate( . ~ manage, data=newd, mean), digits=2)
-
    #>   manage nitro week   Poor Average    Good Excellent
-#> 1     M1   1.5  3.5 0.6711    0.33 0.00109   2.3e-06
-#> 2     M2   1.5  3.5 0.7636    0.24 0.00059   1.2e-06
-#> 3     M3   1.5  3.5 0.0023    0.48 0.51504   4.2e-03
-#> 4     M4   1.5  3.5 0.0086    0.57 0.41908   3.5e-03
    ##   manage nitro week   Poor Average    Good Excellent
-## 1     M1   1.5  3.5 0.67      0.33 0.0011  0.0000023
-## 2     M2   1.5  3.5 0.76      0.24 0.00059 0.0000012
-## 3     M3   1.5  3.5 0.0023    0.48 0.52    0.0042
-## 4     M4   1.5  3.5 0.0086    0.57 0.42    0.0035
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kayad.alfalfa-1.png b/docs/reference/kayad.alfalfa-1.png deleted file mode 100644 index 886cc058..00000000 Binary files a/docs/reference/kayad.alfalfa-1.png and /dev/null differ diff --git a/docs/reference/kayad.alfalfa.html b/docs/reference/kayad.alfalfa.html deleted file mode 100644 index 2f5f26d9..00000000 --- a/docs/reference/kayad.alfalfa.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - - -Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia.

    - - -
    - -
    -

    Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia.

    -
    - - 
    data("kayad.alfalfa")
    - - -

    Format

    - -

    A data frame with 8628 observations on the following 4 variables.

    -
    harvest

    harvest number

    -
    lat

    latitude

    -
    long

    longitude

    -
    yield

    yield, tons/ha

    - -
    - -

    Details

    - -

    Data was collected from a 23.5 ha field of alfalfa in Saudia Arabia. - The field was harvested four consecutive times (H8 = 5 Dec 2013, H9 = 16 Feb 2014, H10 = 2 Apr 2014, H11 = 6 May 2014). - Data were collected using a geo-referenced yield monitor. - Supporting information contains yield monitor data for 4 hay harvests on a center-pivot field.

    -

    # TODO: Normalize the yields for each harvest, then average together - # to create a productivity map. Two ways to normalize: - # Normalize to 0-100: ((mapValue - min) * 100) / (max - min) - # Standardize: ((mapValue - mean) / stdev) * 100

    -

    Source

    - -

    Ahmed G. Kayad, et al. (2016). - Assessing the Spatial Variability of Alfalfa Yield Using Satellite Imagery and Ground-Based Data. - PLOS One, 11(6). - https://doi.org/10.1371/journal.pone.0157166

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  data(kayad.alfalfa)
-  dat <- kayad.alfalfa
-  
-  # match Kayad table 1 stats
-  libs(dplyr)
-  dat <- group_by(dat, harvest)
-  summarize(dat, min=min(yield), max=max(yield),
-            mean=mean(yield), stdev=sd(yield), var=var(yield))
-
    #> `summarise()` ungrouping output (override with `.groups` argument)
    #> # A tibble: 4 x 6
-#>   harvest   min   max  mean stdev   var
-#>   <fct>   <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 H10     0      6.68  2.86  1.24  1.55
-#> 2 H11     0      9.96  4.01  1.69  2.85
-#> 3 H8      0      5.86  2.32  1.01  1.03
-#> 4 H9      0.191  5.97  2.45  1.07  1.14
    
-  # Figure 4 of Kayad
-  libs(latticeExtra)
-  catcols <- c("#cccccc","#ff0000","#ffff00","#55ff00","#0070ff","#c500ff","#73004c")
-  levelplot(yield ~ long*lat |harvest, dat,
-            aspect=1, at = c(0,2,3,4,5,6,7,10), col.regions=catcols,
-            main="kayad.alfalfa",
-            prepanel=prepanel.default.xyplot,
-            panel=panel.levelplot.points)
-
    
-  # Similar to Kayad fig 5.
-  ## levelplot(yield ~ long*lat |harvest, dat,
-  ##           prepanel=prepanel.default.xyplot,
-  ##           panel=panel.levelplot.points,
-  ##           col.regions=pals::brewer.reds)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/keen.potatodamage-1.png b/docs/reference/keen.potatodamage-1.png deleted file mode 100644 index 5155b2d7..00000000 Binary files a/docs/reference/keen.potatodamage-1.png and /dev/null differ diff --git a/docs/reference/keen.potatodamage.html b/docs/reference/keen.potatodamage.html deleted file mode 100644 index 168229c6..00000000 --- a/docs/reference/keen.potatodamage.html +++ /dev/null @@ -1,204 +0,0 @@ - -Damage to potato tubers from lifting rods. — keen.potatodamage • agridat - - -
    -
    - - - -
    -
    -
    -

    Damage to potato tubers from lifting rods.

    - - -
    - -
    -

    Damage to potato tubers from lifting rods.

    -
    - -
    - 
    data("keen.potatodamage")
    -
    - -
    -

    Format

    -

    A data frame with 1152 observations on the following 6 variables.

    energy
    -

    energy factor

    - -
    weight
    -

    weight class

    - -
    gen
    -

    genotype/variety factor

    - -
    rod
    -

    rod factor

    - -
    damage
    -

    damage category

    - -
    count
    -

    count of tubers in each combination of categories

    - - -
    -
    -

    Details

    -

    Experiments performed at Wageningen, Netherlands.

    -

    Potatoes can be damaged by the lifter. In this experiment, eight - types of lifting rod were compared. Two energy levels, six - genotypes/varieties and three weight classes were used. - Most combinations of treatments involved about 20 potato tubers. - Tubers were rated as undamaged (D1) to severely damaged (D4).

    -

    The main interest is in differences between rods, and not in - interactions. The other factors (besides rod) were introduced to - create variety in experimental conditions and are not of interest.

    -

    Keen and Engle estimated the following rod effects.

    -

    # Rod: 1 2 3 4 5 6 7 8

    -

    # Effect: 0 -1.26 -0.42 0.55 -1.50 -1.85 -1.76 -2.09

    -

    Used with permission of Bas Engel.

    -
    -
    -

    Source

    -

    A. Keen and B. Engel. - Analysis of a mixed model for ordinal data by iterative re-weighted REML. - Statistica Neerlandica, 51, 129--144. Table 2. - https://doi.org/10.1111/1467-9574.00044

    -
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    -

    References

    -

    R. Larsson & Jesper Ryden (2021). - Applications of discrete factor analysis. - Communications in Statistics - Simulation and Computation. - https://doi.org/10.1080/03610918.2021.1964528

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(keen.potatodamage)
-dat <- keen.potatodamage
-
-# Energy E1, Rod R4, Weight W1 have higher proportions of severe damage
-# Rod 8 has the least damage
-d2 <- xtabs(count~energy+rod+gen+weight+damage, data=dat)
-mosaicplot(d2, color=c("lemonchiffon1","moccasin","lightsalmon1","indianred"),
-           xlab="Energy / Genotype", ylab="Rod / Weight", main="keen.potatodamage")
-
-
-
-# Not run because CRAN prefers examples less than 5 seconds.
-libs(ordinal)
-#> 
-#> Attaching package: 'ordinal'
-#> The following objects are masked from 'package:lme4':
-#> 
-#>     VarCorr, ranef
-#> The following objects are masked from 'package:nlme':
-#> 
-#>     VarCorr, ranef
-#> The following object is masked from 'package:dplyr':
-#> 
-#>     slice
-# Note, the clmm2 function can have only 1 random term.  Results are
-# similar to Keen & Engle, but necessarily different (they had multiple
-# random terms).
-m1 <- clmm2(damage ~ rod + energy + gen + weight, data=dat,
-            weights=count, random=rod:energy, link='probit')
-#> Warning: clmm2 may not have converged:
-#>   optimizer 'ucminf' terminated with max|gradient|: 6.24989053934658e-05
-round(coef(m1)[4:10],2)
-#> rodR2 rodR3 rodR4 rodR5 rodR6 rodR7 rodR8 
-#> -1.19 -0.41  0.50 -1.46 -1.73 -1.67 -1.99 
-## rodR2 rodR3 rodR4 rodR5 rodR6 rodR7 rodR8
-## -1.19 -0.41  0.50 -1.46 -1.73 -1.67 -1.99
-
-# Alternative
-# m2 <- clmm(damage ~ rod + energy + gen + weight +
-#          (1|rod:energy), data=dat, weights=count, link='probit')
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/kempton.barley.uniformity-1.png b/docs/reference/kempton.barley.uniformity-1.png deleted file mode 100644 index 216c6ef5..00000000 Binary files a/docs/reference/kempton.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kempton.barley.uniformity.html b/docs/reference/kempton.barley.uniformity.html deleted file mode 100644 index e1d004c0..00000000 --- a/docs/reference/kempton.barley.uniformity.html +++ /dev/null @@ -1,271 +0,0 @@ - - - - - - - - -Uniformity trial of barley — kempton.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of barley

    - - -
    - -
    -

    Uniformity trial of barley at Cambridge, England, 1978.

    -
    - - - -

    Details

    - -

    A uniformity trial of spring barley planted in 1978. Conducted by the - Plant Breeding Institute in Cambridge, England.

    -

    Each plot is 5 feet wide, 14 feet long.

    -

    Field width: 7 plots * 14 feet = 98 feet

    -

    Field length: 28 plots * 5 feet = 140 feet

    -

    Format

    - -

    A data frame with 196 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    grain yield, kg

    - -
    - -

    Source

    - -

    R. A. Kempton and C. W. Howes (1981). - The use of neighbouring plot values in the analysis of variety trials. - Applied Statistics, 30, 59--70. - https://doi.org/10.2307/2346657

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science. 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(kempton.barley.uniformity)
-  dat <- kempton.barley.uniformity
-
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          aspect=140/98, tick=TRUE, # true aspect
-          main="kempton.barley.uniformity")
-
      
-  
-  # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91
-  
-  dat <- transform(dat, xf = factor(col), yf=factor(row))
-
-  # ----------
-
-  libs(asreml,lucid) # asreml4
-  
-  dat <- transform(dat, xf = factor(col), yf=factor(row))
-  m1 <- asreml(yield ~ 1, data=dat, resid = ~ar1(xf):ar1(yf))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:48 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1       138.768     0.0880303    195 17:08:48    0.0 (1 restrained)
-#>  2       190.210     0.0624855    195 17:08:48    0.0
-#>  3       224.660     0.0667102    195 17:08:48    0.0
-#>  4       230.382     0.0828740    195 17:08:48    0.0
-#>  5       231.397     0.0999127    195 17:08:48    0.0
-#>  6       231.425     0.1038940    195 17:08:48    0.0
-#>  7       231.425     0.1043644    195 17:08:48    0.0
      
-  # vc(m1)
-  ##       effect component std.error z.ratio bound 
-  ##      xf:yf!R    0.1044   0.02197     4.7     P   0
-  ## xf:yf!xf!cor    0.2458   0.07484     3.3     U   0
-  ## xf:yf!yf!cor    0.8186   0.03821    21       U   0
-  
-  # asreml estimates auto-regression correlations of 0.25, 0.82
-  # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91
-
-  # ----------
-
-  if(0){
-    # Kempton defines 4 blocks, randomly assigns variety codes 1-49 in each block, fits
-    # RCB model, computes mean squares for variety and residual.  Repeat 40 times.
-    # Kempton's estimate: variety = 1032, residual = 1013
-    # Our estimate: variety = 825, residual = 1080
-    fitfun <- function(dat){
-      dat <- transform(dat, block=factor(ceiling(row/7)),
-                       gen=factor(c(sample(1:49),sample(1:49),sample(1:49),sample(1:49))))
-      m2 <- lm(yield*100 ~ block + gen, dat)
-      anova(m2)[2:3,'Mean Sq']
-    }
-    set.seed(251)
-    out <- replicate(50, fitfun(dat))
-    rowMeans(out) # 826 1079
-  }
-
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kempton.competition-1.png b/docs/reference/kempton.competition-1.png deleted file mode 100644 index a4b60338..00000000 Binary files a/docs/reference/kempton.competition-1.png and /dev/null differ diff --git a/docs/reference/kempton.competition-2.png b/docs/reference/kempton.competition-2.png deleted file mode 100644 index 0fecc4ac..00000000 Binary files a/docs/reference/kempton.competition-2.png and /dev/null differ diff --git a/docs/reference/kempton.competition.html b/docs/reference/kempton.competition.html deleted file mode 100644 index 67d0e728..00000000 --- a/docs/reference/kempton.competition.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Sugar beet trial with competition effects — kempton.competition • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Sugar beet trial with competition effects

    - - -
    - -
    -

    Yield of sugar beets for 36 varieties in a 3-rep RCB experiment. - Competition effects are present.

    -
    - - - -

    Format

    - -

    A data frame with 108 observations on the following 5 variables.

    -
    gen

    genotype, 36 levels

    -
    rep

    rep, 3 levels

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, kg/plot

    - -
    - -

    Details

    - -

    Entries are grown in 12m rows, 0.5m apart. Guard rows were grown - alongside replicate boundaries, but yields of these plots are not - included.

    -

    Source

    - -

    R Kempton, 1982. - Adjustment for competition between varieties in plant breeding trials, - Journal of Agricultural Science, 98, 599-611. - https://doi.org/10.1017/S0021859600054381

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-
-data(kempton.competition)
-dat <- kempton.competition
-
-# Raw means in Kempton table 2
-round(tapply(dat$yield, dat$gen, mean),2)
-
    #>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
-#>  6.57 14.70  3.73 18.70  6.17  8.80 10.20  4.57  8.67 16.63  7.77  6.07 14.07 
-#>   G14   G15   G16   G17   G18   G19   G20   G21   G22   G23   G24   G25   G26 
-#> 12.17 15.67 11.93 19.60 11.63 13.50  6.43  8.47 15.97 18.07 15.33  6.03  6.13 
-#>   G27   G28   G29   G30   G31   G32   G33   G34   G35   G36 
-#> 11.87 12.20 22.03 15.07 13.60 14.70 13.67 16.27  5.40  8.43 
    
-# Fixed genotype effects, random rep effects,
-# Autocorrelation of neighboring plots within the same rep, phi = -0.22
-libs(nlme)
-m1 <- lme(yield ~ -1+gen, random=~1|rep, data=dat,
-          corr=corAR1(form=~col|rep))
-# Lag 1 autocorrelation is negative--evidence of competition
-plot(ACF(m1), alpha=.05, grid=TRUE, main="kempton.competition",
-     ylab="Autocorrelation between neighborning plots")
-
    
-# Genotype effects
-round(fixef(m1),2)
-
    #> genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 genG11 
-#>   7.29  14.69   4.44  17.67   5.88   8.69  10.11   4.20   8.66  17.23   8.30 
-#> genG12 genG13 genG14 genG15 genG16 genG17 genG18 genG19 genG20 genG21 genG22 
-#>   6.49  13.94  11.97  15.60  12.07  19.50  12.15  13.38   6.69   8.55  15.87 
-#> genG23 genG24 genG25 genG26 genG27 genG28 genG29 genG30 genG31 genG32 genG33 
-#>  17.86  15.00   6.70   7.08  11.68  12.52  21.71  14.11  12.81  14.93  12.92 
-#> genG34 genG35 genG36 
-#>  16.60   5.68   8.79 
    
-# Variance of yield increases with yield
-plot(m1, main="kempton.competition")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kempton.rowcol-1.png b/docs/reference/kempton.rowcol-1.png deleted file mode 100644 index 8a752300..00000000 Binary files a/docs/reference/kempton.rowcol-1.png and /dev/null differ diff --git a/docs/reference/kempton.rowcol.html b/docs/reference/kempton.rowcol.html deleted file mode 100644 index f493e646..00000000 --- a/docs/reference/kempton.rowcol.html +++ /dev/null @@ -1,249 +0,0 @@ - - - - - - - - -Row-column experiment of wheat — kempton.rowcol • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - -
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    Row-column experiment of wheat

    - - -
    - -
    -

    Row-column experiment of wheat, 35 genotypes, 2 reps.

    -
    - - - -

    Format

    - -

    A data frame with 68 observations on the following 5 variables.

    -
    rep

    replicate factor, 2 levels

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype factor, 35 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Included to illustrate REML analysis of a row-column design.

    -

    Source

    - -

    R A Kempton and P N Fox, - Statistical Methods for Plant Variety Evaluation, - Chapman and Hall, 1997.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(kempton.rowcol)
-dat <- kempton.rowcol
-dat <- transform(dat, rowf=factor(row), colf=factor(col))
-
-libs(desplot)
-desplot(dat, yield~col*row|rep,
-        num=gen, out1=rep, # unknown aspect
-        main="kempton.rowcol")
-
    
-
-# Model with rep, row, col as random.  Kempton, page 62.
-# Use "-1" so that the vcov matrix doesn't include intercept
-libs(lme4)
-m1 <- lmer(yield ~ -1 + gen + rep + (1|rep:rowf) + (1|rep:colf), data=dat)
-
-# Variance components match Kempton.
-print(m1, corr=FALSE)
-
    #> Linear mixed model fit by REML ['lmerMod']
-#> Formula: yield ~ -1 + gen + rep + (1 | rep:rowf) + (1 | rep:colf)
-#>    Data: dat
-#> REML criterion at convergence: 70.0235
-#> Random effects:
-#>  Groups   Name        Std.Dev.
-#>  rep:colf (Intercept) 0.4387  
-#>  rep:rowf (Intercept) 0.2531  
-#>  Residual             0.3002  
-#> Number of obs: 68, groups:  rep:colf, 14; rep:rowf, 10
-#> Fixed Effects:
-#> genG01  genG02  genG03  genG04  genG05  genG06  genG07  genG08  genG09  genG10  
-#>  4.203   3.304   4.487   2.910   3.785   4.799   4.474   3.991   3.739   3.717  
-#> genG11  genG12  genG13  genG14  genG15  genG16  genG17  genG18  genG19  genG20  
-#>  4.320   4.335   4.071   4.154   2.605   3.344   3.542   3.953   5.059   3.708  
-#> genG21  genG22  genG23  genG24  genG25  genG26  genG27  genG28  genG29  genG30  
-#>  3.982   3.401   2.813   3.277   4.029   3.148   4.089   3.682   3.183   3.342  
-#> genG31  genG32  genG33  genG34  genG35   repR2  
-#>  3.248   3.652   4.303   3.687   2.991   1.222  
    
-# Standard error of difference for genotypes.  Kempton page 62, bottom.
-covs <- as.matrix(vcov(m1)[1:35, 1:35])
-vars <- diag(covs)
-vdiff <- outer(vars, vars, "+") - 2 * covs
-sed <- sqrt(vdiff[upper.tri(vdiff)])
-min(sed) # Minimum SED
-
    #> [1] 0.35107
    mean(sed) # Average SED
-
    #> [1] 0.3853689
    max(sed) # Maximum SED
-
    #> [1] 0.5357671
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kempton.slatehall-1.png b/docs/reference/kempton.slatehall-1.png deleted file mode 100644 index d7c5e863..00000000 Binary files a/docs/reference/kempton.slatehall-1.png and /dev/null differ diff --git a/docs/reference/kempton.slatehall.html b/docs/reference/kempton.slatehall.html deleted file mode 100644 index 4400115d..00000000 --- a/docs/reference/kempton.slatehall.html +++ /dev/null @@ -1,280 +0,0 @@ - - - - - - - - -Slate Hall Farm 1976 spring wheat — kempton.slatehall • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Slate Hall Farm 1976 spring wheat

    - - -
    - -
    -

    Yields for a Slate Hall Farm 1976 spring wheat trial.

    -
    - - - -

    Format

    - -

    A data frame with 150 observations on the following 5 variables.

    -
    rep

    rep, 6 levels

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype, 25 levels

    -
    yield

    yield (grams/plot)

    - -
    - -

    Details

    - -

    The trial was a balanced lattice with 25 varieties in 6 replicates, - 10 ranges of 15 columns. The plot size was 1.5 meters by 4 meters. - Each row within a rep is an (incomplete) block.

    -

    Field width: 15 columns * 1.5m = 22.5m

    -

    Field length: 10 ranges * 4m = 40m

    -

    Source

    - -

    R A Kempton and P N Fox. (1997). - Statistical Methods for Plant Variety Evaluation, - Chapman and Hall. Page 84.

    -

    Julian Besag and David Higdon. 1993. - Bayesian Inference for Agricultural Field Experiments. - Bull. Int. Statist. Table 4.1.

    -

    References

    - -

    Gilmour, Arthur R and Robin Thompson and Brian R Cullis. (1994). - Average Information REML: An Efficient Algorithm for Variance - Parameter Estimation in Linear Mixed Models, Biometrics, - 51, 1440-1450.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(kempton.slatehall)
-  dat <- kempton.slatehall
-
-  # Besag 1993 figure 4.1 (left panel)
-  libs(desplot)
-  grays <- colorRampPalette(c("#d9d9d9","#252525"))
-  desplot(dat, yield ~ col * row,
-          aspect=40/22.5, # true aspect
-          num=gen, out1=rep, col.regions=grays, # unknown aspect
-          main="kempton.slatehall - spring wheat yields")
-
    
-  # ----------
-
-  # Incomplete block model of Gilmour et al 1995
-  libs(lme4, lucid)
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:yf) + (1|rep:xf), data=dat)
-  vc(m1)
-
    #>       grp        var1 var2  vcov  sdcor
-#>    rep:xf (Intercept) <NA> 14810 121.7 
-#>    rep:yf (Intercept) <NA> 15600 124.9 
-#>       rep (Intercept) <NA>  4263  65.29
-#>  Residual        <NA> <NA>  8062  89.79
      ##    groups        name variance stddev
-  ##  rep:xf   (Intercept)    14810 121.7
-  ##  rep:yf   (Intercept)    15600 124.9
-  ##  rep      (Intercept)     4262  65.29
-  ##  Residual                 8062  89.79
-  
-  
-  # ----------
-
-  # asreml3 & asreml4
-  libs(asreml,lucid)
-  
-  # Incomplete block model of Gilmour et al 1995
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  m2 <- asreml(yield ~ gen, random = ~ rep/(xf+yf), data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:48:28 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -734.184      26778.41    125 17:48:28    0.0
-#>  2      -720.066      16594.96    125 17:48:28    0.0
-#>  3      -711.122      11175.53    125 17:48:28    0.0
-#>  4      -708.253       8997.74    125 17:48:28    0.0
-#>  5      -707.791       8149.58    125 17:48:28    0.0
-#>  6      -707.786       8062.41    125 17:48:28    0.0
      
-  vc(m2)
-
    #>   effect component std.error z.ratio bound %ch
-#>      rep      4263      6877    0.62     P 0.4
-#>   rep:yf     15600      5090    3.1      P 0  
-#>   rep:xf     14810      4866    3        P 0  
-#>  units!R      8062      1341    6        P 0  
      ##          effect component std.error z.ratio constr
-  ##     rep!rep.var      4262      6890    0.62    pos
-  ##  rep:xf!rep.var     14810      4865    3       pos
-  ##  rep:yf!rep.var     15600      5091    3.1     pos
-  ##      R!variance      8062      1340    6       pos
-  
-  # Table 4
-  # asreml3
-  # predict(m2, data=dat, classify="gen")$predictions$pvals
-  # asreml4
-  # predict(m2, data=dat, classify="gen")$pvals
-
-# }
-
    -
    - -
    - - -
    -
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    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kenward.cattle-1.png b/docs/reference/kenward.cattle-1.png deleted file mode 100644 index 51a420f4..00000000 Binary files a/docs/reference/kenward.cattle-1.png and /dev/null differ diff --git a/docs/reference/kenward.cattle-2.png b/docs/reference/kenward.cattle-2.png deleted file mode 100644 index 256a17ef..00000000 Binary files a/docs/reference/kenward.cattle-2.png and /dev/null differ diff --git a/docs/reference/kenward.cattle-3.png b/docs/reference/kenward.cattle-3.png deleted file mode 100644 index a03475b3..00000000 Binary files a/docs/reference/kenward.cattle-3.png and /dev/null differ diff --git a/docs/reference/kenward.cattle.html b/docs/reference/kenward.cattle.html deleted file mode 100644 index 8e9e2259..00000000 --- a/docs/reference/kenward.cattle.html +++ /dev/null @@ -1,330 +0,0 @@ - - - - - - - - -Repeated measurement of weights of calves with two treatments. — kenward.cattle • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Repeated measurement of weights of calves with two treatments.

    - - -
    - -
    -

    Repeated measurements of the weights of calves from a trial on the - control of intestinal parasites.

    -
    - - 
    data("kenward.cattle")
    - - -

    Format

    - -

    A data frame with 660 observations on the following 4 variables.

    -
    animal

    animal factor

    -
    trt

    treatment factor, A or B

    -
    day

    day, numberic, 0-133

    -
    weight

    bodyweight, kg

    - -
    - -

    Details

    - - - -

    Grazing cattle can ingest larvae, which deprives the host animal of - nutrients and weakens the immune system, affecting the growth of the - animal.

    -

    Two treatments A and B were applied randomly to 60 animals - (30 each in two groups) to control the disease.

    -

    Each animal was weighed 11 times at two-week intervals (one week - between the final two measurements).

    -

    Is there a difference in treatments, and when does that difference - first become manifest?

    -

    Source

    - -

    Kenward, Michael G. (1987). - A Method for Comparing Profiles of Repeated Measurements. - Applied Statistics, 36, 296-308. Table 1. - https://doi.org/10.2307/2347788

    -

    References

    - -

    W. Zhang, C. Leng and C. Y. Tang (2015). - A joint modelling approach for longitudinal studies - J. R. Statist. Soc. B, 77 (2015), 219--238. - https://doi.org/10.1111/rssb.12065

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(kenward.cattle)
-  dat <- kenward.cattle
-
-  # Profile plots
-  libs(lattice)
-  foo1 <- xyplot(weight~day|trt, data=dat, type='l', group=animal,
-                 xlab="Day", ylab="Animal weight", main="kenward.cattle")
-  print(foo1)
-
      
-  # ----------
-
-  # lme4. Fixed treatment intercepts, treatment polynomial trend.
-  # Random deviation for each animal
-  libs(lme4)
-  m1a <-lmer(weight ~ trt*poly(day, 4) + (1|animal), data=dat,
-             REML = FALSE)
-  # Change separate polynomials into common polynomial
-  m1b <-lmer(weight ~ trt + poly(day, 4) + (1|animal), data=dat,
-             REML = FALSE)
-  # Drop treatment differences
-  m1c <-lmer(weight ~ poly(day, 4) + (1|animal), data=dat,
-             REML = FALSE)
-  anova(m1a, m1b, m1c) # Significant differences between trt polynomials
-
    #> Data: dat
-#> Models:
-#> m1c: weight ~ poly(day, 4) + (1 | animal)
-#> m1b: weight ~ trt + poly(day, 4) + (1 | animal)
-#> m1a: weight ~ trt * poly(day, 4) + (1 | animal)
-#>     npar    AIC    BIC  logLik deviance   Chisq Df Pr(>Chisq)    
-#> m1c    7 4901.2 4932.6 -2443.6   4887.2                          
-#> m1b    8 4903.0 4938.9 -2443.5   4887.0  0.2047  1     0.6509    
-#> m1a   12 4885.7 4939.6 -2430.9   4861.7 25.2640  4  4.452e-05 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-  # Overlay polynomial predictions on plot
-  libs(latticeExtra)
-  dat$pred <- predict(m1a, re.form=NA)
-  foo1 + xyplot(pred ~ day|trt, data=dat,
-                lwd=2, col="black", type='l')
-
      
-  # A Kenward-Roger Approximation and Parametric Bootstrap
-  # libs(pbkrtest)
-  # KRmodcomp(m1b, m1c) # Non-signif
-  # Model comparison of nested models using parametric bootstrap methods
-  # PBmodcomp(m1b, m1c, nsim=500)
-  ## Parametric bootstrap test; time: 13.20 sec; samples: 500 extremes: 326;
-  ## large : weight ~ trt + poly(day, 4) + (1 | animal)
-  ## small : weight ~ poly(day, 4) + (1 | animal)
-  ##          stat df p.value
-  ## LRT    0.2047  1  0.6509
-  ## PBtest 0.2047     0.6527
-
-  # -----------
-
-  # asreml3 & asreml4
-  
-  # ASREML approach to model. Not final by any means.
-  # Maybe a spline curve for each treatment, plus random deviations for each time
-  libs(asreml)
-  m1 <- asreml(weight ~  1 + lin(day) +    # overall line
-                 trt + trt:lin(day),       # different line for each treatment
-               data=dat,
-               random = ~ spl(day) +       # overall spline
-                 trt:spl(day) +            # different spline for each treatment
-                 dev(day) + trt:dev(day) ) # non-spline deviation at each time*trt
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:49 2021
-#> Spline: design points closer than 0.0133 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -2194.744       261.768    656 17:08:49    0.0 (3 restrained)
-#>  2     -2187.368       267.488    656 17:08:49    0.0 (2 restrained)
-#>  3     -2186.465       267.804    656 17:08:49    0.0 (2 restrained)
-#>  4     -2186.238       267.347    656 17:08:49    0.0 (2 restrained)
-#>  5     -2186.185       267.055    656 17:08:49    0.0 (2 restrained)
-#>  6     -2186.181       267.001    656 17:08:49    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
      
-  p1 <- predict(m1, data=dat, classify="trt:day")
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:49 2021
-#> Spline: design points closer than 0.0133 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -2186.181       266.996    656 17:08:49    0.0
-#>  2     -2186.181       266.996    656 17:08:49    0.0
-#>  3     -2186.181       266.996    656 17:08:49    0.0
      p1 <- p1$pvals
-  
-  foo2 <- xyplot(predicted.value ~ day|trt, p1, type='l', lwd=2, lty=1, col="black")
-  
-  libs(latticeExtra)
-  print(foo1 + foo2)
-
    
-  # Not much evidence for treatment differences
-  
-  # wald(m1)
-  ##               Df Sum of Sq Wald statistic Pr(Chisq)    
-  ## (Intercept)    1  37128459         139060    <2e-16 ***
-  ## trt            1       455              2    0.1917    
-  ## lin(day)       1    570798           2138    <2e-16 ***
-  ## trt:lin(day)   1       283              1    0.3031    
-  ## residual (MS)          267                             
-  
-  # vc(m1)
-  ##               effect component std.error z.ratio constr
-  ##             spl(day)  25.29    24.09        1       pos
-  ##             dev(day)   1.902    4.923       0.39    pos
-  ## trt:spl(day)!trt.var   0.00003  0.000002   18      bnd 
-  ## trt:dev(day)!trt.var   0.00003  0.000002   18      bnd 
-  ##           R!variance 267       14.84       18       pos
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kerr.sugarcane.uniformity-1.png b/docs/reference/kerr.sugarcane.uniformity-1.png deleted file mode 100644 index 887272e7..00000000 Binary files a/docs/reference/kerr.sugarcane.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kerr.sugarcane.uniformity.html b/docs/reference/kerr.sugarcane.uniformity.html deleted file mode 100644 index a3fc212d..00000000 --- a/docs/reference/kerr.sugarcane.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of sugarcane, 4 fields

    - - -
    - -
    -

    Uniformity trials of sugarcane, 4 fields

    -
    - - 
    data("kerr.sugarcane.uniformity")
    - - -

    Format

    - -

    A data frame with 564 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds per plot

    -
    trial

    trial number

    - -
    - -

    Details

    - -

    Experiment conducted at the Sugar Experiment Station, Brisbane, Queensland, Australia in 1937.

    -

    Four trials were harvested, each 12 plots by 12 plots, each plot 19 feet by 19 feet (one field used 18-foot plots).

    -

    Trial 1 is plant cane.

    -

    Trial 2 is ratoon cane.

    -

    Trial 3 plant cane, irrigated.

    -

    Trial 4 is ratoon cane, irrigated.

    -

    Field length: 12 plots * 19 feet = 228 feet.

    -

    Field width: 12 plots * 19 feet = 228 feet.

    -

    Source

    - -

    H. W. Kerr (1939). Notes on plot technique. - Proc. Internat. Soc. Sugarcane Technol. 6, 764--778.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(kerr.sugarcane.uniformity)
-dat <- kerr.sugarcane.uniformity
-
-# match Kerr figure 4
-libs(desplot)
-desplot(dat, yield ~ col*row|trial,
-        flip=TRUE, aspect=1, # true aspect
-        main="kerr.sugarcane.uniformity")
-
    
-# CV matches Kerr table 2, page 768
-# aggregate(yield ~ trial, dat, FUN= function(x) round(100*sd(x)/mean(x),2))
-##   trial yield
-## 1    T1  7.95
-## 2    T2  9.30
-## 3    T3 10.37
-## 4    T4 13.76
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/khin.rice.uniformity-1.png b/docs/reference/khin.rice.uniformity-1.png deleted file mode 100644 index 82984839..00000000 Binary files a/docs/reference/khin.rice.uniformity-1.png and /dev/null differ diff --git a/docs/reference/khin.rice.uniformity.html b/docs/reference/khin.rice.uniformity.html deleted file mode 100644 index e8c1971d..00000000 --- a/docs/reference/khin.rice.uniformity.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Uniformity trial of rice — khin.rice.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of rice

    - - -
    - -
    -

    Uniformity trial of rice in Burma, 1948.

    -
    - - 
    data("khin.rice.uniformity")
    - - -

    Format

    - -

    A data frame with 1080 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, oz/plot

    - -
    - -

    Details

    - -

    A uniformity trial of rice. Conducted at the Mudon Agricultural - Station, Burma, in 1947-48. Basic plots were 3 feet square.

    -

    Field width: 30 plots * 3 feet.

    -

    Field length: 36 plots * 3 feet.

    -

    Source

    - -

    Khin, San. 1950. - Investigation into the relative costs of rice experiments based on the - efficiency of designs. - Dissertation: Imperial College of Tropical Agriculture (ICTA). - Appendix XV. - https://hdl.handle.net/2139/42422

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(khin.rice.uniformity)
-  dat <- khin.rice.uniformity
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE,
-          main="khin.rice.uniformity",
-          aspect=(36*3)/(30*3)) # true aspect
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kiesselbach.oats.uniformity-1.png b/docs/reference/kiesselbach.oats.uniformity-1.png deleted file mode 100644 index 659941eb..00000000 Binary files a/docs/reference/kiesselbach.oats.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kiesselbach.oats.uniformity.html b/docs/reference/kiesselbach.oats.uniformity.html deleted file mode 100644 index 3a3841ae..00000000 --- a/docs/reference/kiesselbach.oats.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trial of oats — kiesselbach.oats.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of oats

    - - -
    - -
    -

    Uniformity trial of oats at Nebraska in 1916.

    -
    - - 
    data("kiesselbach.oats.uniformity")
    - - -

    Format

    - -

    A data frame with 207 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield bu/ac

    - -
    - -

    Details

    - - - -

    Experiment conducted in 1916. Crop was Kerson oats. Each plot covered 1/30th acre. - Oats were drilled in plats 66 inches wide by 16 rods long. The drill - was 66 inches wide. Plats were separated by a space of 16 inches - between outside drill rows.

    -

    The source document includes three photographs of the field.

    -

    1 acre = 43560 sq feet

    -

    1/30 acre = 1452 sq feet = 16 rods * 16.5 ft/rod * 5.5 ft

    -

    Field width: 3 plats * 16 rods/plat * 16.5 ft/rod = 792 feet

    -

    Field length: 69 plats * 5.5 ft + 68 gaps * 1.33 feet = 469 feet

    -

    Source

    - -

    Kiesselbach, Theodore A. (1917). - Studies Concerning the Elimination of Experimental Error in Comparative Crop Tests. - University of Nebraska Agricultural Experiment Station Research Bulletin No. 13. - Pages 51-72. - https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop - https://digitalcommons.unl.edu/extensionhist/430/

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(kiesselbach.oats.uniformity)
-  dat <- kiesselbach.oats.uniformity
-  
-  range(dat$yield) # 56.7 92.8 match Kiesselbach p 64.
-
    #> [1] 56.7 92.8
    
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          tick=TRUE, flip=TRUE, aspect=792/469, # true aspect
-          main="kiesselbach.oats.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kreusler.maize-1.png b/docs/reference/kreusler.maize-1.png deleted file mode 100644 index 1a7120b7..00000000 Binary files a/docs/reference/kreusler.maize-1.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-10.png b/docs/reference/kreusler.maize-10.png deleted file mode 100644 index 97dcf54b..00000000 Binary files a/docs/reference/kreusler.maize-10.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-11.png b/docs/reference/kreusler.maize-11.png deleted file mode 100644 index f2feb006..00000000 Binary files a/docs/reference/kreusler.maize-11.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-12.png b/docs/reference/kreusler.maize-12.png deleted file mode 100644 index fb9267d9..00000000 Binary files a/docs/reference/kreusler.maize-12.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-13.png b/docs/reference/kreusler.maize-13.png deleted file mode 100644 index c78ab7ad..00000000 Binary files a/docs/reference/kreusler.maize-13.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-14.png b/docs/reference/kreusler.maize-14.png deleted file mode 100644 index 77af7482..00000000 Binary files a/docs/reference/kreusler.maize-14.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-15.png b/docs/reference/kreusler.maize-15.png deleted file mode 100644 index ccb1507e..00000000 Binary files a/docs/reference/kreusler.maize-15.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-16.png b/docs/reference/kreusler.maize-16.png deleted file mode 100644 index 2473ba14..00000000 Binary files a/docs/reference/kreusler.maize-16.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-17.png b/docs/reference/kreusler.maize-17.png deleted file mode 100644 index dcfbbd25..00000000 Binary files a/docs/reference/kreusler.maize-17.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-2.png b/docs/reference/kreusler.maize-2.png deleted file mode 100644 index 7f4dabc4..00000000 Binary files a/docs/reference/kreusler.maize-2.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-3.png b/docs/reference/kreusler.maize-3.png deleted file mode 100644 index a55d2c48..00000000 Binary files a/docs/reference/kreusler.maize-3.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-4.png b/docs/reference/kreusler.maize-4.png deleted file mode 100644 index 2b2efd0c..00000000 Binary files a/docs/reference/kreusler.maize-4.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-5.png b/docs/reference/kreusler.maize-5.png deleted file mode 100644 index 6a504281..00000000 Binary files a/docs/reference/kreusler.maize-5.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-6.png b/docs/reference/kreusler.maize-6.png deleted file mode 100644 index 8de8b4b7..00000000 Binary files a/docs/reference/kreusler.maize-6.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-7.png b/docs/reference/kreusler.maize-7.png deleted file mode 100644 index 8f5c5abb..00000000 Binary files a/docs/reference/kreusler.maize-7.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-8.png b/docs/reference/kreusler.maize-8.png deleted file mode 100644 index 764e8dc3..00000000 Binary files a/docs/reference/kreusler.maize-8.png and /dev/null differ diff --git a/docs/reference/kreusler.maize-9.png b/docs/reference/kreusler.maize-9.png deleted file mode 100644 index 7aa00a16..00000000 Binary files a/docs/reference/kreusler.maize-9.png and /dev/null differ diff --git a/docs/reference/kreusler.maize.html b/docs/reference/kreusler.maize.html deleted file mode 100644 index 5851054b..00000000 --- a/docs/reference/kreusler.maize.html +++ /dev/null @@ -1,318 +0,0 @@ - - - - - - - - -Growth of maize plants in Germany during 1875-1878 — kreusler.maize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Growth of maize plants in Germany during 1875-1878

    - - -
    - -
    -

    Growth of maize plants in Germany during 1875-1878.

    -
    - - 
    data("kreusler.maize")
    - - -

    Format

    - -

    A data frame with 165 observations on the following 17 variables.

    -
    gen

    genotype

    -
    year

    year

    -
    date

    calendar date

    -
    raindays

    number of days of rain per week (zahl der regenstage)

    -
    rain

    rain amount (mm)

    -
    temp

    temperature mean (deg C) (temperatur mittel)

    -
    parentseed

    weight of parent seed (g) (alte korner)

    -
    roots

    weight of roots (g) (wurzel)

    -
    leaves

    weight of leaves (g) (blatter)

    -
    stem

    weight of stem (g) (stengel)

    -
    tassel

    weight of tassel (g) (blutenstande)

    -
    grain

    weight of grain (korner)

    -
    plantweight

    weight of entire plant (ganze pflanze)

    -
    plantheight

    plant height (cm) (mittlere hohe der pflanzen)

    -
    leafcount

    number of leaves (anzahl der blatter)

    -
    leafarea

    leaf area (cm^2) (flachenmaass der blatter)

    - -
    - -

    Details

    - - - -

    Experiments were performed at Poppelsdorf, Germany (near Bonn) during -the years 1875 to 1878. Observations were collected weekly throughout -the growing season.

    -

    Five varieties were grown in 1875. Two in 1876, and one in 1877 and 1878.

    -

    The plants were selected by eye as representative, with the number of -plants chosen decreasing during the growing season. For example, the -dry-weight data was based on the following number of plants:

    -

    In 1875 the number sampled began at 20 and dropped to 10.

    -

    In 1876 the number sampled began at 45 and dropped to 24.

    -

    In 1877 the number sampled began at 90 and dropped to 36.

    -

    In 1878 the number sampled began at 120 and dropped to 40.

    -

    Most of the observations included fresh weight and dry weight of entire -plants, along with leaf area, date of inflorescence, fertilization, and -kernel development.

    -

    The data of Hornberger 71 are the same as Kreusler/Hornberger, but more -complete.

    -

    The temperature data was originally given in degrees Reaumur in 1875 and -1876, and degrees Celsius in 1877 and 1878. All temperatures in this -data are degrees Celsius. Note: deg C = 1.25 deg R. Briggs, Kidd & -West (1920) give all temperature in Celsius.

    -

    Source

    - - - -

    The 1875-1876 data are from:

    -

    A. Prehn & G. Becker. (1878) - Jahresbericht fur Agrikultur-chemie, Vol 20, p. 216-220. - https://books.google.com/books?id=ZfxNAAAAYAAJ&pg=216

    -

    The 1877 data are from:

    -

    A. Kreusler, A. Prehn, Hornberger. (1880) - Jahresbericht fur Agrikultur-Chemie, Vol 21, p 248. - https://books.google.com/books?id=U3IYAQAAIAAJ&pg=248

    -

    The 1878 data are from:

    -

    U. Kreusler, A. Prehn, R. Hornberger. (1880). - Jahresbericht fur Agrikultur-Chemie, Vol 22, p. 211. - https://books.google.com/books?id=9HIYAQAAIAAJ&pg=211

    -

    Dry plant weight and leaf area for all genotypes and years are - repeated by:

    -

    G. E. Briggs, Franklin Kidd, Cyril West. (1920). - A Quantitative Analysis of Plant Growth. Part I. - Annals of Applied Biology, 7, 103-123.

    -

    G. E. Briggs, Franklin Kidd, Cyril West. (1920). - A Quantitative Analysis of Plant Growth. Part II. - Annals of Applied Biology, 7, 202-223.

    -

    References

    - -

    Roderick Hunt, G. Clifford Evans. 1980. - Classical Data on the Growth of Maize: Curve Fitting With Statistical Analysis. - New Phytol, 86, 155-180.

    - -

    Examples

    - 
    
-data(kreusler.maize)
-dat <- kreusler.maize
-dat$date2 <- as.Date(dat$date,"%d %b %Y")
-dat$doy <- as.numeric(strftime(dat$date2, format="%j"))
-
-# Hunt & Evans Fig 2a
-libs(lattice)
-xyplot(log10(plantweight)~doy|factor(year), data=dat, group=gen,
-       type=c('p','smooth'), span=.4, as.table=TRUE, 
-       xlab="Day of year", main="kreusler.maize - growth of maize",
-       auto.key=list(columns=5))
-
    
-# \dontrun{
-
-# Hunt & Evans Fig 2b
-xyplot(log10(plantweight)~doy|gen, data=dat, group=factor(year),
-       type=c('p','smooth'), span=.5, as.table=TRUE, 
-       xlab="Day of year",
-       auto.key=list(columns=4))
-
    # Hunt & Evans Fig 3a
-xyplot(log10(leafarea)~doy|factor(year), data=dat, group=gen,
-       type=c('p','smooth'), span=.5, as.table=TRUE, 
-       xlab="Day of year",
-       auto.key=list(columns=5))
-
    # Hunt & Evans Fig 3a
-xyplot(log10(leafarea)~doy|gen, data=dat, group=factor(year),
-       type=c('p','smooth'), span=.5, as.table=TRUE, 
-       xlab="Day of year",
-       auto.key=list(columns=4))
-
    
-# All traits
-  
-xyplot(raindays~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(rain~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(temp~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(parentseed~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(roots~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(leaves~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(stem~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(grain~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(plantweight~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(plantheight~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(leafcount~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(leafarea~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    xyplot(tassel~doy|factor(year), data=dat, group=gen,
-       type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4))
-
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kristensen.barley.uniformity-1.png b/docs/reference/kristensen.barley.uniformity-1.png deleted file mode 100644 index e5df108c..00000000 Binary files a/docs/reference/kristensen.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kristensen.barley.uniformity.html b/docs/reference/kristensen.barley.uniformity.html deleted file mode 100644 index dcf75ac6..00000000 --- a/docs/reference/kristensen.barley.uniformity.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Uniformity trial of barley — kristensen.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of barley

    - - -
    - -
    -

    Uniformity trial of barley conducted in Denmark, 1905.

    -
    - - 
    data("kristensen.barley.uniformity")
    - - -

    Format

    - -

    A data frame with 718 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, hectograms/plot

    - -
    - -

    Details

    - -

    Experiment conducted in 1905 at Askov, Denmark. Harvested plot size was - 10 x 14 'alen', 6.24 x 8.79 meters. The soil was uniform, but an - attack of mildew spread from an adjacent field. Yield is measured in - hectograms/plot for straw and grain together. (Page 468).

    -

    Orientation of the plots dimensions is not clear from the text, but - the aspect used in the example below aligns well with Kristensen - figure 1.

    -

    Field width: 22 plots * 8.79 m

    -

    Field length: 11 plots * 6.24 m

    -

    Notes from Kristensen: Fig 5 is a 3x3 moving average, Fig 6 is - deviation from the trend, Fig 7 is the field average added to the - deviation. Fig 13 is another uniformity trial of barley in 1924, Fig - 14 is a uniformity trial of oats in 1924.

    -

    Source

    - -

    R. K. Kristensen (1925). - Anlaeg og Opgoerelse af Markforsoeg. - Tidsskrift for landbrugets planteavl, Vol 31, 464-494. - Fig 1, pg. 467. - https://dca.au.dk/publikationer/historiske/planteavl/

    -

    References

    - -

    J. Neyman, K. Iwaszkiewicz, St. Kolodziejczyk. (1935). - Statistical Problems in Agricultural Experimentation. - Supplement to the Journal of the Royal Statistical Society, - Vol. 2, No. 2 (1935), pp. 107-180. - https://doi.org/10.2307/2983637

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(kristensen.barley.uniformity)
-dat <- kristensen.barley.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, aspect=(11*6.24)/(22*8.79),
-        main="kristensen.barley.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/kulkarni.sorghum.uniformity-1.png b/docs/reference/kulkarni.sorghum.uniformity-1.png deleted file mode 100644 index f72818d8..00000000 Binary files a/docs/reference/kulkarni.sorghum.uniformity-1.png and /dev/null differ diff --git a/docs/reference/kulkarni.sorghum.uniformity-2.png b/docs/reference/kulkarni.sorghum.uniformity-2.png deleted file mode 100644 index 1572ca10..00000000 Binary files a/docs/reference/kulkarni.sorghum.uniformity-2.png and /dev/null differ diff --git a/docs/reference/kulkarni.sorghum.uniformity.html b/docs/reference/kulkarni.sorghum.uniformity.html deleted file mode 100644 index 7997acaa..00000000 --- a/docs/reference/kulkarni.sorghum.uniformity.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - - -Uniformity trial of sorghum — kulkarni.sorghum.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sorghum

    - - -
    - -
    -

    Uniformity trial of sorghum in India, 3 years on the same plots 1930-1932.

    -
    - - 
    data("kulkarni.sorghum.uniformity")
    - - -

    Format

    - -

    A data frame with 480 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    grain yield, tolas per plot

    -
    year

    year

    - -
    - -

    Details

    - -

    The experiment was conducted in the Sholapur district in India for - three consecutive years in 1930-1932.

    -

    One acre of land (290 ft x 150 ft) was chosen in the midst of a bigger area - (plot 13 on the Mohol Plot) for sowing to sorghum. It was harvested in plots of 1/160 acre - (72 ft 6 in x 3 ft 9 in) each containing three rows of plants 15 in. apart. - The 160 plots were arranged in forty rows of four columns, and the yields were - measured in tolas. The plot division was kept intact for three years, and the - yields of the 160 plots are available for three consecutive harvests. The original - data are given in Appendix I.

    -

    Field width: 4 plots * 72.5 feet = 290 feet

    -

    Field length: 40 plots * 3.75 feet = 150 feet

    -

    Conclusions: "Thus, highly narrow strips of plots (length much greater than breadth) lead - to greater precision than plots of same area but much wider and not so narrow."

    -

    Correlation of plots from year to years was low.

    -

    Source

    - -

    Kulkarni, R. K., Bose, S. S., and Mahalanobis, P. C. (1936). - The influence of shape and size of plots on the effective precision of field experiments with sorghum. - Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. - https://archive.org/details/in.ernet.dli.2015.271737

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(kulkarni.sorghum.uniformity)
-dat <- kulkarni.sorghum.uniformity
-
-# match means on page 462
-# tapply(dat$yield, dat$year, mean)
-#     1930     1931     1932 
-# 116.2875  67.2250 126.3688 
-
-libs(reshape2)
-libs(lattice)
-dmat <- acast(dat, row+col ~ year, value.var="yield")
-splom(dmat, main="kulkarni.sorghum.uniformity")
-
    cor(dmat)
-
    #>           1930      1931      1932
-#> 1930 1.0000000 0.4192544 0.1683767
-#> 1931 0.4192544 1.0000000 0.2665319
-#> 1932 0.1683767 0.2665319 1.0000000
    
-libs(desplot)
-desplot(dat, yield ~ col*row|year,
-        flip=TRUE, aspect=150/290,
-        main="kulkarni.sorghum.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lambert.soiltemp-1.png b/docs/reference/lambert.soiltemp-1.png deleted file mode 100644 index 07496f58..00000000 Binary files a/docs/reference/lambert.soiltemp-1.png and /dev/null differ diff --git a/docs/reference/lambert.soiltemp.html b/docs/reference/lambert.soiltemp.html deleted file mode 100644 index 5433d285..00000000 --- a/docs/reference/lambert.soiltemp.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Average monthly soil temperature near Zurich — lambert.soiltemp • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Average monthly soil temperature near Zurich

    - - -
    - -
    -

    Average monthly soil temperature near Zurich, at seven depths, - averaged over four years.

    -
    - - - -

    Format

    - -

    A data frame with 84 observations on the following 3 variables.

    -
    month

    month

    -
    depth

    depth in soil (feet)

    -
    temp

    temperature (the units are "du Crest")

    - -
    - -

    Details

    - -

    This is one of the earliest time series in scientific literature.

    -

    These data show the monthly soil temperature near Zurich, averaged - over four years (beginning in 1762), at 7 different depths.

    -

    The temperature measurements are related to the 'du Crest' scale. - (The measurements do not seem to be exactly according to the du Crest - scale. If you can read German, use the Google books link to see if - you can figure out why.) Even the scale on Lambert's own graph - doesn't match the data.

    -

    Greater depths show less variation and a greater lag in temperature - responsiveness to the air temperature.

    -

    This data also appears in Pedometrics, issue 23, December 2007. But, - the formula for converting the temperature does not make sense and the - data in Table 1 do not directly match the corresponding figure.

    -

    Source

    - -

    Johann Heinrich Lambert (1779), Pyrometrie. Page 358. - https://books.google.com/books?id=G5I_AAAAcAAJ&pg=PA358

    -

    Graph: https://www.fisme.science.uu.nl/wiskrant/artikelen/hist_grafieken/begin/images/pyrometrie.gif

    - -

    Examples

    - 
    
-library(agridat)
-# Reproduce Lambert figure 39.
-
-data(lambert.soiltemp)
-dat <- lambert.soiltemp
-
-# Make 3 cycles of the data so that the loess line bends back up at
-# month 1 and month 12
-dat <- rbind(dat, transform(dat, month=month-12),
-             transform(dat, month=month+12))
-libs(lattice)
-xyplot(temp ~ month, dat, group=depth, type=c('p','smooth'),
-       main="lambert.soiltemp",
-       xlim=c(-3,15), ylab="Soil temperature (du Crest) at depth (feet)",
-       span=.2, auto.key=list(columns=4))
-
    
-# To do: Find a good model for this data
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lander.multi.uniformity-1.png b/docs/reference/lander.multi.uniformity-1.png deleted file mode 100644 index 8048eb76..00000000 Binary files a/docs/reference/lander.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/lander.multi.uniformity.html b/docs/reference/lander.multi.uniformity.html deleted file mode 100644 index 40ec2243..00000000 --- a/docs/reference/lander.multi.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of wheat and chari, 4 years on the same land.

    - - -
    - -
    -

    Uniformity trials of wheat and chari, 4 years on the same land, in India.

    -
    - - 
    data("lander.multi.uniformity")
    - - -

    Format

    - -

    A data frame with 780 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, maunds per plot

    -
    year

    year

    -
    crop

    crop

    - -
    - -

    Details

    - - - -

    Note, "chari" in this paper is Andropogon Sorghum, and "wheat" - is Triticum vulgare.

    -

    Uniformity trials carried out at Rawalpindi, India.

    -

    The area consisted of 5 fields (D4,D5,D6,D7,D8), each 5 acres in - size. Each of these 5 fields was divided into three sub-divisions A, - B, C, by means of two strong bunds each 5 feet wide. These 3 - sub-divisions were divided into 5 blocks, each consisting of 13 - experimental plots with 14 non-experiment strips 5 feet wide - separating the plots from the other. The dimensions of the plot were - 207 ft 5 in by 19 ft 1 in.

    -

    The same land was used for 4 consecutive crops. The first crop was - wheat, followed by chari/sorghum, followed by wheat 2 times.

    -

    Field width: 207.42 * 5 plots = 1037.1 feet

    -

    Field length: (19.08+5)*39 rows = 939.12 feet

    -

    Conclusions: It is evident, therefore, that soil heterogenity as - revealed by any one crop cannot be a true index of the subsequent - behavior of that area with respect to other crops. Even the same crop - raised in different seasons has not shown any constancy as regards - soil heterogeneity.

    -

    Source

    - -

    Lander, P. E. et al. (1938). - Soil Uniformity Trials in the Punjab I. - Ind. J. Agr. Sci. 8:271-307.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lander.multi.uniformity)
-dat <- lander.multi.uniformity
-
-# Yearly means, similar to Lander table 7
-## filter(dat) 
-## 1 1929   18.1
-## 2 1930   58.3
-## 3 1931   22.8
-## 4 1932   14.1
-
-# heatmaps for all years
-libs(desplot)
-dat$year <- factor(dat$year)
-desplot(dat, yield ~ col*row|year,
-        flip=TRUE, aspect=(1037.1/939.12),
-        main="lander.multi.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lasrosas.corn-1.png b/docs/reference/lasrosas.corn-1.png deleted file mode 100644 index 70a7585d..00000000 Binary files a/docs/reference/lasrosas.corn-1.png and /dev/null differ diff --git a/docs/reference/lasrosas.corn-2.png b/docs/reference/lasrosas.corn-2.png deleted file mode 100644 index d7f1b429..00000000 Binary files a/docs/reference/lasrosas.corn-2.png and /dev/null differ diff --git a/docs/reference/lasrosas.corn-3.png b/docs/reference/lasrosas.corn-3.png deleted file mode 100644 index e531c828..00000000 Binary files a/docs/reference/lasrosas.corn-3.png and /dev/null differ diff --git a/docs/reference/lasrosas.corn.html b/docs/reference/lasrosas.corn.html deleted file mode 100644 index c05b57f8..00000000 --- a/docs/reference/lasrosas.corn.html +++ /dev/null @@ -1,290 +0,0 @@ - - - - - - - - -Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Yield monitor data for a corn field in Argentina with variable nitrogen.

    - - -
    - -
    -

    Yield monitor data for a corn field in Argentina with variable nitrogen.

    -
    - - 
    data("lasrosas.corn")
    - - -

    Format

    - -

    A data frame with 3443 observations on the following 8 variables.

    -
    year

    year, 1999 or 2001

    -
    lat

    latitude

    -
    long

    longitude

    -
    yield

    yield, quintals/ha

    -
    nitro

    nitrogen fertilizer, kg/ha

    -
    topo

    topographic factor

    -
    bv

    brightness value (proxy for low organic matter content)

    -
    rep

    rep factor

    -
    nf

    nitrogen as a factor, N0-N4

    - -
    - -

    Details

    - - - -

    Corn yield and nitrogen fertilizer treatment with field characteristics - for the Las Rosas farm, Rio Cuarto, Cordoba, Argentina.

    -

    Data has 6 nitro treatments, 3 reps, in strips.

    -

    Data collected using yield monitor, for harvests in 1999 and 2001.

    -

    The points within each long strip have been averaged so that the - distance between points _within_ a strip is the same as the distance - _between_ strips (9.8 meters).

    -

    The topographic factor a factor with levels W = West slope, HT = - Hilltop, E = East slope, LO = Low East.

    -

    The 'rep' factor in this data was added by hand and did not appear in - the original data.

    -

    Slightly different levels of nitrogen were used in the two years, so - the nitrogen factor 'nf' was created to have common levels across - years.

    -

    Published descriptions of the data describe the experiment design as - having randomized nitrogen treatments. The nitrogen treatments were - randomized within one rep, but the same randomization was used in the - other two reps.

    -

    Anselin et al. used corn grain price of $6.85/quintal and nitrogen - cost of $0.4348/kg.

    -

    The corners of the field in 1999 are: - https://www.google.com/maps/place/-33.0501258,-63.8488636 - https://www.google.com/maps/place/-33.05229635,-63.84181819

    -

    Anselin et al. found a significant response to nitrogen for slope. - However, Bongiovanni and Lowenberg-DeBoer (2002) found that slope - position was NOT significant in 2001.

    - -

    Used with permission of the ASU GeoDa Center.

    -

    Source

    - -

    The Las Rosas data files were obtained from - https://geodacenter.asu.edu/sdata - and converted from ESRI shape files to a flat data.frame.

    -

    References

    - -

    Bongiovanni and Lowenberg-DeBoer (2000). - Nitrogen management in corn with a spatial regression model. - Proceedings of the Fifth International Conference on Precision - Agriculture.

    -

    Anselin, L., R. Bongiovanni, J. Lowenberg-DeBoer (2004). - A spatial econometric approach to the economics of site-specific - nitrogen management in corn production. - American Journal of Agricultural Economics, 86, 675--687. - https://doi.org/10.1111/j.0002-9092.2004.00610.x

    -

    Lambert, Lowenberg-Deboer, Bongiovanni (2004). - A Comparison of Four Spatial Regression Models for Yield Monitor Data: - A Case Study from Argentina. - Precision Agriculture, 5, 579-600. - https://doi.org/10.1007/s11119-004-6344-3

    -

    Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei - Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). - Novel approach to the analysis of spatially-varying treatment effects - in on-farm experiments. - Field Crops Research, 255, 15 September 2020, 107783. - https://doi.org/10.1016/j.fcr.2020.107783

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lasrosas.corn)
-dat <- lasrosas.corn
-
-# yield map
-libs(lattice,latticeExtra)  # for panel.levelplot.points
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(yield ~ long*lat|factor(year), data=dat, 
-          main="lasrosas.corn grain yield", xlab="Longitude", ylab="Latitude",
-          scales=list(alternating=FALSE),
-          prepanel = prepanel.default.xyplot,
-          panel = panel.levelplot.points,
-          type = c("p", "g"), aspect = "iso", col.regions=redblue)
-
    
-
-d1 <- subset(dat, year==1999)
-
-# Experiment design
-xyplot(lat~long, data=d1, col=as.numeric(as.factor(d1$nitro)), pch=d1$topo,
-       main="lasrosas.corn experiment layout 1999")
-
    
-# A quadratic response to nitrogen is suggested
-xyplot(yield~nitro|topo, data=d1, type=c('p','smooth'), layout=c(4,1),
-       main="lasrosas.corn yield by topographic zone 1999")
-
    
-# Full-field quadratic response to nitrogen.  Similar to Bongiovanni 2000,
-# table 1.
-m1 <- lm(yield ~ 1 + nitro + I(nitro^2), data=d1, subset=year==1999)
-coef(m1)
-
    #>   (Intercept)         nitro    I(nitro^2) 
-#> 59.2533084708  0.1130069973 -0.0003204139 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lavoranti.eucalyptus-1.png b/docs/reference/lavoranti.eucalyptus-1.png deleted file mode 100644 index 53e33002..00000000 Binary files a/docs/reference/lavoranti.eucalyptus-1.png and /dev/null differ diff --git a/docs/reference/lavoranti.eucalyptus.html b/docs/reference/lavoranti.eucalyptus.html deleted file mode 100644 index a91cc764..00000000 --- a/docs/reference/lavoranti.eucalyptus.html +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - -Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Height of Eucalyptus trees in southern Brazil

    - - -
    - -
    -

    Height of Eucalyptus trees in southern Brazil

    -
    - - - -

    Format

    - -

    A data frame with 490 observations on the following 4 variables.

    -
    gen

    genotype (progeny) factor

    -
    origin

    origin of progeny

    -
    loc

    location

    -
    height

    height, meters

    - -
    - -

    Details

    - -

    The genotypes originated from three different locations in Queensland, - Australia, and were tested in southern Brazil. The experiment was - conducted as a randomized complete block design with 6 plants per plot - and 10 blocks. Mean tree height is reported.

    -

    The testing locations are described in the following table:

    - - - - - - - - -
    LocCityLat (S)Long (W)AltitudeAvg min tempAvg max tempAvg temp (C)Precip (mm)
    L1Barra Ribeiro, RS30.3351.2330925191400
    L2Telemaco Borba, PR24.2520.488501126191480
    L3Boa Experanca de Sul, SP21.9548.535401523211300
    L4Guanhaes, MG18.66439001424191600
    L5Ipatinga, MG19.2542.332501524221250
    L6Aracruz, ES19.840.28501526241360
    L7Cacapva, SP23.0545.766501424201260
    - - -

    Arciniegas-Alarcon (2010) used the 'Ravenshoe' subset of the data to - illustrate imputation of missing values.

    -

    Source

    - -

    O J Lavoranti (2003). Estabilidade e adaptabilidade fenotipica - atraves da reamostragem bootstrap no modelo AMMI, PhD thesis, - University of Sao Paulo, Brazil.

    -

    References

    - -

    Arciniegas-Alarcon, S. and Garcia-Pena, M. and dos Santos Dias, - C.T. and Krzanowski, W.J. (2010). An alternative methodology for - imputing missing data in trials with genotype-by-environment - interaction, Biometrical Letters, 47, 1-14. - https://doi.org/10.2478/bile-2014-0006

    - -

    Examples

    - 
    # \dontrun{
-
-# Arciniegas-Alarcon et al use SVD and regression to estimate missing values.
-# Partition the matrix X as a missing value xm, row vector xr1, column
-# vector xc1, and submatrix X11
-# X = [ xm  xr1 ]
-#     [ xc1 X11 ] and let X11 = UDV'.
-# Estimate the missing value xm = xr1 V D^{-1} U' xc1
-
-data(lavoranti.eucalyptus)
-dat <- lavoranti.eucalyptus
-
-libs(lattice)
-levelplot(height~loc*gen, dat, main="lavoranti.eucalyptus - GxE heatmap")
-
    
-dat <- droplevels(subset(dat, origin=="Ravenshoe"))
-libs(reshape2)
-dat <- acast(dat, gen~loc, value.var='height')
-
-dat[1,1] <- NA
-x11 <- dat[-1,][,-1]
-X11.svd <- svd(x11)
-xc1 <- dat[-1,][,1]
-xr1 <- dat[,-1][1,]
-xm <- xr1 
-xm # = 18.29, Original value was 17.4
-
    #>    L2    L3    L4    L5    L6    L7 
-#> 25.00 18.67 20.61 13.88 19.84 14.72 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/laycock.tea.uniformity-1.png b/docs/reference/laycock.tea.uniformity-1.png deleted file mode 100644 index 85ff21d0..00000000 Binary files a/docs/reference/laycock.tea.uniformity-1.png and /dev/null differ diff --git a/docs/reference/laycock.tea.uniformity.html b/docs/reference/laycock.tea.uniformity.html deleted file mode 100644 index 9aa844ce..00000000 --- a/docs/reference/laycock.tea.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trials of tea — laycock.tea.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of tea

    - - -
    - -
    -

    Uniformity trials of tea

    -
    - - 
    data("laycock.tea.uniformity")
    - - -

    Format

    - -

    A data frame with 54 observations on the following 4 variables.

    -
    loc

    location, L1 or L2

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Actual physical dimensions for the tea shrubs are not given, so we use - an estimate of four feet square for each shrub (which is similar to - the eden.tea.uniformity experiment).

    -

    Location 1 (Laycock, page 108) is at the Research Station, - Nyasaland. Plots were 10 by 15 bushes, harvested 23 times in 1942.

    -

    Field length: 8 plots * 10 bushes * 4 feet = 320 feet.

    -

    Field width: 4 plots * 15 bushes * 4 feet = 240 feet.

    -

    Location 2 (Laycock page 110) is at Mianga Estate, Nyasaland. Plots - were 9 by 11 bushes, harvested 18 times in 1951/52.

    -

    Field length: 9 plots * 9 bushes * 4 feet = 324 feet.

    -

    Field width: 6 plots * 11 bushes * 4 feet = 264 feet.

    -

    Source

    - -

    Laycock, D. H. (1955). - The effect of plot shape in reducing the errors of tea experiments. - Tropical Agriculture, 32, 107-114.

    -

    References

    - -

    Zimmerman, Dale L., and David A. Harville. (1991). - A random field approach to the analysis of field-plot experiments - and other spatial experiments. - Biometrics, 47, 223-239.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(laycock.tea.uniformity)
-dat <- laycock.tea.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row|loc,
-        flip=TRUE, aspect=322/252, # average of 2 locs
-        main="laycock.tea.uniformity")
-
    
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lee.potatoblight-1.png b/docs/reference/lee.potatoblight-1.png deleted file mode 100644 index c35e37bd..00000000 Binary files a/docs/reference/lee.potatoblight-1.png and /dev/null differ diff --git a/docs/reference/lee.potatoblight-2.png b/docs/reference/lee.potatoblight-2.png deleted file mode 100644 index bb449f09..00000000 Binary files a/docs/reference/lee.potatoblight-2.png and /dev/null differ diff --git a/docs/reference/lee.potatoblight.html b/docs/reference/lee.potatoblight.html deleted file mode 100644 index 58bfaa6a..00000000 --- a/docs/reference/lee.potatoblight.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - - -Repeated measurements of resistance to potato blight — lee.potatoblight • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Repeated measurements of resistance to potato blight

    - - -
    - -
    -

    Repeated measurements of resistance to potato blight.

    -
    - - 
    data("lee.potatoblight")
    - - -

    Format

    - -

    A data frame with 14570 observations on the following 7 variables.

    -
    year

    planting year

    -
    gen

    genotype / cultivar factor

    -
    col

    column

    -
    row

    row

    -
    rep

    replicate block (numeric)

    -
    date

    date for data collection

    -
    y

    score 1-9 for blight resistance

    - -
    - -

    Details

    - - - -

    These data werre collected from biennial screening trials conducted by -the New Zealand Institute of Crop and Food Research at the Pukekohe -Field Station. The trials evaluate the resistance of potato cultivars -to late blight caused by the fungus Phytophthora infestans. In -each trial, the damage to necrotic tissue was rated on a 1-9 scale at -multiple time points during the growing season.

    -

    Lee (2009) used a Bayesian model that extends the ordinal regression of -McCullagh to include spatial variation and sigmoid logistic curves to model -the time dependence of repeated measurements on the same plot.

    -

    Data from 1989 were not included due to a different trial setup being -used. All the trials here were laid out as latinized row-column -designs with 4 or 5 reps. Each plot consisted of four seed tubers -planted with two Ilam Hardy spread plants in a single row 2 meters long -with 76 centimeter spacing between rows.

    -

    In 1997, 18 plots were lost due to flooding. In 2001, by the end of the -season most plants were nearly dead.

    -

    Note, in plant-breeding, it is common to use a "breeder code" for each genotype, -which after several years of testing is changed to a registered -commercial variety name. -For this R package, the Potato Pedigree Database, -https://www.plantbreeding.wur.nl/potatopedigree/reverselookup.php, -was used to change breeder codes (in early testing) to the variety names -used in later testing. For example, among the changes made were the -following:

    - - - - - - - - - - - -
    Driver287.12
    Kiwitea064/56
    Gladiator1308.66
    Karaka221.17
    Kiwitea064.56 maybe 064.54
    Moonlight511.1
    Pacific177.3
    Red Rascal1830.11
    Rua155.05
    Summit517.12
    White Delight1949.64
    - - - -

    Used with permission of Arier Chi-Lun Lee and John Anderson.

    -

    Data retrieved from - https://researchspace.auckland.ac.nz/handle/2292/5240.

    -

    Licensed via Open Database License 1.0. (allows sub-licensing). - See: https://opendatacommons.org/licenses/dbcl/1.0/

    -

    Source

    - -

    Lee, Arier Chi-Lun (2009). - Random effects models for ordinal data. - Ph.D. thesis, The University of Auckland. - https://researchspace.auckland.ac.nz/handle/2292/4544.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lee.potatoblight)
-dat <- lee.potatoblight
-
-# Common cultivars across years.
-# Based on code from here: https://stackoverflow.com/questions/20709808
-gg <- tapply(dat$gen, dat$year, function(x) as.character(unique(x)))
-tab <- outer(1:11, 1:11,
-             Vectorize(function(a, b) length(Reduce(intersect, gg[c(a, b)]))))
-head(tab) # Matches Lee page 27.
-
    #>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
-#> [1,]   20   10    7    5    3    2    3    2    3     3     2
-#> [2,]   10   30   17    5    4    3    4    4    5     4     2
-#> [3,]    7   17   35    9    6    3    4    5    6     4     3
-#> [4,]    5    5    9   35   16    8    9   14   15    13    11
-#> [5,]    3    4    6   16   40   12   11   18   18    16    14
-#> [6,]    2    3    3    8   12   30   13   10    9     8     7
    ##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
-## [1,]   20   10    7    5    3    2    3    2    3     3     2
-## [2,]   10   30   17    5    4    3    4    4    5     4     2
-## [3,]    7   17   35    9    6    3    4    5    6     4     3
-## [4,]    5    5    9   35   16    8    9   14   15    13    11
-## [5,]    3    4    6   16   40   12   11   18   18    16    14
-
-
-# Note the progression to lower scores as time passes in each year
-skp <- c(rep(0,10),
-         rep(0,7),1,1,1,
-         rep(0,8),1,1,
-         rep(0,6),1,1,1,1,
-         rep(0,5),1,1,1,1,1,
-         rep(0,5),1,1,1,1,1,
-         rep(0,6),1,1,1,1,
-         rep(0,5),1,1,1,1,1,
-         rep(0,5),1,1,1,1,1,
-         rep(0,5),1,1,1,1,1)
-
-libs(desplot)
-desplot(dat, y ~ col*row|date,
-        ylab="Year of testing", # unknown aspect
-        layout=c(10,11),skip=as.logical(skp),
-        main="lee.potatoblight - maps of blight resistance over time")
-
    
-
-# 1983 only.  I.Hardy succumbs to blight quickly
-libs(lattice)
-xyplot(y ~ date|gen, dat, subset=year==1983, group=rep,
-       xlab="Date", ylab="Blight resistance score",
-       main="lee.potatoblight 1983", as.table=TRUE,
-       auto.key=list(columns=5),
-       scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lehmann.millet.uniformity-1.png b/docs/reference/lehmann.millet.uniformity-1.png deleted file mode 100644 index 4df16a7f..00000000 Binary files a/docs/reference/lehmann.millet.uniformity-1.png and /dev/null differ diff --git a/docs/reference/lehmann.millet.uniformity-2.png b/docs/reference/lehmann.millet.uniformity-2.png deleted file mode 100644 index 52e60f71..00000000 Binary files a/docs/reference/lehmann.millet.uniformity-2.png and /dev/null differ diff --git a/docs/reference/lehmann.millet.uniformity.html b/docs/reference/lehmann.millet.uniformity.html deleted file mode 100644 index 3e4c31af..00000000 --- a/docs/reference/lehmann.millet.uniformity.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - - - -Uniformity trial of millet in India — lehmann.millet.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of millet in India

    - - -
    - -
    -

    Uniformity trial of millet in India, 3 years on same land.

    -
    - - 
    data("lehmann.millet.uniformity")
    - - -

    Format

    - -

    A data frame with 396 observations on the following 5 variables.

    -
    year

    year

    -
    plot

    plot (row)

    -
    range

    range (column)

    -
    yield

    grain yield (pounds)

    -
    total

    total crop yield (pounds)

    - -
    - -

    Details

    - - - -

    The 6th report has a map (only partially scanned in the pdf). -The ninth report, pages 1-10 has comments. Pages 36-39 have data: Table 1 has grain yield, table 2 total yield.

    -

    Experiment farm near Bangalore. The plots are 1/10 acre, each 50 links wide and 200 links long. [6th report, p. 2]

    -

    The season of 1906 was abnormally wet compared with 1905 and 1907. [9th report]

    -

    Source

    - -

    Department of Agriculture, Mysore State. - [2nd-9th] Annual Report of the Agricultural Chemist. - https://books.google.com/books?id=u_dHAAAAYAAJ

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(lehmann.millet.uniformity)
-  dat <- lehmann.millet.uniformity
-
-  libs(desplot)
-  dat$year = factor(dat$year)
-  desplot(dat, yield ~ range*plot|year,
-          aspect=(22*50)/(6*200),
-          main="lehmann.millet.uniformity",
-          flip=TRUE, tick=TRUE)
-
      desplot(dat, total ~ range*plot|year,
-          aspect=(22*50)/(6*200),
-          main="lehmann.millet.uniformity",
-          flip=TRUE, tick=TRUE)
-
      
-  # libs(dplyr)
-  # group_by(dat, year) 
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lehner.soybeanmold-1.png b/docs/reference/lehner.soybeanmold-1.png deleted file mode 100644 index 1d028eb4..00000000 Binary files a/docs/reference/lehner.soybeanmold-1.png and /dev/null differ diff --git a/docs/reference/lehner.soybeanmold-2.png b/docs/reference/lehner.soybeanmold-2.png deleted file mode 100644 index 70ebb2ba..00000000 Binary files a/docs/reference/lehner.soybeanmold-2.png and /dev/null differ diff --git a/docs/reference/lehner.soybeanmold-3.png b/docs/reference/lehner.soybeanmold-3.png deleted file mode 100644 index 7696a3fb..00000000 Binary files a/docs/reference/lehner.soybeanmold-3.png and /dev/null differ diff --git a/docs/reference/lehner.soybeanmold.html b/docs/reference/lehner.soybeanmold.html deleted file mode 100644 index 9e97c895..00000000 --- a/docs/reference/lehner.soybeanmold.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - - - - -Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Yield, white mold, and sclerotia for soybeans in Brazil

    - - -
    - -
    -

    Yield, white mold, and sclerotia for soybeans in Brazil

    -
    - - 
    data("lehner.soybeanmold")
    - - -

    Format

    - -

    A data frame with 382 observations on the following 9 variables.

    -
    study

    study number

    -
    year

    year of harvest

    -
    loc

    location name

    -
    elev

    elevation

    -
    region

    region

    -
    trt

    treatment number

    -
    yield

    crop yield, kg/ha

    -
    mold

    white mold incidence, percent

    -
    sclerotia

    weight of sclerotia g/ha

    - -
    - -

    Details

    - -

    Data are the mean of 4 reps.

    -

    Original source (Portuguese) - https://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009-a-2012.pdf

    -

    Data included here via GPL3 license.

    -

    Source

    - -

    Lehner, M. S., Pethybridge, S. J., Meyer, M. C., & Del Ponte, - E. M. (2016). - Meta-analytic modelling of the incidence-yield and - incidence-sclerotial production relationships in soybean white mould - epidemics. - Plant Pathology. doi:10.1111/ppa.12590

    -

    References

    - -

    Full commented code and analysis - https://emdelponte.github.io/paper-white-mold-meta-analysis/

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lehner.soybeanmold)
-dat <- lehner.soybeanmold
-
-if(0){
-  op <- par(mfrow=c(2,2))
-  hist(dat$mold, main="White mold incidence")
-  hist(dat$yield, main="Yield")
-  hist(dat$sclerotia, main="Sclerotia weight")
-  par(op)
-}
-
-libs(lattice)
-xyplot(yield ~ mold|study, dat, type=c('p','r'),
-       main="lehner.soybeanmold")
-
    # xyplot(sclerotia ~ mold|study, dat, type=c('p','r'))
-
-# meta-analysis. Could use metafor package to construct the forest plot,
-# but latticeExtra is easy; ggplot is slow/clumsy
-libs(latticeExtra, metafor)
-
    #> Loading 'metafor' package (version 2.4-0). For an overview 
-#> and introduction to the package please type: help(metafor).
    #> 
-#> Attaching package: 'metafor'
    #> The following object is masked from 'package:ordinal':
-#> 
-#>     ranef
    #> The following object is masked from 'package:HH':
-#> 
-#>     vif
    #> The following object is masked from 'package:car':
-#> 
-#>     vif
    # calculate correlation & confidence for each loc
-cors <- split(dat, dat$study)
-cors <- sapply(cors,
-               FUN=function(X){
-                 res <- cor.test(X$yield, X$mold)
-                 c(res$estimate, res$parameter[1],
-                   conf.low=res$conf.int[1], conf.high=res$conf.int[2])
-               })
-cors <- as.data.frame(t(as.matrix(cors)))
-cors$study <- rownames(cors)
-# Fisher Z transform
-cors <- transform(cors, ri = cor)
-cors <- transform(cors, ni = df + 2)
-cors <- transform(cors,
-                  yi = 1/2 * log((1 + ri)/(1 - ri)),
-                  vi = 1/(ni - 3))
-# Overall correlation across studies
-overall <- rma.uni(yi, vi, method="ML", data=cors) # metafor package
-# back transform
-overall <- predict(overall, transf=transf.ztor)
-
-# weight and size for forest plot
-wi    <- 1/sqrt(cors$vi)
-size  <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi))
-
-# now the forest plot
-# must use latticeExtra::layer in case ggplot2 is also loaded
-segplot(factor(study) ~ conf.low+conf.high, data=cors,
-        draw.bands=FALSE, level=size, centers=ri, cex=size,
-        col.regions=colorRampPalette(c("gray85", "dodgerblue4")),
-        main="White mold vs. soybean yield",
-        xlab=paste("Study correlation, confidence, and study weight (blues)\n",
-                   "Overall (black)"),
-        ylab="Study ID") +
-  latticeExtra::layer(panel.abline(v=overall$pred, lwd=2)) +
-  latticeExtra::layer(panel.abline(v=c(overall$cr.lb, overall$cr.ub), lty=2, col="gray"))
-
    
-
-# Meta-analyses are typically used when the original data is not available.
-# Since the original data is available, a mixed model is probably better.
-libs(lme4)
-m1 <- lmer(yield ~ mold # overall slope
-           + (1+mold |study), # random intercept & slope per study
-           data=dat)
-
    #> Warning: Model failed to converge with max|grad| = 0.00587194 (tol = 0.002, component 1)
    summary(m1)
-
    #> Linear mixed model fit by REML ['lmerMod']
-#> Formula: yield ~ mold + (1 + mold | study)
-#>    Data: dat
-#> 
-#> REML criterion at convergence: 5293.1
-#> 
-#> Scaled residuals: 
-#>     Min      1Q  Median      3Q     Max 
-#> -3.7268 -0.6009 -0.0259  0.5045  3.2526 
-#> 
-#> Random effects:
-#>  Groups   Name        Variance  Std.Dev. Corr 
-#>  study    (Intercept) 614294.78 783.770       
-#>           mold            40.38   6.354  -0.30
-#>  Residual              36900.07 192.094       
-#> Number of obs: 382, groups:  study, 35
-#> 
-#> Fixed effects:
-#>             Estimate Std. Error t value
-#> (Intercept) 3456.609    134.241   25.75
-#> mold         -17.318      1.488  -11.64
-#> 
-#> Correlation of Fixed Effects:
-#>      (Intr)
-#> mold -0.303
-#> convergence code: 0
-#> Model failed to converge with max|grad| = 0.00587194 (tol = 0.002, component 1)
-#> 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lessman.sorghum.uniformity-1.png b/docs/reference/lessman.sorghum.uniformity-1.png deleted file mode 100644 index 17d054f0..00000000 Binary files a/docs/reference/lessman.sorghum.uniformity-1.png and /dev/null differ diff --git a/docs/reference/lessman.sorghum.uniformity-2.png b/docs/reference/lessman.sorghum.uniformity-2.png deleted file mode 100644 index c293d7f9..00000000 Binary files a/docs/reference/lessman.sorghum.uniformity-2.png and /dev/null differ diff --git a/docs/reference/lessman.sorghum.uniformity.html b/docs/reference/lessman.sorghum.uniformity.html deleted file mode 100644 index 0d76a634..00000000 --- a/docs/reference/lessman.sorghum.uniformity.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - - -Uniformity trial of sorghum — lessman.sorghum.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sorghum

    - - -
    - -
    -

    Uniformity trial of sorghum at Ames, Iowa, 1959.

    -
    - - 
    data("lessman.sorghum.uniformity")
    - - -

    Format

    - -

    A data frame with 2640 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, ounces

    - -
    - -

    Details

    - - - -

    The uniformity trial was conducted at the Agronomy Farm at Ames, Iowa, - in 1959. The field was planted to grain sorghum in rows spaces 40 - inches apart, thinned to a stand of three inches between plants. The - entire field was 48 rows (40 inches apart), each 300 feet long and - harvested in 5-foot lengths. Threshed grain was dried to 8-10 percent - moisture before weighing. Weights are ounces. Average yield for the - field was 95.3 bu/ac.

    -

    Field width: 48 rows * 40 inches / 12in/ft = 160 feet

    -

    Field length: 60 plots * 5 feet = 300 feet

    -

    Plot yields from the two outer rows on each side of the field were - omitted from the analysis.

    -

    CV values from this data do not quite match Lessman's value. The - first page of Table 17 was manually checked for correctness and there - were no problems with the optical character recognition (other than - obvious errors like 0/o).

    -

    Source

    - -

    Lessman, Koert James (1962). - Comparisons of methods for testing grain yield of sorghum. - Iowa State University. - Retrospective Theses and Dissertations. Paper 2063. Appendix Table 17. - https://lib.dr.iastate.edu/rtd/2063

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(lessman.sorghum.uniformity)
-  dat <- lessman.sorghum.uniformity
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          aspect=300/160, tick=TRUE, flip=TRUE, # true aspect
-          main="lessman.sorghum.uniformity")
-
    
-  # Omit outer two columns (called 'rows' by Lessman)
-  dat <- subset(dat, col > 2 & col < 47)
-  nrow(dat)
-
    #> [1] 2640
      var(dat$yield) # 9.09
-
    #> [1] 9.088291
      sd(dat$yield)/mean(dat$yield) # CV 9.2
-
    #> [1] 0.0924119
    
-  libs(reshape2)
-  libs(agricolae)
-  dmat <- acast(dat, row~col, value.var='yield')
-  index.smith(dmat,
-              main="lessman.sorghum.uniformity",
-              col="red") # Similar to Lessman Table 1
-
      # Lessman said that varying the width of plots did not have an appreciable
-  # effect on CV, and optimal row length was 3.2 basic plots, about 15-20
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/li.millet.uniformity-1.png b/docs/reference/li.millet.uniformity-1.png deleted file mode 100644 index 0e9b190c..00000000 Binary files a/docs/reference/li.millet.uniformity-1.png and /dev/null differ diff --git a/docs/reference/li.millet.uniformity.html b/docs/reference/li.millet.uniformity.html deleted file mode 100644 index 0e393a54..00000000 --- a/docs/reference/li.millet.uniformity.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Uniformity trial of millet — li.millet.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of millet

    - - -
    - -
    -

    Uniformity trial of millet at China in 1934.

    -
    - - - -

    Format

    - -

    A data frame with 600 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield (grams)

    - -
    - -

    Details

    - - - -

    Crop date estimated to be 1934.

    -

    Field was 100 ft x 100 ft. Plots were 15 feet long by 1 foot wide.

    -

    Field width: 100 plots * 1 foot = 100 feet

    -

    Field length: 6 plots * 15 feet = 100 feet

    -

    Li found the most efficient use of land was obtained with plats 15 - feet long and two rowss wide. Also satisfactory would be one row 30 - feet long.

    -

    Source

    - -

    Li, HW and Meng, CJ and Liu, TN. 1936. - Field Results in a Millet Breeding Experiment. - Agronomy Journal, 28, 1-15. Table 1. - https://doi.org/10.2134/agronj1936.00021962002800010001x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(li.millet.uniformity)
-  dat <- li.millet.uniformity
-
-  mean(dat$yield) # matches Li et al.
-
    #> [1] 255.14
      
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          aspect=100/100, # true aspect
-          main="li.millet.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/libs.html b/docs/reference/libs.html deleted file mode 100644 index 0fa38880..00000000 --- a/docs/reference/libs.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - -Load multiple packages and install if needed - -The 'agridat' package uses dozens of packages in the examples for each dataset. -The 'libs' function provides a simple way to load multiple packages at once, -and can install any missing packages on-the-fly. - -This is very similar to the `pacman::p_load` function. — libs • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Load multiple packages and install if needed - -The 'agridat' package uses dozens of packages in the examples for each dataset. -The 'libs' function provides a simple way to load multiple packages at once, -and can install any missing packages on-the-fly. - -This is very similar to the `pacman::p_load` function.

    - Source: R/libs.R - -
    - -
    -

    Load multiple packages and install if needed

    -

    The 'agridat' package uses dozens of packages in the examples for each dataset. -The 'libs' function provides a simple way to load multiple packages at once, -and can install any missing packages on-the-fly.

    -

    This is very similar to the `pacman::p_load` function.

    -
    - - 
    libs(...)
    - -

    Arguments

    - - - - - - -
    ...

    Comma-separated unquoted package names

    - -

    Value

    - -

    None

    -

    References

    - -

    None

    -

    Author

    - -

    Kevin Wright

    - -

    Examples

    - 
    # \dontrun{
-libs(dplyr,reshape2)
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lillemo.wheat-1.png b/docs/reference/lillemo.wheat-1.png deleted file mode 100644 index a2edd3a2..00000000 Binary files a/docs/reference/lillemo.wheat-1.png and /dev/null differ diff --git a/docs/reference/lillemo.wheat-2.png b/docs/reference/lillemo.wheat-2.png deleted file mode 100644 index 0ea83e15..00000000 Binary files a/docs/reference/lillemo.wheat-2.png and /dev/null differ diff --git a/docs/reference/lillemo.wheat.html b/docs/reference/lillemo.wheat.html deleted file mode 100644 index 72781240..00000000 --- a/docs/reference/lillemo.wheat.html +++ /dev/null @@ -1,345 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat susceptibile to powdery mildew

    - - -
    - -
    -

    Resistance of wheat to powdery mildew

    -
    - - 
    data("lillemo.wheat")
    - - -

    Format

    - -

    A data frame with 408 observations on the following 4 variables.

    -
    gen

    genotype, 24 levels

    -
    env

    environrment, 13 levels

    -
    score

    score

    -
    scale

    scale used for score

    - -
    - -

    Details

    - - - -

    The data are means across reps of the original scores. Lower scores - indicate better resistance to mildew.

    -

    Each location used one of four different measurement scales for - scoring resistance to powdery mildew: 0-5 scale, 1-9 scale, 0-9 scale, - percent.

    -

    Environment codes consist of two letters for the location name and two - digits for the year of testing. Location names: CA=Cruz Alta, - Brazil. Ba= Bawburgh, UK. Aa=As, Norway. Ha=Hamar, Norway. Ch=Choryn, - Poland. Ce=Cerekwica, Poland. Ma=Martonvasar, Hungary. Kh=Kharkiv, - Ukraine. BT=Bila Tserkva, Ukraine. Gl=Glevakha, Ukraine. Bj=Beijing, - China.

    -

    Note, Lillemo et al. did not remove genotype effects as is customary - when calculating Huehn's non-parametric stability statistics.

    -

    In the examples below, the results do not quite match the - results of Lillemo. This could easily be the result of the original - data table being rounded to 1 decimal place. For example, environment - 'Aa03' had 3 reps and so the mean for genotype 1 was probably 16.333, - not 16.3.

    - -

    Used with permission of Morten Lillemo.

    -

    Electronic data supplied by Miroslav Zoric.

    -

    Source

    - - - -

    Morten Lillemo, Ravi Sing, Maarten van Ginkel. (2011). - Identification of Stable Resistance to Powdery Mildew in Wheat Based - on Parametric and Nonparametric Methods - Crop Sci. 50:478-485. - https://doi.org/10.2135/cropsci2009.03.0116

    -

    References

    - -

    None.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(lillemo.wheat)
-dat <- lillemo.wheat
-
-# Change factor levels to match Lillemo
-dat$env <- as.character(dat$env)
-dat$env <- factor(dat$env,
-                  levels=c("Bj03","Bj05","CA03","Ba04","Ma04",
-                           "Kh06","Gl05","BT06","Ch04","Ce04",
-                           "Ha03","Ha04","Ha05","Ha07","Aa03","Aa04","Aa05"))
-# Interesting look at different measurement scales by environment
-libs(lattice)
-qqmath(~score|env, dat, group=scale,
-       as.table=TRUE, scales=list(y=list(relation="free")),
-       auto.key=list(columns=4),
-       main="lillemo.wheat - QQ plots by environment")
-
    
-# ----------------------------------------------------------------------------
-
-# \dontrun{
-  # Change data to matrix format
-  libs(reshape2)
-  datm <- acast(dat, gen~env, value.var='score')
-  
-  # Environment means. Matches Lillemo Table 3
-  apply(datm, 2, mean)
-
    #>      Bj03      Bj05      CA03      Ba04      Ma04      Kh06      Gl05      BT06 
-#>  3.750000  4.729167  1.854167  4.362500  5.208333 17.875000 16.479167 14.708333 
-#>      Ch04      Ce04      Ha03      Ha04      Ha05      Ha07      Aa03      Aa04 
-#>  4.750000  4.416667 14.504167 18.416667 21.354167 16.270833 23.012500 12.833333 
-#>      Aa05 
-#> 24.062500 
      
-  # Two different transforms within envts to approximate 0-9 scale
-  datt <- datm
-  datt[,"CA03"] <- 1.8 * datt[,"CA03"]
-  ix <- c("Ba04","Kh06","Gl05","BT06","Ha03","Ha04","Ha05","Ha07","Aa03","Aa04","Aa05")
-  datt[,ix] <- apply(datt[,ix],2,sqrt)
-
-  # Genotype means of transformed data. Matches Lillemo table 3.
-  round(rowMeans(datt),2)
-
    #>  G01  G02  G03  G04  G05  G06  G07  G08  G09  G10  G11  G12  G13  G14  G15  G16 
-#> 3.24 2.40 2.96 3.05 3.76 3.38 3.74 4.66 2.34 2.64 3.97 4.61 4.47 4.85 3.83 2.95 
-#>  G17  G18  G19  G20  G21  G22  G23  G24 
-#> 3.45 4.61 3.34 4.08 2.91 3.79 7.72 7.46 
    
-  # Biplot of transformed data like Lillemo Fig 2
-  libs(gge)
-  biplot(gge(datt, scale=FALSE), main="lillemo.wheat")
-
      
-  # Median polish of transformed table
-  m1 <- medpolish(datt)
-
    #> 1: 337.6127
-#> 2: 325.4158
-#> Final: 324.2539
      # Half-normal prob plot like Fig 1
-  # libs(faraway)
-  # halfnorm(abs(as.vector(m1$resid)))
-
-  # Nonparametric stability statistics. Lillemo Table 4.
-  huehn <- function(mat){
-    # Gen in rows, Env in cols  
-    nenv <- ncol(mat)
-    # Corrected yield. Remove genotype effects
-    # Remove the following line to match Table 4 of Lillemo
-    mat <- sweep(mat, 1, rowMeans(mat)) + mean(mat)
-    # Ranks in each environment
-    rmat <- apply(mat, 2, rank)
-    
-    # Mean genotype rank across envts
-    MeanRank <- apply(rmat, 1, mean)
-    
-    # Huehn S1
-    gfun <- function(x){
-      oo <- outer(x,x,"-")
-      sum(abs(oo)) # sum of all absolute pairwise differences
-    }
-    S1 <- apply(rmat, 1, gfun)/(nenv*(nenv-1))
-    
-    # Huehn S2
-    S2 <- apply((rmat-MeanRank)^2,1,sum)/(nenv-1)
-    
-    out <- data.frame(MeanRank,S1,S2)
-    rownames(out) <- rownames(mat)
-    return(out)
-  }
-  round(huehn(datm),2) # Matches table 4
-
    #>     MeanRank    S1     S2
-#> G01    12.00  7.01  36.12
-#> G02    14.53  9.66  71.51
-#> G03    13.29  7.59  41.72
-#> G04    14.06  9.51  68.93
-#> G05    11.00  7.26  38.50
-#> G06    13.47  3.99  12.39
-#> G07    12.29  6.66  33.22
-#> G08    10.06  9.18  65.68
-#> G09    14.47  7.21  38.01
-#> G10    14.00  6.47  31.00
-#> G11    10.88  7.47  41.74
-#> G12    10.82  7.29  40.53
-#> G13    10.88  8.97  62.49
-#> G14    11.18  8.69  55.65
-#> G15    13.12  7.34  39.99
-#> G16    13.88  6.00  27.74
-#> G17    11.88  6.97  35.74
-#> G18    10.94  6.37  31.81
-#> G19    14.24  5.40  21.32
-#> G20    11.06  7.87  46.43
-#> G21    14.88  7.96  48.99
-#> G22    11.35  9.00  59.87
-#> G23    13.00 11.85 117.12
-#> G24    12.71 11.29 102.22
      
-  # I do not think phenability package gives correct values for S1
-  # libs(phenability)
-  # nahu(datm)
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lin.superiority-1.png b/docs/reference/lin.superiority-1.png deleted file mode 100644 index 1c6b7a71..00000000 Binary files a/docs/reference/lin.superiority-1.png and /dev/null differ diff --git a/docs/reference/lin.superiority.html b/docs/reference/lin.superiority.html deleted file mode 100644 index 887d4154..00000000 --- a/docs/reference/lin.superiority.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of 33 barley genotypes in 12 locations

    - - -
    - -
    -

    Multi-environment trial of 33 barley genotypes in 12 locations

    -
    - - 
    data("lin.superiority")
    - - -

    Format

    - -

    A data frame with 396 observations on the following 4 variables.

    -
    gen

    genotype/cultivar

    -
    region

    region

    -
    loc

    location

    -
    yield

    yield (kg/ha)

    - -
    - -

    Details

    - -

    Yield of six-row barley from the 1983 annual report of Eastern - Cooperative Test in Canada.

    -

    The named cultivars Bruce, Conquest, Laurier, Leger are checks, while - the other cultivars were tests.

    -

    Source

    - -

    C. S. Lin, M. R. Binns (1985). - Procedural approach for assessing cultivar-location data: Pairwise genotype-environment interactions of test cultivars with checks - Canadian Journal of Plant Science, 1985, 65(4): 1065-1071. Table 1. - https://doi.org/10.4141/cjps85-136

    -

    References

    - -

    C. S. Lin, M. R. Binns (1988). - A Superiority Measure Of Cultivar Performance For Cultivar x Location Data. - Canadian Journal of Plant Science, 68, 193-198. - https://doi.org/10.4141/cjps88-018

    -

    Mohammed Ali Hussein, Asmund Bjornstad, and A. H. Aastveit (2000). - SASG x ESTAB: A SAS Program for Computing Genotype x Environment - Stability Statistics. - Agronomy Journal, 92; 454-459. - https://doi.org/10.2134/agronj2000.923454x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(lin.superiority)
-dat <- lin.superiority
-
-libs(latticeExtra)
-libs(reshape2)
-# calculate the superiority measure of Lin & Binns 1988
-
-dat2 <- acast(dat, gen ~ loc, value.var="yield")
-locmean <- apply(dat2, 2, mean)
-locmax <- apply(dat2, 2, max)
-P <- apply(dat2, 1, function(x) {
-  sum((x-locmax)^2)/(2*length(x))
-})/1000
-P <- sort(P)
-round(P) # match Lin & Binns 1988 table 2, column Pi
-
    #>    Leger      T14      T07      T13      T06      T03      T25      T05 
-#>       36      102      107      137      165      182      185      200 
-#>      T27      T10      T01      T29      T09      T20      T26    Bruce 
-#>      220      235      247      248      254      256      283      298 
-#>  Laurier      T21      T17      T18      T23      T19      T02      T22 
-#>      312      313      323      364      388      421      430      434 
-#>      T11      T15      T08      T28      T12 Conquest      T16      T04 
-#>      445      465      473      540      579      585      604      611 
-#>      T24 
-#>      720 
    
-# atlantic & quebec regions overlap
-# libs(gge)
-# m1 <- gge(dat, yield ~ gen*loc, env.group=region,
-#           main="lin.superiority")
-# biplot(m1)
-
-# create a figure similar to Lin & Binns 1988
-
-# add P, locmean, locmax back into the data
-dat$locmean <- locmean[match(dat$loc, names(locmean))]
-dat$locmax <- locmax[match(dat$loc, names(locmax))]
-dat$P <- P[match(dat$gen, names(P))]
-dat$gen <- reorder(dat$gen, dat$P)
-xyplot(locmax ~ locmean|gen, data=dat,
-       type=c('p','r'), as.table=TRUE, col="gray",
-       main="lin.superiority - Superiority index",
-       xlab="Location Mean",
-       ylab="Yield of single cultivars (blue) & Maximum (gray)") +
-  xyplot(yield ~ locmean|gen, data=dat,
-         type=c('p','r'), as.table=TRUE, pch=19)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lin.unbalanced-1.png b/docs/reference/lin.unbalanced-1.png deleted file mode 100644 index 183caa5f..00000000 Binary files a/docs/reference/lin.unbalanced-1.png and /dev/null differ diff --git a/docs/reference/lin.unbalanced.html b/docs/reference/lin.unbalanced.html deleted file mode 100644 index ba0f48c9..00000000 --- a/docs/reference/lin.unbalanced.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of 33 barley genotypes in 18 locations

    - - -
    - -
    -

    Multi-environment trial of 33 barley genotypes in 18 locations

    -
    - - 
    data("lin.unbalanced")
    - - -

    Format

    - -

    A data frame with 405 observations on the following 4 variables.

    -
    gen

    genotype/cultivar

    -
    loc

    location

    -
    yield

    yield (kg/ha)

    -
    region

    region

    - -
    - -

    Details

    - -

    Yield of six-row barley from the 1986 Eastern Cooperative trial

    -

    The named cultivars Bruce, Laurier, Leger are checks, while the other cultivars were tests. Cultivar names use the following codes. - "A" is for Atlantic-Quebec. "O" is for "Ontario". - "S" is second-year. "T" is third-year.

    -

    Source

    - -

    C. S. Lin, M. R. Binns (1988). - A Method for Assessing Regional Trial Data When The Test Cultivars Are - Unbalanced With Respect to Locations. - Canadian Journal of Plant Science, 68(4): 1103-1110. - https://doi.org/10.4141/cjps88-130

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lin.unbalanced)
-dat <- lin.unbalanced
-
-# location maximum, Lin & Binns table 1
-# aggregate(yield ~ loc, data=dat, FUN=max)
-
-# location mean/index, Lin & Binns, table 1
-dat2 <- subset(dat, is.element(dat$gen,
-  c('Bruce','Laurier','Leger','S1','S2',
-    'S3','S4','S5','S6','S7','T1','T2')))
-aggregate(yield ~ loc, data=dat2, FUN=mean)
-
    #>    loc    yield
-#> 1  L01 6205.417
-#> 2  L02 6562.000
-#> 3  L03 4118.167
-#> 4  L04 7203.583
-#> 5  L05 3795.417
-#> 6  L06 5440.583
-#> 7  L07 5243.250
-#> 8  L08 5419.500
-#> 9  L09 6205.750
-#> 10 L10 4186.583
-#> 11 L11 3565.250
-#> 12 L12 4027.583
-#> 13 L13 4245.667
-#> 14 L14 5061.750
-#> 15 L15 4348.083
-#> 16 L16 5424.583
-#> 17 L17 3909.250
-#> 18 L18 3123.833
    
-libs(reshape2)
-dat3 <- acast(dat, gen ~ loc, value.var="yield")
-libs(lattice)
-lattice::levelplot(t(scale(dat3)), main="lin.unbalanced", xlab="loc", ylab="genotype")
-
    
-# calculate the superiority measure of Lin & Binns 1988.
-# lower is better
-locmax <- apply(dat3, 2, max, na.rm=TRUE)
-P <- apply(dat3, 1, function(x) {
-  sum((x-locmax)^2, na.rm=TRUE)/(2*length(na.omit(x)))
-})/1000
-P <- sort(P)
-round(P) # match Lin & Binns 1988 table 2, column P
-
    #>     O02      S4     O01     O10     O07     O06     O05     O08     O04      S5 
-#>     110     121     142     150     171     199     203     223     316     338 
-#>   Leger      S1     A04     O09     A06      T2      S2     A03      T1     O03 
-#>     366     379     388     401     421     423     485     515     519     528 
-#>      S7     A11      S6     A09     A07      S3     A05     A08     A10   Bruce 
-#>     549     567     657     706     717     750     762     791     849     900 
-#> Laurier     A02     A01 
-#>     933    1134    1566 
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/linder.wheat-1.png b/docs/reference/linder.wheat-1.png deleted file mode 100644 index 80c3740d..00000000 Binary files a/docs/reference/linder.wheat-1.png and /dev/null differ diff --git a/docs/reference/linder.wheat.html b/docs/reference/linder.wheat.html deleted file mode 100644 index 084e223e..00000000 --- a/docs/reference/linder.wheat.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat in Switzerland — linder.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat in Switzerland

    - - -
    - -
    -

    Multi-environment trial of wheat in Switzerland

    -
    - - 
    data("linder.wheat")
    - - -

    Format

    - -

    A data frame with 252 observations on the following 4 variables.

    -
    env

    environment

    -
    block

    block

    -
    gen

    genotype

    -
    yield

    yield, in 10 kg/ha

    - -
    - -

    Details

    - -

    An experiment of 9 varieties of wheat in 7 localities in Switzerland - in 1960, RCB design.

    -

    Source

    - -

    Arthur Linder (1960). - Design and Analysis of Experiments, notes on lectures held during the - fall semester 1963 at the Statistics Department, University of North - Carolina, page 160. - https://www.stat.ncsu.edu/information/library/mimeo.archive/ISMS_1964_398-A.pdf

    -

    References

    - -

    None.

    - -

    Examples

    - 
    
-library(agridat)
-data(linder.wheat)
-dat <- linder.wheat
-# \dontrun{
-libs(gge)
-dat <- transform(dat, eb=paste0(env,block))
-m1 <- gge(dat, yield~gen*eb, env.group=env)
-biplot(m1, main="linder.wheat")
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/little.splitblock-1.png b/docs/reference/little.splitblock-1.png deleted file mode 100644 index 7a456d29..00000000 Binary files a/docs/reference/little.splitblock-1.png and /dev/null differ diff --git a/docs/reference/little.splitblock.html b/docs/reference/little.splitblock.html deleted file mode 100644 index 03ab799b..00000000 --- a/docs/reference/little.splitblock.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Split-block experiment of sugar beets — little.splitblock • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Split-block experiment of sugar beets

    - - -
    - -
    -

    Split-block experiment of sugar beets.

    -
    - - 
    data("little.splitblock")
    - - -

    Format

    - -

    A data frame with 80 observations on the following 6 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    sugar beet yield, tons/acre

    -
    harvest

    harvest date, weeks after planting

    -
    nitro

    nitrogen, pounds/acre

    -
    block

    block

    - -
    - -

    Details

    - -

    Four rates of nitrogen, laid out as a 4x4 Latin-square experiment.

    -

    Within each column block, the sub-plots are strips (across 4 rows) of - 5 different harvest dates.

    -

    The use of sub-plots a s strips necessitates care when determining the - error terms in the ANOVA table.

    -

    Note, Little has yield value of 22.3 for row 3, column I-H3. This data - uses 23.3 in order to match the marginal totals given by Little.

    -

    Source

    - -

    Thomas M. Little, F. Jackson Hills. (1978) - Agricultural Experimentation

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(little.splitblock)
-dat <- little.splitblock
-
-# Match marginal totals given by Little.
-## sum(dat$yield)
-## with(dat, tapply(yield,col,sum))
-## with(dat, tapply(yield,row,sum))
-
-# Layout shown by Little figure 10.2
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        out1=block, out2=col, col=nitro, cex=1, num=harvest,
-        main="little.splitblock")
-
    
-
-# Convert continuous traits to factors
-dat <- transform(dat, R=factor(row), C=factor(block),
-                 H=factor(harvest), N=factor(nitro))
-
-if(0){
-  libs(lattice)
-  xyplot(yield ~ nitro|H,dat)
-  xyplot(yield ~ harvest|N,dat)
-}
-
-# Anova table matches Little, table 10.3
-m1 <- aov(yield ~ R + C + N + H + N:H +
-            Error(R:C:N + C:H + C:N:H), data=dat)
-
    #> Warning: Error() model is singular
    summary(m1)
-
    #> 
-#> Error: C:H
-#>           Df Sum Sq Mean Sq F value   Pr(>F)    
-#> C          3     58    19.4   2.326    0.126    
-#> H          4   3719   929.6 111.711 2.19e-09 ***
-#> Residuals 12    100     8.3                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: R:C:N
-#>           Df Sum Sq Mean Sq F value  Pr(>F)   
-#> R          3  224.7    74.9   3.755 0.07888 . 
-#> N          3 1101.3   367.1  18.406 0.00198 **
-#> Residuals  6  119.7    19.9                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: C:N:H
-#>           Df Sum Sq Mean Sq F value   Pr(>F)    
-#> N:H       12 157.68  13.140   6.497 5.61e-06 ***
-#> Residuals 36  72.81   2.022                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/loesell.bean.uniformity-1.png b/docs/reference/loesell.bean.uniformity-1.png deleted file mode 100644 index ba0d7d66..00000000 Binary files a/docs/reference/loesell.bean.uniformity-1.png and /dev/null differ diff --git a/docs/reference/loesell.bean.uniformity.html b/docs/reference/loesell.bean.uniformity.html deleted file mode 100644 index 5ed31f18..00000000 --- a/docs/reference/loesell.bean.uniformity.html +++ /dev/null @@ -1,223 +0,0 @@ - - - - - - - - -Uniformity trial of white pea beans — loesell.bean.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of white pea beans

    - - -
    - -
    -

    Uniformity trial of white pea beans

    -
    - - 
    data("loesell.bean.uniformity")
    - - -

    Format

    - -

    A data frame with 1890 observations on the following 3 variables.

    -
    row

    row ordinate

    -
    col

    column ordinate

    -
    yield

    yield, grams per plot

    - -
    - -

    Details

    - -

    Trial conducted at Michigan Agricultural Experiment Station, 1.75 - acres. Beans were planted in rows 28 inches apart on 15 Jun - 1932. Plants spaced 1 to 2 inches apart. After planting, an area 210 - ft x 210 feet. This area was divided into 21 columns, each 10 foot - wide, and each containing90 rows.

    -

    Field length: 90 rows * 28 inches = 210 feet.

    -

    Field width: 21 series * 10 feet = 210 feet.

    -

    Author's conclusion: Increasing the size of the plot by increasing its - length was more efficient than increasing its width.

    -

    Note, the missing values in this dataset are a result of the PDF scan - omitting corners of the table.

    -

    Source

    - -

    Loesell, Clarence (1936). - Size of plot & number of replications necessary for varietal trials - with white pea beans. - PhD Thesis, Michigan State. Table 3, p. 9-10. - https://d.lib.msu.edu/etd/5271

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  require(agridat)
-  data(loesell.bean.uniformity)
-  dat <- loesell.bean.uniformity
-  require(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=1, tick=TRUE,
-          main="loesell.bean.uniformity")
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lonnquist.maize-1.png b/docs/reference/lonnquist.maize-1.png deleted file mode 100644 index 22efb7dc..00000000 Binary files a/docs/reference/lonnquist.maize-1.png and /dev/null differ diff --git a/docs/reference/lonnquist.maize.html b/docs/reference/lonnquist.maize.html deleted file mode 100644 index 20bf1b85..00000000 --- a/docs/reference/lonnquist.maize.html +++ /dev/null @@ -1,233 +0,0 @@ - -Multi-environment trial of maize, half diallel — lonnquist.maize • agridat - - -
    -
    - - - -
    -
    -
    -

    Multi-environment trial of maize, half diallel

    - - -
    - -
    -

    Half diallel of maize

    -
    - -
    - 
    data("lonnquist.maize")
    -
    - -
    -

    Format

    -

    A data frame with 78 observations on the following 3 variables.

    p1
    -

    parent 1 factor

    - -
    p2
    -

    parent 2 factor

    - -
    yield
    -

    yield

    - - -
    -
    -

    Details

    - - -

    Twelve hybrids were selfed/crossed in a half-diallel design. - The data here are means adjusted for block effects. - Original experiment was 3 reps at 2 locations in 2 years.

    -
    -
    -

    Source

    -

    J. H. Lonnquist, C. O. Gardner. (1961) - Heterosis in Intervarietal Crosses in Maize and Its Implication in - Breeding Procedures. - Crop Science, 1, 179-183. Table 1.

    -
    -
    -

    References

    -

    Mohring, Melchinger, Piepho. (2011). - REML-Based Diallel Analysis. - Crop Science, 51, 470-478. - https://doi.org/10.2135/cropsci2010.05.0272

    -

    C. O. Gardner and S. A. Eberhart. 1966. - Analysis and Interpretation of the Variety Cross Diallel and Related - Populations. Biometrics, 22, 439-452. - https://doi.org/10.2307/2528181

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(lonnquist.maize)
-  dat <- lonnquist.maize
-  dat <- transform(dat,
-                   p1=factor(p1,
-                             levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2")),
-                   p2=factor(p2,
-                             levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2")))
-  
-  libs(lattice)
-  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-  levelplot(yield ~ p1*p2, dat, col.regions=redblue,
-            main="lonnquist.maize - yield of diallel cross")
-
-
-
-  # Calculate the F1 means in Lonnquist, table 1
-  # libs(reshape2)
-  # mat <- acast(dat, p1~p2)
-  # mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] # make symmetric
-  # diag(mat) <- NA
-  # round(rowMeans(mat, na.rm=TRUE),1)
-  ##    C     L     M     H     G     P     B    RM     N     K    R2    K2
-  ## 94.8  89.2  95.0  96.4  95.3  95.2  97.3  93.7  95.0  94.0  98.9 102.4
-
-
-  # Griffings method
-  # https://www.statforbiology.com/2021/stat_met_diallel_griffing/
-  # libs(lmDiallel)
-  # dat2 <- lonnquist.maize
-  # dat2 <- subset(dat2,
-  #                is.element(p1, c("M","H","G","B","K","K2")) &
-  #                is.element(p2, c("M","H","G","B","K","K2")))
-  # dat2 <- droplevels(dat2)
-  # dmod1 <- lm(yield ~ GCA(p1, p2) + tSCA(p1, p2),
-  #             data = dat2)
-  # dmod2 <- lm.diallel(yield ~ p1 + p2, 
-  #                     data = dat2, fct = "GRIFFING2")
-  # anova.diallel(dmod1, MSE=7.1, dfr=60)
-  ## Response: yield
-  ##              Df Sum Sq Mean Sq F value    Pr(>F)    
-  ## GCA(p1, p2)   5 234.23  46.846  6.5980 5.923e-05 ***
-  ## tSCA(p1, p2) 15 238.94  15.929  2.2436   0.01411 *  
-  ## Residuals    60          7.100                      
-
-  
-  # ----------
-
-  # asreml4
-  # Mohring 2011 used 6 varieties to calculate GCA & SCA
-  # Matches Table 3, column 2
-  d2 <- subset(dat, is.element(p1, c("M","H","G","B","K","K2")) &
-                      is.element(p2, c("M","H","G","B","K","K2")))
-  d2 <- droplevels(d2)
-  libs(asreml,lucid)
-  m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2))
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -42.1442       18.4535     20 15:17:47    0.0
-#>  2      -42.0279       17.6444     20 15:17:47    0.0
-#>  3      -41.9054       16.2198     20 15:17:47    0.0
-#>  4      -41.9007       15.9433     20 15:17:47    0.0
-  # vc(m2)
-  ##     effect component std.error z.ratio      con
-  ##  p1!p1.var     3.865     3.774     1   Positive
-  ## R!variance    15.93      5.817     2.7 Positive
-  
-  # Calculate GCA effects
-  m3 <- asreml(yield~ p1 + and(p2), data=d2)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:47 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -34.0862       15.9294     15 15:17:47    0.0
-  coef(m3)$fixed-1.462
-#>               effect
-#> p1_M        -1.46200
-#> p1_H        -0.19950
-#> p1_G        -2.16200
-#> p1_B         1.48800
-#> p1_K        -1.78700
-#> p1_K2        4.12550
-#> (Intercept) 91.77014
-  # Matches Gardner 1966, Table 5, Griffing method
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/lord.rice.uniformity-1.png b/docs/reference/lord.rice.uniformity-1.png deleted file mode 100644 index 8e334617..00000000 Binary files a/docs/reference/lord.rice.uniformity-1.png and /dev/null differ diff --git a/docs/reference/lord.rice.uniformity.html b/docs/reference/lord.rice.uniformity.html deleted file mode 100644 index 55289966..00000000 --- a/docs/reference/lord.rice.uniformity.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - - -Uniformity trial of rice — lord.rice.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of rice

    - - -
    - -
    -

    Uniformity trial of rice in Ceylon, 1929.

    -
    - - 
    data("lord.rice.uniformity")
    - - -

    Format

    - -

    A data frame with 560 observations on the following 5 variables.

    -
    field

    field

    -
    row

    row

    -
    col

    column

    -
    grain

    grain weight, pounds per plot

    -
    straw

    straw weight, pounds per plot

    - -
    - -

    Details

    - -

    In 1929, eight fields 1/5 acre in size were broadcast seeded with rice - at the Anuradhapura Experiment Station in the northern dry zone of - Ceylon. After broadcast, the fields were marked into 10 ft by 10 ft - squares. At harvest, weights of grain and straw were recorded.

    -

    Fields 10-14 were on one side of a drain, and fields 26-28 on the - other side.

    -

    Each field was surrounded by a bund. Plots next to the bunds had higher - yields.

    -

    Field width: 5 plots * 10 feet = 50 feet

    -

    Field length: 14 plots * 10 feet = 140 feet

    -

    Conclusions: "It would appear that plots of about 1/87 acre are the most effective."

    -

    Source

    - -

    Lord, L. (1931). - A Uniformity Trial with Irrigated Broadcast Rice. - The Journal of Agricultural Science, 21(1), 178-188. - https://doi.org/10.1017/S0021859600008029

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(lord.rice.uniformity)
-dat <- lord.rice.uniformity
-
-# match table on page 180
-## libs(dplyr)
-## dat 
-##   field grain straw
-##   <chr> <dbl> <dbl>
-## 1 10      590   732
-## 2 11      502   600
-## 3 12      315   488
-## 4 13      291   538
-## 5 14      489   670
-## 6 26      441   560
-## 7 27      451   629
-## 8 28      530   718
-
-# There are consistently high yields along all edges of the field
-# libs(lattice)
-# bwplot(grain ~ factor(col)|field,dat)
-# bwplot(grain ~ factor(col)|field,dat)
-
-# Heatmaps
-libs(desplot)
-desplot(dat, grain ~ col*row|field,
-        flip=TRUE, aspect=140/50,
-        main="lord.rice.uniformity")
-
    
-
-# bivariate scatterplots  
-# xyplot(grain ~ straw|field, dat)
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/love.cotton.uniformity-1.png b/docs/reference/love.cotton.uniformity-1.png deleted file mode 100644 index 6f7d5442..00000000 Binary files a/docs/reference/love.cotton.uniformity-1.png and /dev/null differ diff --git a/docs/reference/love.cotton.uniformity.html b/docs/reference/love.cotton.uniformity.html deleted file mode 100644 index 438ff7e8..00000000 --- a/docs/reference/love.cotton.uniformity.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - - -Uniformity trial of cotton — love.cotton.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of cotton

    - - -
    - -
    -

    Uniformity trial of cotton

    -
    - - 
    data("love.cotton.uniformity")
    - - -

    Format

    - -

    A data frame with 170 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Within each 100-foot row, the first 20 feet were harvested as a single plot, - and then the rest of the row was harvested in 5-foot lengths.

    -

    Crop location is unknown, but likely Southeast Asia given Love's work - teaching in China. Date circa 1930.

    -

    Possibly more information would be in the collected papers of Harry - Love at Cornell: - https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html - Cotton - Plot Technic Study 1930-1932. Box 3, Folder 34

    -

    Source

    - -

    Harry Love (1937). - Application of Statistical Methods to Agricultural Research. - The Commercial Press, Shanghai. Page 411. - https://archive.org/details/in.ernet.dli.2015.233346/page/n421

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(love.cotton.uniformity)
-  # omit first column which has 20-foot plots
-  dat <- subset(love.cotton.uniformity, col > 1)
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=20/80, # just a guess
-          main="love.cotton.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lu.stability.html b/docs/reference/lu.stability.html deleted file mode 100644 index e110ece7..00000000 --- a/docs/reference/lu.stability.html +++ /dev/null @@ -1,360 +0,0 @@ - - - - - - - - -Multi-environment trial of maize, to illustrate stability statistics — lu.stability • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize, to illustrate stability statistics

    - - -
    - -
    -

    Multi-environment trial to illustrate stability statistics

    -
    - - 
    data("lu.stability")
    - - -

    Format

    - -

    A data frame with 120 observations on the following 4 variables.

    -
    yield

    yield

    -
    gen

    genotype factor, 5 levels

    -
    env

    environment factor, 6 levels

    -
    block

    block factor, 4 levels

    - -
    - -

    Details

    - -

    Data for 5 maize genotypes in 2 years x 3 sites = 6 environments.

    -

    Source

    - -

    H.Y. Lu and C. T. Tien. (1993) - Studies on nonparametric method of phenotypic stability: II. Selection - for stability of agroeconomic concept. - J. Agric. Assoc. China 164:1-17.

    -

    References

    - - - -

    Hsiu Ying Lu. 1995. - PC-SAS Program for Estimating Huehn's Nonparametric Stability - Statistics. - Agron J. 87:888-891.

    -

    Kae-Kang Hwu and Li-yu D Liu. (2013) - Stability Analysis Using Multiple Environment Trials Data by Linear - Regression. (In Chinese) - Crop, Environment & Bioinformatics 10:131-142.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(lu.stability)
-  dat <- lu.stability
-
-  # GxE means. Match Lu 1995 table 1
-  libs(reshape2)
-  datm <- acast(dat, gen~env, fun=mean, value.var='yield')
-  round(datm, 2)
-
    #>         E1      E2      E3      E4      E5      E6
-#> G1 3288.75 2162.50 3692.75 3920.75 1588.75 1805.25
-#> G2 3487.25 1659.50 3148.25 3897.25 1936.50 1404.00
-#> G3 2651.25 1996.25 2374.50 2919.75  982.50 1158.00
-#> G4 2467.00 2095.25 2574.50 3743.00 1845.75 1488.75
-#> G5 2752.50 1761.25 3912.75 4178.75 1889.50 1678.00
      # Gen/Env means. Match Lu 1995 table 3
-  apply(datm, 1, mean)
-
    #>       G1       G2       G3       G4       G5 
-#> 2743.125 2588.792 2013.708 2369.042 2695.458 
      apply(datm, 2, mean)
-
    #>      E1      E2      E3      E4      E5      E6 
-#> 2929.35 1934.95 3140.55 3731.90 1648.60 1506.80 
      
-  
-  # Traditional ANOVA. Match Hwu table 2
-  # F value for gen,env
-  m1 = aov(yield~env+gen+Error(block:env+env:gen), data=dat)
-
    #> Warning: Error() model is singular
      summary(m1)   
-
    #> 
-#> Error: block:env
-#>           Df   Sum Sq  Mean Sq F value   Pr(>F)    
-#> env        5 82817889 16563578   102.9 1.36e-12 ***
-#> Residuals 18  2897821   160990                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: env:gen
-#>           Df  Sum Sq Mean Sq F value  Pr(>F)   
-#> gen        4 8573084 2143271   4.436 0.00995 **
-#> Residuals 20 9662873  483144                   
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Error: Within
-#>           Df   Sum Sq Mean Sq F value Pr(>F)
-#> Residuals 72 12912246  179337               
      # F value for gen:env, block:env
-  m2 <- aov(yield ~ gen + env + gen:env + block:env, data=dat) 
-  summary(m2)
-
    #>             Df   Sum Sq  Mean Sq F value   Pr(>F)    
-#> gen          4  8573084  2143271  11.951 1.68e-07 ***
-#> env          5 82817889 16563578  92.360  < 2e-16 ***
-#> gen:env     20  9662873   483144   2.694  0.00114 ** 
-#> env:block   18  2897821   160990   0.898  0.58302    
-#> Residuals   72 12912246   179337                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-  # Finlay Wilkinson regression coefficients
-  # First, calculate env mean, merge in
-  libs(dplyr)
-  dat2 <- group_by(dat, env)
-  dat2 <- mutate(dat2, locmn=mean(yield))
-  m4 <- lm(yield ~ gen -1 + gen:locmn, data=dat2)
-  coef(m4) # Match Hwu table 4
-
    #>        genG1        genG2        genG3        genG4        genG5  genG1:locmn 
-#>   24.4698520 -176.3280420    0.5186791  366.5962974 -215.2567865    1.0953375 
-#>  genG2:locmn  genG3:locmn  genG4:locmn  genG5:locmn 
-#>    1.1140580    0.8111077    0.8067789    1.1727179 
    
-# Table 6: Shukla's heterogeneity test
-  dat2$ge = paste0(dat2$gen, dat2$env) # Create a separate ge interaction term  
-  m6 <- lm(yield ~ gen + env + ge + ge:locmn, data=dat2)
-  m6b <- lm( yield ~ gen + env + ge + locmn, data=dat2)
-  anova(m6, m6b) # Non-significant difference
-
    #> Analysis of Variance Table
-#> 
-#> Model 1: yield ~ gen + env + ge + ge:locmn
-#> Model 2: yield ~ gen + env + ge + locmn
-#>   Res.Df      RSS Df Sum of Sq F Pr(>F)
-#> 1     90 15810067                      
-#> 2     90 15810067  0         0         
    
-  # Table 7 - Shukla stability
-  # First, environment means
-  emn <- group_by(dat2, env)
-  emn <- summarize(emn, ymn=mean(yield))
-
    #> `summarise()` ungrouping output (override with `.groups` argument)
      # Regress GxE terms on envt means
-  getab = (model.tables(m2,"effects")$tables)$'gen:env'
-  getab
-
    #>     env
-#> gen  E1        E2        E3        E4        E5        E6       
-#>   G1   98.3000  -33.5500  291.1000  -72.2500 -320.9500   37.3500
-#>   G2  451.1333 -382.2167  -99.0667   58.5833  181.1333 -209.5667
-#>   G3  190.2167  529.6167 -297.7333 -343.8333 -197.7833  119.5167
-#>   G4 -349.3667  273.2833 -453.0667  124.0833  310.1333   94.9333
-#>   G5 -390.2833 -387.1333  558.7667  233.4167   27.4667  -42.2333
      for (ll in 1:nrow(getab)){
-    m7l <- lm(getab[ll, ] ~ emn$ymn)
-    cat("\n\n*************** Gen ",ll," ***************\n") 
-    cat("Regression coefficient: ",round(coefficients(m7l)[2],5),"\n") 
-    print(anova(m7l)) 
-  } # Match Hwu table 7.
-
    #> 
-#> 
-#> *************** Gen  1  ***************
-#> Regression coefficient:  0.09534 
-#> Analysis of Variance Table
-#> 
-#> Response: getab[ll, ]
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> emn$ymn    1  37638   37638  0.8987 0.3968
-#> Residuals  4 167514   41879               
-#> 
-#> 
-#> *************** Gen  2  ***************
-#> Regression coefficient:  0.11406 
-#> Analysis of Variance Table
-#> 
-#> Response: getab[ll, ]
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> emn$ymn    1  53870   53870  0.5586 0.4963
-#> Residuals  4 385715   96429               
-#> 
-#> 
-#> *************** Gen  3  ***************
-#> Regression coefficient:  -0.18889 
-#> Analysis of Variance Table
-#> 
-#> Response: getab[ll, ]
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> emn$ymn    1 147748  147748   1.377 0.3057
-#> Residuals  4 429197  107299               
-#> 
-#> 
-#> *************** Gen  4  ***************
-#> Regression coefficient:  -0.19322 
-#> Analysis of Variance Table
-#> 
-#> Response: getab[ll, ]
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> emn$ymn    1 154598  154598  1.6804 0.2646
-#> Residuals  4 368004   92001               
-#> 
-#> 
-#> *************** Gen  5  ***************
-#> Regression coefficient:  0.17272 
-#> Analysis of Variance Table
-#> 
-#> Response: getab[ll, ]
-#>           Df Sum Sq Mean Sq F value Pr(>F)
-#> emn$ymn    1 123529  123529  0.9018 0.3961
-#> Residuals  4 547906  136976               
    
-# } # dontrun
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lucas.switchback-1.png b/docs/reference/lucas.switchback-1.png deleted file mode 100644 index 95f4eeb7..00000000 Binary files a/docs/reference/lucas.switchback-1.png and /dev/null differ diff --git a/docs/reference/lucas.switchback.html b/docs/reference/lucas.switchback.html deleted file mode 100644 index a818fb2d..00000000 --- a/docs/reference/lucas.switchback.html +++ /dev/null @@ -1,269 +0,0 @@ - - - - - - - - -Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -
    -

    Switchback experiment on dairy cattle, milk yield for 3 treatments

    - - -
    - -
    -

    Switchback experiment on dairy cattle, milk yield for 3 treatments

    -
    - - 
    data("lucas.switchback")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 5 variables.

    -
    cow

    cow factor, 12 levels

    -
    trt

    treatment factor, 3 levels

    -
    period

    period factor, 3 levels

    -
    yield

    yield (FCM = fat corrected milk), pounds/day

    -
    block

    block factor

    - -
    - -

    Details

    - - - -

    Lucas says - "because no data from feeding trials employing the present designs are yet available, uniformity data will be used".

    -

    Six cows were started together in block 1, then three cows in block 2 - and three cows in block 3.

    -

    Source

    - -

    Lucas, HL. 1956. - Switchback trials for more than two treatments. - Journal of Dairy Science, 39, 146-154. - https://doi.org/10.3168/jds.S0022-0302(56)94721-X

    -

    References

    - -

    Sanders, WL and Gaynor, PJ. 1987. - Analysis of Switchback Data Using Statistical Analysis System. - Journal of Dairy Science, 70, 2186-2191. - https://doi.org/10.3168/jds.S0022-0302(87)80273-4

    - -

    Examples

    - 
    
-library(agridat)
-
-data(lucas.switchback)
-dat <- lucas.switchback
-
-# Create a numeric period variable
-dat$per <- as.numeric(substring(dat$period,2))
-
-libs(lattice)
-xyplot(yield ~ period|block, data=dat, group=cow, type=c('l','r'),
-       auto.key=list(columns=6),
-       main="lucas.switchback - (actually uniformity data)")
-
    
-# Need to use 'terms' to preserve the order of the model terms
-# Really, cow(block), per:cow(block), period(block)
-m1 <- aov(terms(yield ~ block + cow:block + per:cow:block +
-                period:block + trt, keep.order=TRUE), data=dat)
-anova(m1) # Match Sanders & Gaynor table 3
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>               Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> block          2   30.93  15.464  55.345 5.132e-05 ***
-#> block:cow      9 1700.97 188.997 676.426 1.907e-09 ***
-#> block:cow:per 12  120.47  10.040  35.932 4.137e-05 ***
-#> block:period   3   14.85   4.950  17.717  0.001194 ** 
-#> trt            2    1.58   0.789   2.825  0.126048    
-#> Residuals      7    1.96   0.279                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Analysis of Variance Table
-##               Df  Sum Sq Mean Sq F value    Pr(>F)
-## block          2   30.93  15.464  55.345 5.132e-05 ***
-## block:cow      9 1700.97 188.997 676.426 1.907e-09 ***
-## block:cow:per 12  120.47  10.040  35.932 4.137e-05 ***
-## block:period   3   14.85   4.950  17.717  0.001194 **
-## trt            2    1.58   0.789   2.825  0.126048
-## Residuals      7    1.96   0.279
-
-coef(m1) # trtT2 and trtT3 match Sanders table 3 trt diffs
-
    #>        (Intercept)            blockB2            blockB3     blockB3:cowC10 
-#>         37.5833333         -4.1750000        -14.7250000         18.0083333 
-#>     blockB3:cowC11      blockB1:cowC2      blockB1:cowC3      blockB1:cowC4 
-#>          4.6041667        -12.5375000         -1.1416667         14.9402778 
-#>      blockB1:cowC5      blockB1:cowC6      blockB2:cowC7      blockB2:cowC8 
-#>        -15.1555556        -10.9305556         -1.0291667          5.7416667 
-#>  blockB1:cowC1:per blockB3:cowC10:per blockB3:cowC11:per blockB3:cowC12:per 
-#>         -3.0500000         -2.1500000         -1.1500000         -1.0000000 
-#>  blockB1:cowC2:per  blockB1:cowC3:per  blockB1:cowC4:per  blockB1:cowC5:per 
-#>         -2.1000000         -2.7000000         -3.4500000         -0.0500000 
-#>  blockB1:cowC6:per  blockB2:cowC7:per  blockB2:cowC8:per  blockB2:cowC9:per 
-#>         -0.7500000         -1.8500000         -3.4000000         -2.2000000 
-#>   blockB1:periodP2   blockB2:periodP2   blockB3:periodP2              trtT2 
-#>          1.4833333          1.1166667          1.3333333         -0.5333333 
-#>              trtT3 
-#>         -0.5541667 
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lyon.potato.uniformity-1.png b/docs/reference/lyon.potato.uniformity-1.png deleted file mode 100644 index 582f786e..00000000 Binary files a/docs/reference/lyon.potato.uniformity-1.png and /dev/null differ diff --git a/docs/reference/lyon.potato.uniformity.html b/docs/reference/lyon.potato.uniformity.html deleted file mode 100644 index 04d8e22a..00000000 --- a/docs/reference/lyon.potato.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trial of potatoes — lyon.potato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trial of potatoes

    - - -
    - -
    -

    Uniformity trial of potatoes at Nebraska Experiment Station, 1909.

    -
    - - - -

    Format

    - -

    A data frame with 204 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column, section

    -
    yield

    yield, pounds

    - -
    - -

    Details

    - -

    In 1909, potatoes were harvested from uniform land at Nebraska - Experiment Station.

    -

    There were 34 rows, 34 inches apart. - Lyon, page 97 says "He harvested each row in six sections, - each of which was seventy-two feet and seven inches long." - It is not clear if each section is 72 feet long, or if the - entire row is 72 feet long. Yield of potato is roughly 0.5 to 0.8 - pounds per square foot, so it seems more plausible the entire row is - 72 feet long (see calculations below).

    -

    Field width: 6 plots = 72 feet

    -

    Field length: 34 rows * 34 in / 12in/ft = 96 ft

    -

    Source

    - -

    Lyon, T.L. (1911). - Some experiments to estimate errors in field plat tests. - Proc. Amer. Soc. Agron, 3, 89-114. Table III. - https://doi.org/10.2134/agronj1911.00021962000300010016x

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(lyon.potato.uniformity)
-  dat <- lyon.potato.uniformity
-
-  # Yield per square foot, assuming 72 foot rows
-  sum(dat$yield)/(72*96) # 0.67 # seems about right
-
    #> [1] 0.6738571
      # Yield per square foot, assuming 72 foot plots
-  sum(dat$yield)/(6*72*96) # 0.11
-
    #> [1] 0.1123095
      
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          tick=TRUE, flip=TRUE, aspect=96/72, # true aspect
-          main="lyon.potato.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/lyons.wheat-1.png b/docs/reference/lyons.wheat-1.png deleted file mode 100644 index 6fac8b80..00000000 Binary files a/docs/reference/lyons.wheat-1.png and /dev/null differ diff --git a/docs/reference/lyons.wheat.html b/docs/reference/lyons.wheat.html deleted file mode 100644 index 45bddf64..00000000 --- a/docs/reference/lyons.wheat.html +++ /dev/null @@ -1,211 +0,0 @@ - - - - - - - - -Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    Multi-environment trial of winter wheat at 12 sites in 4 years.

    - - -
    - -
    -

    Yield of winter wheat at 12 sites in 4 years.

    -
    - - - -

    Format

    - -

    A data frame with 48 observations on the following 3 variables.

    -
    loc

    location, 12 levels

    -
    year

    year, numeric

    -
    yield

    yield (kg)

    - -
    - -

    Details

    - -

    Krzanowski uses this briefly for multi-dimensional scaling.

    -

    Source

    - -

    R. Lyons (1980). A review of multidimensional scaling. - Unpublished M.Sc. dissertation, University of Reading.

    -

    References

    - -

    Krzanowski, W.J. (1988) - Principles of multivariate analysis. Oxford University Press.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(lyons.wheat)
-dat <- lyons.wheat
-
-libs(lattice)
-xyplot(yield~factor(year), dat, group=loc,
-  main="lyons.wheat",
-  auto.key=list(columns=4), type=c('p','l'))
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/magistad.pineapple.uniformity-1.png b/docs/reference/magistad.pineapple.uniformity-1.png deleted file mode 100644 index 058fcd58..00000000 Binary files a/docs/reference/magistad.pineapple.uniformity-1.png and /dev/null differ diff --git a/docs/reference/magistad.pineapple.uniformity-2.png b/docs/reference/magistad.pineapple.uniformity-2.png deleted file mode 100644 index 79c5d99d..00000000 Binary files a/docs/reference/magistad.pineapple.uniformity-2.png and /dev/null differ diff --git a/docs/reference/magistad.pineapple.uniformity-3.png b/docs/reference/magistad.pineapple.uniformity-3.png deleted file mode 100644 index f602cd67..00000000 Binary files a/docs/reference/magistad.pineapple.uniformity-3.png and /dev/null differ diff --git a/docs/reference/magistad.pineapple.uniformity-4.png b/docs/reference/magistad.pineapple.uniformity-4.png deleted file mode 100644 index 6ce7a07f..00000000 Binary files a/docs/reference/magistad.pineapple.uniformity-4.png and /dev/null differ diff --git a/docs/reference/magistad.pineapple.uniformity.html b/docs/reference/magistad.pineapple.uniformity.html deleted file mode 100644 index ba4329d9..00000000 --- a/docs/reference/magistad.pineapple.uniformity.html +++ /dev/null @@ -1,264 +0,0 @@ - - - - - - - - -Uniformity trial of pineapple — magistad.pineapple.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trial of pineapple

    - - -
    - -
    -

    Uniformity trial of pineapple in Hawaii in 1932

    -
    - - 
    data("magistad.pineapple.uniformity")
    - - -

    Format

    - -

    A data frame with 137 observations on the following 6 variables.

    -
    field

    field number

    -
    plat

    plat number

    -
    row

    row

    -
    col

    column

    -
    number

    number of fruits

    -
    weight

    weight of fruits, grams

    - -
    - -

    Details

    - - - -

    Field 19. Kunia. Harvested 1932.

    -

    "In this field, harvested in 1932, there were four rows per bed. A 300-foot bed was divided into four equal parts to form plats 1, 2, 3, and 4. The third [sic, second] bed from this was similarly divided to form plats 5 to 8, inclusive. In the same manner plats 9 to 24 were formed. In this way 24 plats each 75 feet long and 1 bed wide were formed."

    -

    Field length: 4 plats * 75 feet = 300 feet

    -

    Field width: 6 plats * 6.5 feet = 39 feet

    - -

    Field 82. Pearl City.

    -

    "Eight beds, each separated by two beds, were selected and harvested. Beds were 8 feet center to center. Each bed was divided into three plats 76 feet long."

    -

    Note: Layout of plats into rows/columns assumes the same pattern as field 19.

    -

    Field length: 3 plats * 76 feet = 228 feet

    -

    Field width: 8 plats * 8 feet * 3 (every third bed) = 192 feet.

    - -

    Field 21. Kahuku.

    -

    "In field 21, Kahuku, the experimental plan was of the Latin square type, having five beds of five plats each. The beds were 7.5 feet center to center. Each plat was approximately 60 feet long and each third bed was selected and harvested." -Note: Layout of plats into rows/columns assumes the same pattern as field 19.

    -

    Field lenght: 5 plats * 60 feet = 300 feet

    -

    Field width: 5 plats * 7.5 feet * 3 (every third bed) = 112.5 feet

    - -

    Field 1. Kunia.

    -

    "This experiment was another Latin square test having eight plats in each column and eight plats in each row. It was harvested in 1930. Each plat consisted of two beds 150 feet long. Beds were 6 feet center to center and consisted of three rows each. The entire experimental area occupied 2.85 acres."

    -

    Field length: 8 plats * 2 beds * 150 feet = 2400 feet

    -

    Field width: 8 plats * 6 feet = 48 feet

    -

    Total area: 2400*48/43560=2.64 acres

    -

    Source

    - -

    O. C. Magistad & C. A. Farden (1934). - Experimental Error In Field Experiments With Pineapples. - Journal of the American Society of Agronomy, 26, 631--643.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(magistad.pineapple.uniformity)
-  dat <- magistad.pineapple.uniformity
-
-  # match table page 641
-  ## dat 
-  ##   summarize(number=mean(number),
-  ##             weight=mean(weight)) 
-  ##   field   number   weight
-  ## 1     1 596.4062 2499.922
-  ## 2    19 171.1667 2100.250
-  ## 3    21 171.1600 2056.800
-  ## 4    82 220.7500 1264.500
-  
-  libs(desplot)
-  desplot(dat, weight ~ col*row,
-          subset=field==1,
-          aspect=2400/48,
-          main="magistad.pineapple.uniformity - field 1")          
-
      desplot(dat, weight ~ col*row,
-          subset=field==19,
-          aspect=300/39,
-          main="magistad.pineapple.uniformity - field 19")
-
      desplot(dat, weight ~ col*row,
-          subset=field==82,
-          aspect=228/192,
-          main="magistad.pineapple.uniformity - field 82")          
-
      desplot(dat, weight ~ col*row,
-          subset=field==21,
-          aspect=300/112.5,
-          main="magistad.pineapple.uniformity - field 21")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/masood.rice.uniformity-1.png b/docs/reference/masood.rice.uniformity-1.png deleted file mode 100644 index 7736a6e9..00000000 Binary files a/docs/reference/masood.rice.uniformity-1.png and /dev/null differ diff --git a/docs/reference/masood.rice.uniformity-2.png b/docs/reference/masood.rice.uniformity-2.png deleted file mode 100644 index 4e79ea9f..00000000 Binary files a/docs/reference/masood.rice.uniformity-2.png and /dev/null differ diff --git a/docs/reference/masood.rice.uniformity.html b/docs/reference/masood.rice.uniformity.html deleted file mode 100644 index fe85cde9..00000000 --- a/docs/reference/masood.rice.uniformity.html +++ /dev/null @@ -1,237 +0,0 @@ - - - - - - - - -Uniformity trial of rice — masood.rice.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of rice

    - - -
    - -
    -

    Uniformity trial of rice at Lahore, Punjab, circa 2011.

    -
    - - 
    data("masood.rice.uniformity")
    - - -

    Format

    - -

    A data frame with 288 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, kg/m^2

    - -
    - -

    Details

    - -

    Data by collected from the Rice Research Institute on a paddy yield trial. - A single variety of rice was harvested in an area 12m x 24 m. - Yield in kilograms was measured for each square meter. - Masood et al report a low degree of similarity for neighboring plots.

    -

    Note, the Smith index calculations below match the results in the - Pakistan Journal of Agricultural Research, but do not match the - results in the American-Eurasian Journal, which seems to be the same - paper and seems to refer to the same data. The results may simply - differ by a scaling factor.

    -

    Field length: 24 plots x 1m = 24m.

    -

    Field width: 12 plots x 1m = 12m.

    -

    Used with permission of Asif Masood.

    -

    Source

    - -

    Masood, M Asif and Raza, Irum. 2012. - Estimation of optimum field plot size and shape in paddy yield trial. - Pakistan J. Agric. Res., Vol. 25 No. 4, 2012

    -

    References

    - -

    Masood, M Asif and Raza, Irum. 2012. - Estimation of optimum field plot size and shape in paddy yield trial. - American-Eurasian Journal of Scientific Research, 7, 264-269. - Table 1. - https://doi.org/10.5829/idosi.aejsr.2012.7.6.1926

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(masood.rice.uniformity)
-  dat <- masood.rice.uniformity
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, tick=TRUE, aspect=24/12, # true aspect
-          main="masood.rice.uniformity - yield heatmap")
-
    
-  libs(agricolae)
-  libs(reshape2)
-  dmat <- acast(dat, row~col, value.var='yield')
-  index.smith(dmat,
-              main="masood.rice.uniformity",
-              col="red") # CVs match Table 3
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mcclelland.corn.uniformity-1.png b/docs/reference/mcclelland.corn.uniformity-1.png deleted file mode 100644 index fe5f5e5f..00000000 Binary files a/docs/reference/mcclelland.corn.uniformity-1.png and /dev/null differ diff --git a/docs/reference/mcclelland.corn.uniformity.html b/docs/reference/mcclelland.corn.uniformity.html deleted file mode 100644 index d6f31da1..00000000 --- a/docs/reference/mcclelland.corn.uniformity.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - -Uniformity trial of corn — mcclelland.corn.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of corn

    - - -
    - -
    -

    Uniformity trial of corn at Arkansas Experiment Station, 1925.

    -
    - - 
    data("mcclelland.corn.uniformity")
    - - -

    Format

    - -

    A data frame with 438 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - - - -

    A uniformity trial of corn in 1925 at the Arkansas Experimental Station.

    -

    Field width = 66ft * 2 = 132 feet.

    -

    Field length = 219 rows * 44 inches / 12 inches/ft = 803 ft.

    -

    Note: In the source document, table 2, first 'west' column and - second-to-last row (page 822), the value 1.40 is assumed to be a - typographical error and was changed to 14.0 for this data.

    -

    Source

    - -

    McClelland, Chalmer Kirk (1926). - Some determinations of plat variability. - Agronomy Journal, 18, 819-823. - https://doi.org/10.2134/agronj1926.00021962001800090009x

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(mcclelland.corn.uniformity)
-  dat <- mcclelland.corn.uniformity
-  
-  # McClelland table 3, first row, gives 11.2
-  # Probable error = 0.67449 * sd(). Relative to mean.
-  # 0.67449 * sd(dat$yield)/mean(dat$yield) # 11.2
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE,
-          aspect=(219*44/12)/132, # true aspect, 219 rows * 44 inches x 132 feet
-          main="mcclelland.corn.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mcconway.turnip-1.png b/docs/reference/mcconway.turnip-1.png deleted file mode 100644 index 69c9d647..00000000 Binary files a/docs/reference/mcconway.turnip-1.png and /dev/null differ diff --git a/docs/reference/mcconway.turnip.html b/docs/reference/mcconway.turnip.html deleted file mode 100644 index 41941341..00000000 --- a/docs/reference/mcconway.turnip.html +++ /dev/null @@ -1,587 +0,0 @@ - - - - - - - - -RCB experiment of turnips — mcconway.turnip • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of turnips

    - - -
    - -
    -

    RCB experiment of turnips, 2 treatments for planting date and density

    -
    - - - -

    Format

    - -

    A data frame with 64 observations on the following 6 variables.

    -
    gen

    genotype

    -
    date

    planting date, levels 21Aug1990 28Aug1990

    -
    density

    planting density, 1, 2, 4, 8 kg/ha

    -
    block

    block, 4 levels

    -
    yield

    yield

    - -
    - -

    Details

    - -

    This is a randomized block experiment with 16 treatments - allocated at random to each of four blocks. The 16 treatments - were combinations of two varieties, two planting dates, and - four densities.

    -

    Lee et al (2008) proposed an analysis using mixed models with changing - treatment variances.

    -

    Piepho (2009) proposed an ordinary ANOVA using transformed data.

    -

    Used with permission of Kevin McConway.

    -

    Source

    - -

    K. J. McConway, M. C. Jones, P. C. Taylor. - Statistical Modelling Using Genstat.

    -

    References

    - -

    Michael Berthold, D. J. Hand. - Intelligent data analysis: an introduction, 1998. - Pages 75--82.

    -

    Lee, C.J. and O Donnell, M. and O Neill, M. (2008). - Statistical analysis of field trials with changing treatment - variance. - Agronomy Journal, 100, 484--489.

    -

    Piepho, H.P. (2009), - Data transformation in statistical analysis of field trials - with changing treatment variance. - Agronomy Journal, 101, 865--869. - https://doi.org/10.2134/agronj2008.0226x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mcconway.turnip)
-dat <- mcconway.turnip
-dat$densf <- factor(dat$density)
-
-# Table 2 of Lee et al.
-m0 <- aov( yield ~ gen * densf * date + block, dat )
-summary(m0)
-
    #>                Df Sum Sq Mean Sq F value   Pr(>F)    
-#> gen             1   84.0   83.95   8.753  0.00491 ** 
-#> densf           3  470.4  156.79  16.347 2.51e-07 ***
-#> date            1  233.7  233.71  24.367 1.14e-05 ***
-#> block           3  163.7   54.58   5.690  0.00216 ** 
-#> gen:densf       3    8.6    2.88   0.301  0.82485    
-#> gen:date        1   36.5   36.45   3.800  0.05749 .  
-#> densf:date      3  154.8   51.60   5.380  0.00299 ** 
-#> gen:densf:date  3   18.0    6.00   0.626  0.60224    
-#> Residuals      45  431.6    9.59                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ##                Df Sum Sq Mean Sq F value   Pr(>F)
-## gen             1   84.0   83.95   8.753  0.00491 **
-## densf           3  470.4  156.79  16.347 2.51e-07 ***
-## date            1  233.7  233.71  24.367 1.14e-05 ***
-## block           3  163.7   54.58   5.690  0.00216 **
-## gen:densf       3    8.6    2.88   0.301  0.82485
-## gen:date        1   36.5   36.45   3.800  0.05749 .
-## densf:date      3  154.8   51.60   5.380  0.00299 **
-## gen:densf:date  3   18.0    6.00   0.626  0.60224
-## Residuals      45  431.6    9.59
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-# Boxplots suggest heteroskedasticity for date, density
-libs("HH")
-interaction2wt(yield ~ gen + date + densf +block, dat,
-               x.between=0, y.between=0,
-               main="mcconway.turnip - yield")
-
    
-
-libs(nlme)
-# Random block model
-m1 <- lme(yield ~ gen * date * densf, random= ~1|block, data=dat)
-summary(m1)
-
    #> Linear mixed-effects model fit by REML
-#>  Data: dat 
-#>        AIC      BIC    logLik
-#>   308.1365 341.8181 -136.0683
-#> 
-#> Random effects:
-#>  Formula: ~1 | block
-#>         (Intercept) Residual
-#> StdDev:    1.676819 3.096991
-#> 
-#> Fixed effects: yield ~ gen * date * densf 
-#>                                Value Std.Error DF    t-value p-value
-#> (Intercept)                    2.275  1.760900 45  1.2919531  0.2030
-#> genMarco                      -1.025  2.189903 45 -0.4680573  0.6420
-#> date28Aug1990                  1.325  2.189903 45  0.6050496  0.5482
-#> densf2                         1.550  2.189903 45  0.7077939  0.4827
-#> densf4                         1.875  2.189903 45  0.8562023  0.3964
-#> densf8                         2.900  2.189903 45  1.3242595  0.1921
-#> genMarco:date28Aug1990        -1.150  3.096991 45 -0.3713282  0.7121
-#> genMarco:densf2               -0.600  3.096991 45 -0.1937365  0.8473
-#> genMarco:densf4                0.550  3.096991 45  0.1775918  0.8598
-#> genMarco:densf8                1.025  3.096991 45  0.3309665  0.7422
-#> date28Aug1990:densf2          -0.175  3.096991 45 -0.0565065  0.9552
-#> date28Aug1990:densf4           8.550  3.096991 45  2.7607446  0.0083
-#> date28Aug1990:densf8           7.650  3.096991 45  2.4701399  0.0174
-#> genMarco:date28Aug1990:densf2  0.200  4.379806 45  0.0456641  0.9638
-#> genMarco:date28Aug1990:densf4 -4.950  4.379806 45 -1.1301870  0.2644
-#> genMarco:date28Aug1990:densf8 -2.725  4.379806 45 -0.6221737  0.5370
-#>  Correlation: 
-#>                               (Intr) genMrc dt28A1990 densf2 densf4 densf8
-#> genMarco                      -0.622                                      
-#> date28Aug1990                 -0.622  0.500                               
-#> densf2                        -0.622  0.500  0.500                        
-#> densf4                        -0.622  0.500  0.500     0.500              
-#> densf8                        -0.622  0.500  0.500     0.500  0.500       
-#> genMarco:date28Aug1990         0.440 -0.707 -0.707    -0.354 -0.354 -0.354
-#> genMarco:densf2                0.440 -0.707 -0.354    -0.707 -0.354 -0.354
-#> genMarco:densf4                0.440 -0.707 -0.354    -0.354 -0.707 -0.354
-#> genMarco:densf8                0.440 -0.707 -0.354    -0.354 -0.354 -0.707
-#> date28Aug1990:densf2           0.440 -0.354 -0.707    -0.707 -0.354 -0.354
-#> date28Aug1990:densf4           0.440 -0.354 -0.707    -0.354 -0.707 -0.354
-#> date28Aug1990:densf8           0.440 -0.354 -0.707    -0.354 -0.354 -0.707
-#> genMarco:date28Aug1990:densf2 -0.311  0.500  0.500     0.500  0.250  0.250
-#> genMarco:date28Aug1990:densf4 -0.311  0.500  0.500     0.250  0.500  0.250
-#> genMarco:date28Aug1990:densf8 -0.311  0.500  0.500     0.250  0.250  0.500
-#>                               gnM:28A1990 gnMr:2 gnMr:4 gnMr:8 d28A1990:2
-#> genMarco                                                                 
-#> date28Aug1990                                                            
-#> densf2                                                                   
-#> densf4                                                                   
-#> densf8                                                                   
-#> genMarco:date28Aug1990                                                   
-#> genMarco:densf2                0.500                                     
-#> genMarco:densf4                0.500       0.500                         
-#> genMarco:densf8                0.500       0.500  0.500                  
-#> date28Aug1990:densf2           0.500       0.500  0.250  0.250           
-#> date28Aug1990:densf4           0.500       0.250  0.500  0.250  0.500    
-#> date28Aug1990:densf8           0.500       0.250  0.250  0.500  0.500    
-#> genMarco:date28Aug1990:densf2 -0.707      -0.707 -0.354 -0.354 -0.707    
-#> genMarco:date28Aug1990:densf4 -0.707      -0.354 -0.707 -0.354 -0.354    
-#> genMarco:date28Aug1990:densf8 -0.707      -0.354 -0.354 -0.707 -0.354    
-#>                               d28A1990:4 d28A1990:8 gM:28A1990:2 gM:28A1990:4
-#> genMarco                                                                     
-#> date28Aug1990                                                                
-#> densf2                                                                       
-#> densf4                                                                       
-#> densf8                                                                       
-#> genMarco:date28Aug1990                                                       
-#> genMarco:densf2                                                              
-#> genMarco:densf4                                                              
-#> genMarco:densf8                                                              
-#> date28Aug1990:densf2                                                         
-#> date28Aug1990:densf4                                                         
-#> date28Aug1990:densf8           0.500                                         
-#> genMarco:date28Aug1990:densf2 -0.354     -0.354                              
-#> genMarco:date28Aug1990:densf4 -0.707     -0.354      0.500                   
-#> genMarco:date28Aug1990:densf8 -0.354     -0.707      0.500        0.500      
-#> 
-#> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -2.95673473 -0.47103959 -0.08756561  0.45826905  2.90356710 
-#> 
-#> Number of Observations: 64
-#> Number of Groups: 4 
    anova(m1)
-
    #>                numDF denDF  F-value p-value
-#> (Intercept)        1    45 33.89726  <.0001
-#> gen                1    45  8.75282  0.0049
-#> date               1    45 24.36650  <.0001
-#> densf              3    45 16.34730  <.0001
-#> gen:date           1    45  3.80045  0.0575
-#> gen:densf          3    45  0.30050  0.8248
-#> date:densf         3    45  5.37960  0.0030
-#> gen:date:densf     3    45  0.62554  0.6022
    
-# Multiplicative variance model over densities and dates
-m2 <- update(m1,
-             weights=varComb(varIdent(form=~1|densf),
-               varIdent(form=~1|date)))
-summary(m2)
-
    #> Linear mixed-effects model fit by REML
-#>  Data: dat 
-#>        AIC      BIC    logLik
-#>   294.2643 335.4307 -125.1322
-#> 
-#> Random effects:
-#>  Formula: ~1 | block
-#>         (Intercept) Residual
-#> StdDev:   0.4004249 1.014646
-#> 
-#> Combination of variance functions: 
-#>  Structure: Different standard deviations per stratum
-#>  Formula: ~1 | densf 
-#>  Parameter estimates:
-#>        1        2        4        8 
-#> 1.000000 1.481556 3.237435 2.771912 
-#>  Structure: Different standard deviations per stratum
-#>  Formula: ~1 | date 
-#>  Parameter estimates:
-#> 21Aug1990 28Aug1990 
-#>  1.000000  1.747335 
-#> Fixed effects: yield ~ gen * date * densf 
-#>                                Value Std.Error DF   t-value p-value
-#> (Intercept)                    2.275  0.545401 45  4.171245  0.0001
-#> genMarco                      -1.025  0.717463 45 -1.428644  0.1600
-#> date28Aug1990                  1.325  1.021369 45  1.297278  0.2011
-#> densf2                         1.550  0.906819 45  1.709271  0.0943
-#> densf4                         1.875  1.718994 45  1.090754  0.2812
-#> densf8                         2.900  1.494969 45  1.939840  0.0587
-#> genMarco:date28Aug1990        -1.150  1.444434 45 -0.796160  0.4301
-#> genMarco:densf2               -0.600  1.282436 45 -0.467860  0.6421
-#> genMarco:densf4                0.550  2.431025 45  0.226242  0.8220
-#> genMarco:densf8                1.025  2.114205 45  0.484816  0.6302
-#> date28Aug1990:densf2          -0.175  1.825655 45 -0.095856  0.9241
-#> date28Aug1990:densf4           8.550  3.460766 45  2.470551  0.0173
-#> date28Aug1990:densf8           7.650  3.009747 45  2.541742  0.0145
-#> genMarco:date28Aug1990:densf2  0.200  2.581866 45  0.077463  0.9386
-#> genMarco:date28Aug1990:densf4 -4.950  4.894263 45 -1.011388  0.3172
-#> genMarco:date28Aug1990:densf8 -2.725  4.256425 45 -0.640209  0.5253
-#>  Correlation: 
-#>                               (Intr) genMrc dt28A1990 densf2 densf4 densf8
-#> genMarco                      -0.658                                      
-#> date28Aug1990                 -0.462  0.351                               
-#> densf2                        -0.520  0.396  0.278                        
-#> densf4                        -0.275  0.209  0.147     0.165              
-#> densf8                        -0.316  0.240  0.169     0.190  0.100       
-#> genMarco:date28Aug1990         0.327 -0.497 -0.707    -0.196 -0.104 -0.119
-#> genMarco:densf2                0.368 -0.559 -0.196    -0.707 -0.117 -0.134
-#> genMarco:densf4                0.194 -0.295 -0.104    -0.117 -0.707 -0.071
-#> genMarco:densf8                0.223 -0.339 -0.119    -0.134 -0.071 -0.707
-#> date28Aug1990:densf2           0.258 -0.196 -0.559    -0.497 -0.082 -0.094
-#> date28Aug1990:densf4           0.136 -0.104 -0.295    -0.082 -0.497 -0.050
-#> date28Aug1990:densf8           0.157 -0.119 -0.339    -0.094 -0.050 -0.497
-#> genMarco:date28Aug1990:densf2 -0.183  0.278  0.396     0.351  0.058  0.067
-#> genMarco:date28Aug1990:densf4 -0.096  0.147  0.209     0.058  0.351  0.035
-#> genMarco:date28Aug1990:densf8 -0.111  0.169  0.240     0.067  0.035  0.351
-#>                               gnM:28A1990 gnMr:2 gnMr:4 gnMr:8 d28A1990:2
-#> genMarco                                                                 
-#> date28Aug1990                                                            
-#> densf2                                                                   
-#> densf4                                                                   
-#> densf8                                                                   
-#> genMarco:date28Aug1990                                                   
-#> genMarco:densf2                0.278                                     
-#> genMarco:densf4                0.147       0.165                         
-#> genMarco:densf8                0.169       0.190  0.100                  
-#> date28Aug1990:densf2           0.396       0.351  0.058  0.067           
-#> date28Aug1990:densf4           0.209       0.058  0.351  0.035  0.165    
-#> date28Aug1990:densf8           0.240       0.067  0.035  0.351  0.190    
-#> genMarco:date28Aug1990:densf2 -0.559      -0.497 -0.082 -0.094 -0.707    
-#> genMarco:date28Aug1990:densf4 -0.295      -0.082 -0.497 -0.050 -0.117    
-#> genMarco:date28Aug1990:densf8 -0.339      -0.094 -0.050 -0.497 -0.134    
-#>                               d28A1990:4 d28A1990:8 gM:28A1990:2 gM:28A1990:4
-#> genMarco                                                                     
-#> date28Aug1990                                                                
-#> densf2                                                                       
-#> densf4                                                                       
-#> densf8                                                                       
-#> genMarco:date28Aug1990                                                       
-#> genMarco:densf2                                                              
-#> genMarco:densf4                                                              
-#> genMarco:densf8                                                              
-#> date28Aug1990:densf2                                                         
-#> date28Aug1990:densf4                                                         
-#> date28Aug1990:densf8           0.100                                         
-#> genMarco:date28Aug1990:densf2 -0.117     -0.134                              
-#> genMarco:date28Aug1990:densf4 -0.707     -0.071      0.165                   
-#> genMarco:date28Aug1990:densf8 -0.071     -0.707      0.190        0.100      
-#> 
-#> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -1.94246823 -0.54962541 -0.01044348  0.47135647  2.10724103 
-#> 
-#> Number of Observations: 64
-#> Number of Groups: 4 
    anova(m2)
-
    #>                numDF denDF  F-value p-value
-#> (Intercept)        1    45 84.42187  <.0001
-#> gen                1    45  9.35277  0.0037
-#> date               1    45  7.85586  0.0074
-#> densf              3    45 12.82334  <.0001
-#> gen:date           1    45  2.03115  0.1610
-#> gen:densf          3    45  0.13262  0.9401
-#> date:densf         3    45  4.83297  0.0053
-#> gen:date:densf     3    45  0.47936  0.6983
    
-# Unstructured variance model over densities and dates
-m3 <- update(m1, weights=varIdent(form=~1|densf*date))
-summary(m3)
-
    #> Linear mixed-effects model fit by REML
-#>  Data: dat 
-#>        AIC      BIC    logLik
-#>   298.2036 344.9836 -124.1018
-#> 
-#> Random effects:
-#>  Formula: ~1 | block
-#>         (Intercept)  Residual
-#> StdDev:   0.2719394 0.9379041
-#> 
-#> Variance function:
-#>  Structure: Different standard deviations per stratum
-#>  Formula: ~1 | densf * date 
-#>  Parameter estimates:
-#> 1*21Aug1990 2*21Aug1990 4*21Aug1990 8*21Aug1990 1*28Aug1990 2*28Aug1990 
-#>    1.000000    1.871530    2.668169    3.330860    1.979384    2.383431 
-#> 4*28Aug1990 8*28Aug1990 
-#>    7.394191    4.760777 
-#> Fixed effects: yield ~ gen * date * densf 
-#>                                Value Std.Error DF   t-value p-value
-#> (Intercept)                    2.275  0.488266 45  4.659345  0.0000
-#> genMarco                      -1.025  0.663198 45 -1.545541  0.1292
-#> date28Aug1990                  1.325  1.039970 45  1.274075  0.2092
-#> densf2                         1.550  0.995088 45  1.557652  0.1263
-#> densf4                         1.875  1.336236 45  1.403195  0.1674
-#> densf8                         2.900  1.630890 45  1.778170  0.0821
-#> genMarco:date28Aug1990        -1.150  1.470740 45 -0.781919  0.4384
-#> genMarco:densf2               -0.600  1.407266 45 -0.426358  0.6719
-#> genMarco:densf4                0.550  1.889723 45  0.291048  0.7724
-#> genMarco:densf8                1.025  2.306427 45  0.444410  0.6589
-#> date28Aug1990:densf2          -0.175  1.760996 45 -0.099376  0.9213
-#> date28Aug1990:densf4           8.550  3.830255 45  2.232228  0.0306
-#> date28Aug1990:densf8           7.650  2.916474 45  2.623030  0.0119
-#> genMarco:date28Aug1990:densf2  0.200  2.490425 45  0.080308  0.9363
-#> genMarco:date28Aug1990:densf4 -4.950  5.416798 45 -0.913824  0.3657
-#> genMarco:date28Aug1990:densf8 -2.725  4.124517 45 -0.660683  0.5122
-#>  Correlation: 
-#>                               (Intr) genMrc dt28A1990 densf2 densf4 densf8
-#> genMarco                      -0.679                                      
-#> date28Aug1990                 -0.433  0.319                               
-#> densf2                        -0.453  0.333  0.213                        
-#> densf4                        -0.337  0.248  0.158     0.165              
-#> densf8                        -0.276  0.203  0.130     0.136  0.101       
-#> genMarco:date28Aug1990         0.306 -0.451 -0.707    -0.150 -0.112 -0.092
-#> genMarco:densf2                0.320 -0.471 -0.150    -0.707 -0.117 -0.096
-#> genMarco:densf4                0.238 -0.351 -0.112    -0.117 -0.707 -0.071
-#> genMarco:densf8                0.195 -0.288 -0.092    -0.096 -0.071 -0.707
-#> date28Aug1990:densf2           0.256 -0.188 -0.591    -0.565 -0.093 -0.077
-#> date28Aug1990:densf4           0.118 -0.087 -0.272    -0.058 -0.349 -0.035
-#> date28Aug1990:densf8           0.154 -0.114 -0.357    -0.076 -0.056 -0.559
-#> genMarco:date28Aug1990:densf2 -0.181  0.266  0.418     0.400  0.066  0.054
-#> genMarco:date28Aug1990:densf4 -0.083  0.122  0.192     0.041  0.247  0.025
-#> genMarco:date28Aug1990:densf8 -0.109  0.161  0.252     0.054  0.040  0.395
-#>                               gnM:28A1990 gnMr:2 gnMr:4 gnMr:8 d28A1990:2
-#> genMarco                                                                 
-#> date28Aug1990                                                            
-#> densf2                                                                   
-#> densf4                                                                   
-#> densf8                                                                   
-#> genMarco:date28Aug1990                                                   
-#> genMarco:densf2                0.213                                     
-#> genMarco:densf4                0.158       0.165                         
-#> genMarco:densf8                0.130       0.136  0.101                  
-#> date28Aug1990:densf2           0.418       0.400  0.066  0.054           
-#> date28Aug1990:densf4           0.192       0.041  0.247  0.025  0.160    
-#> date28Aug1990:densf8           0.252       0.054  0.040  0.395  0.211    
-#> genMarco:date28Aug1990:densf2 -0.591      -0.565 -0.093 -0.077 -0.707    
-#> genMarco:date28Aug1990:densf4 -0.272      -0.058 -0.349 -0.035 -0.113    
-#> genMarco:date28Aug1990:densf8 -0.357      -0.076 -0.056 -0.559 -0.149    
-#>                               d28A1990:4 d28A1990:8 gM:28A1990:2 gM:28A1990:4
-#> genMarco                                                                     
-#> date28Aug1990                                                                
-#> densf2                                                                       
-#> densf4                                                                       
-#> densf8                                                                       
-#> genMarco:date28Aug1990                                                       
-#> genMarco:densf2                                                              
-#> genMarco:densf4                                                              
-#> genMarco:densf8                                                              
-#> date28Aug1990:densf2                                                         
-#> date28Aug1990:densf4                                                         
-#> date28Aug1990:densf8           0.097                                         
-#> genMarco:date28Aug1990:densf2 -0.113     -0.149                              
-#> genMarco:date28Aug1990:densf4 -0.707     -0.068      0.160                   
-#> genMarco:date28Aug1990:densf8 -0.068     -0.707      0.211        0.097      
-#> 
-#> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -1.69200677 -0.60443077 -0.08494936  0.54315774  1.88973883 
-#> 
-#> Number of Observations: 64
-#> Number of Groups: 4 
    anova(m3)
-
    #>                numDF denDF   F-value p-value
-#> (Intercept)        1    45 108.51118  <.0001
-#> gen                1    45   9.22296  0.0040
-#> date               1    45   9.82424  0.0030
-#> densf              3    45  13.20545  <.0001
-#> gen:date           1    45   2.18519  0.1463
-#> gen:densf          3    45   0.06028  0.9804
-#> date:densf         3    45   4.71544  0.0060
-#> gen:date:densf     3    45   0.42959  0.7328
    
-# Table 3 of Piepho, using transformation
-m4 <- aov( yield^.235 ~ gen * date * densf + block, dat )
-summary(m4)
-
    #>                Df Sum Sq Mean Sq F value   Pr(>F)    
-#> gen             1 0.3677  0.3677  10.203 0.002562 ** 
-#> date            1 0.5873  0.5873  16.294 0.000208 ***
-#> densf           3 2.0902  0.6967  19.330 3.37e-08 ***
-#> block           3 0.8409  0.2803   7.776 0.000274 ***
-#> gen:date        1 0.0713  0.0713   1.979 0.166364    
-#> gen:densf       3 0.0252  0.0084   0.233 0.873171    
-#> date:densf      3 0.3822  0.1274   3.535 0.022038 *  
-#> gen:date:densf  3 0.0030  0.0010   0.028 0.993624    
-#> Residuals      45 1.6220  0.0360                     
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mcleod.barley-1.png b/docs/reference/mcleod.barley-1.png deleted file mode 100644 index 78f8b5d4..00000000 Binary files a/docs/reference/mcleod.barley-1.png and /dev/null differ diff --git a/docs/reference/mcleod.barley.html b/docs/reference/mcleod.barley.html deleted file mode 100644 index 2995c6ed..00000000 --- a/docs/reference/mcleod.barley.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - - -Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of barley in South Canterbury with yield and yield components

    - - -
    - -
    -

    Yield and yield components for barley with different seeding rates.

    -
    - - - -

    Format

    - -

    A data frame with 40 observations on the following 10 variables.

    -
    year

    year, numeric

    -
    site

    site factor

    -
    rate

    rate, numeric

    -
    plants

    plants per sq meter

    -
    tillers

    tillers per plant

    -
    heads

    heads per plant

    -
    surviving

    percent surviving tillers

    -
    grains

    grains per head

    -
    weight

    weight of 1000 grains

    -
    yield

    yield tons/hectare

    - -
    - -

    Details

    - -

    Trials were conducted at 5 sites, 3 years in South Canterbury. - (not all sites in every year). - Values are the average of 6 blocks. In 1974 there was a - severe drought. The other years had favorable growing conditions.

    -

    Source

    - -

    C. C. McLeod (1982). - Effects of rates of seeding on barley sown for grain. - New Zealand Journal of Experimental Agriculture, 10, 133-136. - https://doi.org/10.1080/03015521.1982.10427857.

    -

    References

    - -

    Maindonald (1992).

    - -

    Examples

    - 
    
-library(agridat)
-
-data(mcleod.barley)
-dat <- mcleod.barley
-
-# Table 3 of McLeod.  Across-environment means by planting rate
-d1 <- aggregate(cbind(plants, tillers, heads, surviving, grains,
-                      weight, yield) ~ rate, dat, FUN=mean)
-# Calculate income based on seed cost of $280/ton, grain $140/ton.
-d1 <- transform(d1, income=140*yield-280*rate/1000)
-signif(d1,3)
-
    #>   rate plants tillers heads surviving grains weight yield income
-#> 1   50    112    5.22  4.36      84.0   21.2   46.1  3.97    541
-#> 2   75    163    4.04  3.26      80.9   20.0   45.1  4.26    575
-#> 3  100    203    3.69  2.72      74.3   19.2   44.7  4.38    585
-#> 4  125    239    3.28  2.33      71.9   18.4   43.4  4.41    582
-#> 5  150    294    2.90  2.00      69.5   17.9   42.8  4.47    584
    ##  rate plants tillers heads surviving grains weight yield
-##    50 112.12    5.22  4.36     83.95  21.25  46.11  3.97
-##    75 162.75    4.04  3.26     80.89  19.95  45.10  4.26
-##   100 202.62    3.69  2.73     74.29  19.16  44.66  4.38
-##   125 239.00    3.28  2.33     71.86  18.45  43.45  4.41
-##   150 293.62    2.90  2.00     69.54  17.94  42.77  4.47
-
-# Even though tillers/plant, heads/plant, surviving tillers,
-# grains/head, weight/1000 grains are all decreasing as planting
-# rate increases, the total yield is still increasing.
-# But, income peaks around seed rate of 100.
-
-libs(lattice)
-xyplot(yield +income +surviving +grains +weight +plants +tillers +heads ~ rate,
-       data=d1, outer=TRUE, type=c('p','l'),
-       scales=list(y=list(relation="free")),
-       xlab="Nitrogen rate", ylab="Trait value",
-       main="mcleod.barley - nitrogen response curves"  )
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mead.cauliflower-1.png b/docs/reference/mead.cauliflower-1.png deleted file mode 100644 index 92700732..00000000 Binary files a/docs/reference/mead.cauliflower-1.png and /dev/null differ diff --git a/docs/reference/mead.cauliflower.html b/docs/reference/mead.cauliflower.html deleted file mode 100644 index d698a0da..00000000 --- a/docs/reference/mead.cauliflower.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - - -Leaves for cauliflower plants at different times — mead.cauliflower • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Leaves for cauliflower plants at different times

    - - -
    - -
    -

    Leaves for cauliflower plants at different times in two years.

    -
    - - - -

    Format

    - -

    A data frame with 14 observations on the following 4 variables.

    -
    year

    year factor

    -
    degdays

    degree days above 32F

    -
    leaves

    number of leaves

    - -
    - -

    Details

    - -

    Numbers of leaves for 10 cauliflower plants in each of two years, and - temperature degree-days above 32F, divided by 100.

    -

    The year is 1956-57 or 1957-58.

    -

    Over the data range shown, the number of leaves is increasing - linearly. Extrapolating backwards shows that a linear model is - inappropriate, and so a glm is used.

    -

    Source

    - -

    Roger Mead, Robert N Curnow, Anne M Hasted. 2002. - Statistical Methods in Agriculture and Experimental Biology, 3rd ed. - Chapman and Hall. Page 251.

    -

    References

    - -

    Mick O'Neill. Regression & Generalized Linear (Mixed) Models. - Statistical Advisory & Training Service Pty Ltd.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.cauliflower)
-dat <- mead.cauliflower
-
-dat <- transform(dat, year=factor(year))
-
-m1 <- glm(leaves ~ degdays + year, data=dat, family=poisson)
-coef(m1)
-
    #> (Intercept)     degdays    year1957 
-#>  3.49492453  0.08512651  0.21688760 
    ## (Intercept)     degdays    year1957
-##  3.49492453  0.08512651  0.21688760
-
-dat$pred <- predict(m1, type="response")
-libs(lattice)
-libs(latticeExtra)
-xyplot(leaves~degdays, data=dat, groups=year, type=c('p'),
-       auto.key=list(columns=2),
-       main="mead.cauliflower - observed (symbol) & fitted (line)",
-       xlab="degree days", ylab="Number of leaves", ) +
-  xyplot(pred~degdays, data=dat, groups=year, type=c('l'), col="black")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mead.cowpea.maize-1.png b/docs/reference/mead.cowpea.maize-1.png deleted file mode 100644 index 8a0eaceb..00000000 Binary files a/docs/reference/mead.cowpea.maize-1.png and /dev/null differ diff --git a/docs/reference/mead.cowpea.maize.html b/docs/reference/mead.cowpea.maize.html deleted file mode 100644 index d35ee67e..00000000 --- a/docs/reference/mead.cowpea.maize.html +++ /dev/null @@ -1,239 +0,0 @@ - -Intercropping experiment of maize/cowpea — mead.cowpea.maize • agridat - - -
    -
    - - - -
    -
    -
    -

    Intercropping experiment of maize/cowpea

    - - -
    - -
    -

    Intercropping experiment of maize/cowpea, multiple nitrogen treatments.

    -
    - - -
    -

    Format

    -

    A data frame with 72 observations on the following 6 variables.

    block
    -

    block, 3 levels

    - -
    nitro
    -

    nitrogen, 4 levels

    - -
    cowpea
    -

    cowpea variety, 2 levels

    - -
    maize
    -

    maize variety, 3 levels

    - -
    cyield
    -

    cowpea yield, kg/ha

    - -
    myield
    -

    maize yield, kg/ha

    - - -
    -
    -

    Details

    -

    An intercropping experiment conducted in Nigeria. The four nitrogen - treatments were 0, 40, 80, 120 kg/ha.

    -
    -
    -

    Source

    -

    Roger Mead. 1990. A Review of Methodology For The Analysis of Intercropping - Experiments. Training Working Document No. 6. CIMMYT. - https://repository.cimmyt.org/xmlui/handle/10883/868

    -
    -
    -

    References

    -

    Roger Mead, Robert N Curnow, Anne M Hasted. 2002. - Statistical Methods in Agriculture and Experimental Biology, 3rd ed. - Chapman and Hall. Page 390.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.cowpea.maize)
-dat <- mead.cowpea.maize
-
-# Cowpea and maize yields are clearly in competition
-libs("latticeExtra")
-#> 
-#> Attaching package: 'latticeExtra'
-#> The following object is masked from 'package:corrgram':
-#> 
-#>     panel.ellipse
-useOuterStrips(xyplot(myield ~ cyield|maize*cowpea, dat, group=nitro,
-                      main="mead.cowpea.maize - intercropping",
-                      xlab="cowpea yield",
-                      ylab="maize yield", auto.key=list(columns=4)))
-
-
-
-# Mead Table 2 Cowpea yield anova...strongly affected by maize variety.
-anova(aov(cyield ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-#> Analysis of Variance Table
-#> 
-#> Response: cyield
-#>                    Df Sum Sq Mean Sq F value    Pr(>F)    
-#> block               2  73014   36507  2.8019  0.071083 .  
-#> maize               2 409446  204723 15.7122 6.298e-06 ***
-#> cowpea              1   6013    6013  0.4615  0.500319    
-#> nitro               3 113064   37688  2.8925  0.045320 *  
-#> maize:cowpea        2   9910    4955  0.3803  0.685790    
-#> maize:nitro         6  67563   11261  0.8642  0.528246    
-#> cowpea:nitro        3 172403   57468  4.4106  0.008282 ** 
-#> maize:cowpea:nitro  6 135379   22563  1.7317  0.135003    
-#> Residuals          46 599359   13030                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-# Cowpea mean yields for nitro*cowpea
-aggregate(cyield ~ nitro+cowpea, dat, FUN=mean)
-#>   nitro cowpea   cyield
-#> 1    N0     C1 482.3333
-#> 2    N1     C1 459.2222
-#> 3    N2     C1 413.1111
-#> 4    N3     C1 511.4444
-#> 5    N0     C2 596.5556
-#> 6    N1     C2 496.7778
-#> 7    N2     C2 478.8889
-#> 8    N3     C2 367.0000
-# Cowpea mean yields for each maize variety
-aggregate(cyield ~ maize, dat, FUN=mean)
-#>   maize   cyield
-#> 1    M1 581.9167
-#> 2    M2 430.5000
-#> 3    M3 414.5833
-
-# Bivariate analysis
-aov.c <- anova(aov(cyield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-aov.m <- anova(aov(myield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-aov.cm <- anova(aov(cyield/1000 + myield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-biv <- cbind(aov.m[,1:2], aov.c[,2], aov.cm[,2])
-names(biv) <- c('df','maize ss','cowpea ss','ss for sum')
-biv$'sum of prod' <- (biv[,4] - biv[,2] - biv[,3] ) /2
-biv$cor <- biv[,5]/(sqrt(biv[,2] * biv[,3]))
-signif(biv,2)
-#>                    df maize ss cowpea ss ss for sum sum of prod    cor
-#> block               2    0.290    0.0730      0.250      -0.058 -0.400
-#> maize               2   18.000    0.4100     13.000      -2.600 -0.980
-#> cowpea              1    0.027    0.0060      0.058       0.013  1.000
-#> nitro               3   29.000    0.1100     25.000      -1.800 -0.980
-#> maize:cowpea        2    1.100    0.0099      0.920      -0.099 -0.950
-#> maize:nitro         6    1.300    0.0680      0.920      -0.200 -0.680
-#> cowpea:nitro        3    0.240    0.1700      0.150      -0.130 -0.640
-#> maize:cowpea:nitro  6    1.300    0.1400      1.300      -0.033 -0.079
-#> Residuals          46   16.000    0.6000     14.000      -1.400 -0.460
-##                    df maize ss cowpea ss ss for sum sum of prod    cor
-## block               2    0.290    0.0730      0.250      -0.058 -0.400
-## maize               2   18.000    0.4100     13.000      -2.600 -0.980
-## cowpea              1    0.027    0.0060      0.058       0.013  1.000
-## nitro               3   29.000    0.1100     25.000      -1.800 -0.980
-## maize:cowpea        2    1.100    0.0099      0.920      -0.099 -0.950
-## maize:nitro         6    1.300    0.0680      0.920      -0.200 -0.680
-## cowpea:nitro        3    0.240    0.1700      0.150      -0.130 -0.640
-## maize:cowpea:nitro  6    1.300    0.1400      1.300      -0.033 -0.079
-## Residuals          46   16.000    0.6000     14.000      -1.400 -0.460
-
-# }
-
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    Developed by Kevin Wright.

    -
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    Site built with pkgdown 2.0.1.

    -
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    - - - - - - - - diff --git a/docs/reference/mead.cowpeamaize-1.png b/docs/reference/mead.cowpeamaize-1.png deleted file mode 100644 index 6285378a..00000000 Binary files a/docs/reference/mead.cowpeamaize-1.png and /dev/null differ diff --git a/docs/reference/mead.cowpeamaize.html b/docs/reference/mead.cowpeamaize.html deleted file mode 100644 index 98580092..00000000 --- a/docs/reference/mead.cowpeamaize.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Intercropping experiment of maize/cowpea — mead.cowpeamaize • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Intercropping experiment of maize/cowpea

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    Intercropping experiment of maize/cowpea, multiple nitrogen treatments.

    -
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    Format

    - -

    A data frame with 72 observations on the following 6 variables.

    -
    block

    block, 3 levels

    -
    nitro

    nitrogen, 4 levels

    -
    cowpea

    cowpea variety, 2 levels

    -
    maize

    maize variety, 3 levels

    -
    cyield

    cowpea yield, kg/ha

    -
    myield

    maize yield, kg/ha

    - -
    - -

    Details

    - -

    An intercropping experiment conducted in Nigeria. The four nitrogen - treatments were 0, 40, 80, 120 kg/ha.

    -

    Source

    - -

    Roger Mead. 1990. A Review of Methodology For The Analysis of Intercropping - Experiments. Training Working Document No. 6. CIMMYT. - https://repository.cimmyt.org/xmlui/handle/10883/868

    -

    References

    - -

    Roger Mead, Robert N Curnow, Anne M Hasted. 2002. - Statistical Methods in Agriculture and Experimental Biology, 3rd ed. - Chapman and Hall. Page 390.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.cowpeamaize)
-dat <- mead.cowpeamaize
-
-# Cowpea and maize yields are clearly in competition
-libs("latticeExtra")
-useOuterStrips(xyplot(myield ~ cyield|maize*cowpea, dat, group=nitro,
-                      main="mead.cowpeamaize - intercropping",
-                      xlab="cowpea yield",
-                      ylab="maize yield", auto.key=list(columns=4)))
-
    
-
-# Mead Table 2 Cowpea yield anova...strongly affected by maize variety.
-anova(aov(cyield ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
    #> Analysis of Variance Table
-#> 
-#> Response: cyield
-#>                    Df Sum Sq Mean Sq F value    Pr(>F)    
-#> block               2  73014   36507  2.8019  0.071083 .  
-#> maize               2 409446  204723 15.7122 6.298e-06 ***
-#> cowpea              1   6013    6013  0.4615  0.500319    
-#> nitro               3 113064   37688  2.8925  0.045320 *  
-#> maize:cowpea        2   9910    4955  0.3803  0.685790    
-#> maize:nitro         6  67563   11261  0.8642  0.528246    
-#> cowpea:nitro        3 172403   57468  4.4106  0.008282 ** 
-#> maize:cowpea:nitro  6 135379   22563  1.7317  0.135003    
-#> Residuals          46 599359   13030                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    # Cowpea mean yields for nitro*cowpea
-aggregate(cyield ~ nitro+cowpea, dat, FUN=mean)
-
    #>   nitro cowpea   cyield
-#> 1    N0     C1 482.3333
-#> 2    N1     C1 459.2222
-#> 3    N2     C1 413.1111
-#> 4    N3     C1 511.4444
-#> 5    N0     C2 596.5556
-#> 6    N1     C2 496.7778
-#> 7    N2     C2 478.8889
-#> 8    N3     C2 367.0000
    # Cowpea mean yields for each maize variety
-aggregate(cyield ~ maize, dat, FUN=mean)
-
    #>   maize   cyield
-#> 1    M1 581.9167
-#> 2    M2 430.5000
-#> 3    M3 414.5833
    
-# Bivariate analysis
-aov.c <- anova(aov(cyield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-aov.m <- anova(aov(myield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-aov.cm <- anova(aov(cyield/1000 + myield/1000 ~ block + maize + cowpea + nitro +
-          maize:cowpea + maize:nitro + cowpea:nitro +
-          maize:cowpea:nitro, dat))
-
-biv <- cbind(aov.m[,1:2], aov.c[,2], aov.cm[,2])
-names(biv) <- c('df','maize ss','cowpea ss','ss for sum')
-biv$'sum of prod' <- (biv[,4] - biv[,2] - biv[,3] ) /2
-biv$cor <- biv[,5]/(sqrt(biv[,2] * biv[,3]))
-signif(biv,2)
-
    #>                    df maize ss cowpea ss ss for sum sum of prod    cor
-#> block               2    0.290    0.0730      0.250      -0.058 -0.400
-#> maize               2   18.000    0.4100     13.000      -2.600 -0.980
-#> cowpea              1    0.027    0.0060      0.058       0.013  1.000
-#> nitro               3   29.000    0.1100     25.000      -1.800 -0.980
-#> maize:cowpea        2    1.100    0.0099      0.920      -0.099 -0.950
-#> maize:nitro         6    1.300    0.0680      0.920      -0.200 -0.680
-#> cowpea:nitro        3    0.240    0.1700      0.150      -0.130 -0.640
-#> maize:cowpea:nitro  6    1.300    0.1400      1.300      -0.033 -0.079
-#> Residuals          46   16.000    0.6000     14.000      -1.400 -0.460
    ##                    df maize ss cowpea ss ss for sum sum of prod    cor
-## block               2    0.290    0.0730      0.250      -0.058 -0.400
-## maize               2   18.000    0.4100     13.000      -2.600 -0.980
-## cowpea              1    0.027    0.0060      0.058       0.013  1.000
-## nitro               3   29.000    0.1100     25.000      -1.800 -0.980
-## maize:cowpea        2    1.100    0.0099      0.920      -0.099 -0.950
-## maize:nitro         6    1.300    0.0680      0.920      -0.200 -0.680
-## cowpea:nitro        3    0.240    0.1700      0.150      -0.130 -0.640
-## maize:cowpea:nitro  6    1.300    0.1400      1.300      -0.033 -0.079
-## Residuals          46   16.000    0.6000     14.000      -1.400 -0.460
-
-# }
-
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    Developed by Kevin Wright.

    -
    - -
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    Site built with pkgdown 1.6.1.

    -
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    - - - - - - - - diff --git a/docs/reference/mead.germination-1.png b/docs/reference/mead.germination-1.png deleted file mode 100644 index b90806ba..00000000 Binary files a/docs/reference/mead.germination-1.png and /dev/null differ diff --git a/docs/reference/mead.germination.html b/docs/reference/mead.germination.html deleted file mode 100644 index e5aa371f..00000000 --- a/docs/reference/mead.germination.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Seed germination with different temperatures/concentrations — mead.germination • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Seed germination with different temperatures/concentrations

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    Seed germination with different temperatures/concentrations

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    Format

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    A data frame with 64 observations on the following 5 variables.

    -
    temp

    temperature regimen

    -
    rep

    replication factor (not blocking)

    -
    conc

    chemical concentration

    -
    germ

    number of seeds germinating

    -
    seeds

    number of seeds tested = 50

    -
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    Details

    - -

    The rep factor is NOT a blocking factor.

    - -

    Source

    - -

    Roger Mead, Robert N Curnow, Anne M Hasted. 2002. - Statistical Methods in Agriculture and Experimental Biology, 3rd ed. - Chapman and Hall. Page 350-351.

    -

    Used with permission of Roger Mead, Robert Curnow, and Anne Hasted.

    - -

    References

    - -

    Schabenberger, O. and Pierce, F.J., 2002. - Contemporary statistical models for the plant and soil sciences, - CRC.

    - - -

    Examples

    - 
    
-library(agridat)
-
-data(mead.germination)
-dat <- mead.germination
-dat <- transform(dat, concf=factor(conc))
-libs(lattice)
-xyplot(germ~log(conc+.01)|temp, dat, layout=c(4,1),
-       main="mead.germination", ylab="number of seeds germinating")
    
-m1 <- glm(cbind(germ, seeds-germ) ~ 1, dat, family=binomial)
-m2 <- glm(cbind(germ, seeds-germ) ~ temp, dat, family=binomial)
-m3 <- glm(cbind(germ, seeds-germ) ~ concf, dat, family=binomial)
-m4 <- glm(cbind(germ, seeds-germ) ~ temp + concf, dat, family=binomial)
-m5 <- glm(cbind(germ, seeds-germ) ~ temp * concf, dat, family=binomial)
-anova(m1,m2,m3,m4,m5)
    #> Analysis of Deviance Table
-#> 
-#> Model 1: cbind(germ, seeds - germ) ~ 1
-#> Model 2: cbind(germ, seeds - germ) ~ temp
-#> Model 3: cbind(germ, seeds - germ) ~ concf
-#> Model 4: cbind(germ, seeds - germ) ~ temp + concf
-#> Model 5: cbind(germ, seeds - germ) ~ temp * concf
-#>   Resid. Df Resid. Dev Df Deviance
-#> 1        63    1193.80            
-#> 2        60     430.11  3   763.69
-#> 3        60     980.10  0  -549.98
-#> 4        57     148.11  3   831.99
-#> 5        48      55.64  9    92.46
    ##   Resid. Df Resid. Dev Df Deviance
-## 1        63    1193.80
-## 2        60     430.11  3   763.69
-## 3        60     980.10  0  -549.98
-## 4        57     148.11  3   831.99
-## 5        48      55.64  9    92.46
-
-# Show logit and fitted values.  T2 has highest germination
-subset(cbind(dat, predict(m5), fitted(m5)), rep=="R1")
    #>    temp rep conc germ seeds concf predict(m5) fitted(m5)
-#> 1    T1  R1  0.0    9    50     0 -1.81528997      0.140
-#> 2    T1  R1  0.1   13    50   0.1 -0.99462258      0.270
-#> 3    T1  R1  1.0   21    50     1 -0.10008346      0.475
-#> 4    T1  R1 10.0   40    50    10  1.07212067      0.745
-#> 5    T2  R1  0.0   19    50     0 -0.02000067      0.495
-#> 6    T2  R1  0.1   33    50   0.1  0.42634269      0.605
-#> 7    T2  R1  1.0   43    50     1  1.41784272      0.805
-#> 8    T2  R1 10.0   48    50    10  3.31678004      0.965
-#> 9    T3  R1  0.0    7    50     0 -2.14286337      0.105
-#> 10   T3  R1  0.1    1    50   0.1 -2.84385174      0.055
-#> 11   T3  R1  1.0    8    50     1 -1.69591151      0.155
-#> 12   T3  R1 10.0    3    50    10 -2.19722458      0.100
-#> 13   T4  R1  0.0    4    50     0 -2.04065552      0.115
-#> 14   T4  R1  0.1   13    50   0.1 -1.35533214      0.205
-#> 15   T4  R1  1.0   16    50     1 -0.70818506      0.330
-#> 16   T4  R1 10.0   13    50    10 -0.89538405      0.290
    
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    Developed by Kevin Wright.

    -
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    Site built with pkgdown 1.3.0.9100.

    -
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    - - - - - - - - diff --git a/docs/reference/mead.lamb-1.png b/docs/reference/mead.lamb-1.png deleted file mode 100644 index b4830754..00000000 Binary files a/docs/reference/mead.lamb-1.png and /dev/null differ diff --git a/docs/reference/mead.lamb.html b/docs/reference/mead.lamb.html deleted file mode 100644 index 64feb6be..00000000 --- a/docs/reference/mead.lamb.html +++ /dev/null @@ -1,348 +0,0 @@ - - - - - - - - -Number of lambs born to 3 breeds on 3 farms — mead.lamb • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Number of lambs born to 3 breeds on 3 farms

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    Number of lambs born to 3 breeds on 3 farms

    -
    - - 
    data("mead.lamb")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 4 variables.

    -
    farm

    farm: F1, F2, F3

    -
    breed

    breed: B1, B2, B3

    -
    lambclass

    lambing class: L0, L1, L2, L3

    -
    y

    count of ewes in class

    - -
    - -

    Details

    - -

    The data 'y' are counts of ewes in different lambing classes. - The classes are number of live lambs per birth for 0, 1, 2, 3+ lambs.

    -

    Source

    - -

    Roger Mead, Robert N Curnow, Anne M Hasted. 2002. - Statistical Methods in Agriculture and Experimental Biology, 3rd ed. - Chapman and Hall. Page 359.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.lamb)
-dat <- mead.lamb
-
-# farm 1 has more ewes in lambclass 3
-d2 <- xtabs(y ~ farm+breed+lambclass, data=dat)
-mosaicplot(d2, color=c("lemonchiffon1","moccasin","lightsalmon1","indianred"),
-           xlab="farm/lambclass", ylab="breed", main="mead.lamb")
-
    
-names(dat) <- c('F','B','L','y') # for compactness
-# Match totals in Mead example 14.6
-libs(dplyr)
-dat <- group_by(dat, F,B)
-summarize(dat, y=sum(y))
-
    #> `summarise()` regrouping output by 'F' (override with `.groups` argument)
    #> # A tibble: 9 x 3
-#> # Groups:   F [3]
-#>   F     B         y
-#>   <fct> <fct> <int>
-#> 1 F1    A       150
-#> 2 F1    B        46
-#> 3 F1    C        78
-#> 4 F2    A        72
-#> 5 F2    B        79
-#> 6 F2    C        28
-#> 7 F3    A       224
-#> 8 F3    B       129
-#> 9 F3    C        34
    ##          F      B     y
-##   <fctr> <fctr> <int>
-## 1     F1      A   150
-## 2     F1      B    46
-## 3     F1      C    78
-## 4     F2      A    72
-## 5     F2      B    79
-## 6     F2      C    28
-## 7     F3      A   224
-## 8     F3      B   129
-## 9     F3      C    34
-
-
-# Models
-m1 <- glm(y ~ F + B + F:B, data=dat,
-          family=poisson(link=log))
-m2 <- update(m1, y ~ F + B + F:B + L)
-m3 <- update(m1, y ~ F + B + F:B + L + B:L)
-m4 <- update(m1, y ~ F + B + F:B + L + F:L)
-m5 <- update(m1, y ~ F + B + F:B + L + B:L + F:L)
-
-AIC(m1, m2, m3, m4, m5) # Model 4 has best AIC
-
    #>    df      AIC
-#> m1  9 852.9800
-#> m2 12 306.5457
-#> m3 18 303.5781
-#> m4 18 206.1520
-#> m5 24 213.8873
    ##    df      AIC
-## m1  9 852.9800
-## m2 12 306.5457
-## m3 18 303.5781
-## m4 18 206.1520
-## m5 24 213.8873
-
-# Change contrasts for Miroslav
-m4 <- update(m4,
-             contrasts=list(F=contr.sum,B=contr.sum,L=contr.sum))
-summary(m4)
-
    #> 
-#> Call:
-#> glm(formula = y ~ F + B + L + F:B + F:L, family = poisson(link = log), 
-#>     data = dat, contrasts = list(F = contr.sum, B = contr.sum, 
-#>         L = contr.sum))
-#> 
-#> Deviance Residuals: 
-#>      Min        1Q    Median        3Q       Max  
-#> -2.00000  -0.49709   0.04627   0.47793   1.54534  
-#> 
-#> Coefficients:
-#>             Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)  2.38679    0.07147  33.395  < 2e-16 ***
-#> F1           0.23967    0.08795   2.725 0.006431 ** 
-#> F2          -0.44980    0.11371  -3.956 7.63e-05 ***
-#> B1           0.56941    0.05214  10.922  < 2e-16 ***
-#> B2           0.02240    0.05790   0.387 0.698885    
-#> L1          -0.50321    0.11242  -4.476 7.60e-06 ***
-#> L2           0.45373    0.08930   5.081 3.76e-07 ***
-#> L3           1.42046    0.07553  18.806  < 2e-16 ***
-#> F1:B1        0.04256    0.07061   0.603 0.546678    
-#> F2:B1       -0.28552    0.08104  -3.523 0.000426 ***
-#> F1:B2       -0.59242    0.08541  -6.936 4.03e-12 ***
-#> F2:B2        0.35428    0.08405   4.215 2.50e-05 ***
-#> F1:L1       -0.34198    0.15261  -2.241 0.025031 *  
-#> F2:L1        0.01066    0.17867   0.060 0.952420    
-#> F1:L2       -0.76829    0.12326  -6.233 4.58e-10 ***
-#> F2:L2        0.12824    0.13935   0.920 0.357432    
-#> F1:L3       -0.05738    0.09454  -0.607 0.543896    
-#> F2:L3        0.23542    0.12030   1.957 0.050351 .  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for poisson family taken to be 1)
-#> 
-#>     Null deviance: 1008.825  on 35  degrees of freedom
-#> Residual deviance:   18.845  on 18  degrees of freedom
-#> AIC: 206.15
-#> 
-#> Number of Fisher Scoring iterations: 5
-#> 
    
-# Match deviance table from Mead
-libs(broom)
-all <- do.call(rbind, lapply(list(m1, m2, m3, m4, m5), broom::glance))
-all$model <- unlist(lapply(list(m1, m2, m3, m4, m5),
-                               function(x) as.character(formula(x)[3])))
-all[,c('model','deviance','df.residual')]
-
    #> # A tibble: 5 x 3
-#>   model                       deviance df.residual
-#>   <chr>                          <dbl>       <int>
-#> 1 F + B + F:B                    684.           27
-#> 2 F + B + L + F:B                131.           24
-#> 3 F + B + L + F:B + B:L          116.           18
-#> 4 F + B + L + F:B + F:L           18.8          18
-#> 5 F + B + L + F:B + B:L + F:L     14.6          12
    ##                         model  deviance df.residual
-## 1                 F + B + F:B 683.67257          27
-## 2             F + B + L + F:B 131.23828          24
-## 3       F + B + L + F:B + B:L 116.27069          18
-## 4       F + B + L + F:B + F:L  18.84460          18
-## 5 F + B + L + F:B + B:L + F:L  14.57987          12
-
-
-if(0){
-  # Using MASS::loglm
-  libs(MASS)
-  # Note: without 'fitted=TRUE', devtools::run_examples has an error
-  m4b <- MASS::loglm(y ~ F + B + F:B + L + F:L, data = dat, fitted=TRUE)
-  # Table of farm * class interactions. Match Mead p. 360
-  round(coef(m4b)$F.L,2)
-  fitted(m4b)
-  resid(m4b)
-  # libs(vcd)
-  # mosaic(m4b, shade=TRUE,
-  # formula = ~ F + B + F:B + L + F:L,
-  # residual_type="rstandard", keep_aspect=FALSE)
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mead.strawberry-1.png b/docs/reference/mead.strawberry-1.png deleted file mode 100644 index 6957bac3..00000000 Binary files a/docs/reference/mead.strawberry-1.png and /dev/null differ diff --git a/docs/reference/mead.strawberry.html b/docs/reference/mead.strawberry.html deleted file mode 100644 index d096c576..00000000 --- a/docs/reference/mead.strawberry.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - - - -RCB experiment of strawberry — mead.strawberry • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of strawberry

    - - -
    - -
    -

    RCB experiment of strawberry

    -
    - - - -

    Format

    - -

    A data frame with 32 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    block

    block, 4 levels

    -
    gen

    genotype, 8 levels

    -
    yield

    yield, pounds

    - -
    - -

    Details

    - -

    A hedge along the right side (column 8) caused shading and lower - yields.

    -

    R. Mead said (in a discussion of the Besag & Higdon paper), - "the blocks defined (as given to me by the experimenter) - are the entire horizontal rows...the design of the trial is actually - (and unrecognized by me also) a checker-board of eight half-blocks - with two groups of split-plot varieties".

    -

    The two sub-groups of genotypes are G, V, R1, F and Re, M, E, P.

    -

    Source

    - -

    Unknown, but prior to 1968 according to Besag. Probably via R. Mead.

    -

    References

    - -

    R. Mead, 1990, The Design of Experiments.

    -

    Julian Besag and D Higdon, 1999. - Bayesian Analysis of Agricultural Field Experiments, - Journal of the Royal Statistical Society: Series B (Statistical - Methodology),61, 691--746. Table 4.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.strawberry)
-dat <- mead.strawberry
-dat$sub <- ifelse(is.element(dat$gen, c('G', 'V', 'R1', 'F')),
-              "S1","S2")
-
-libs(desplot)
-desplot(dat, yield~col*row,
-        text=gen, cex=1, out1=block, out2=sub, # unknown aspect
-        main="mead.strawberry")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mead.turnip-1.png b/docs/reference/mead.turnip-1.png deleted file mode 100644 index c8986b4c..00000000 Binary files a/docs/reference/mead.turnip-1.png and /dev/null differ diff --git a/docs/reference/mead.turnip.html b/docs/reference/mead.turnip.html deleted file mode 100644 index 50a7f0d6..00000000 --- a/docs/reference/mead.turnip.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - -Density/spacing experiment for turnips in 3 blocks. — mead.turnip • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    -
    -

    Density/spacing experiment for turnips in 3 blocks.

    - - -
    - -
    -

    Density/spacing experiment for turnips in 3 blocks.

    -
    - - 
    data("mead.turnip")
    - - -

    Format

    - -

    A data frame with 60 observations on the following 4 variables.

    -
    yield

    log yield (pounds/plot)

    -
    block

    block

    -
    spacing

    row spacing, inches

    -
    density

    density of seeds, pounds/acre

    - -
    - -

    Details

    - -

    An experiment with turnips, 3 blocks, 20 treatments in a factorial - arrangement of 5 seeding rates (density) and 4 widths (spacing).

    -

    Source

    - -

    Roger Mead. (1988). - The Design of Experiments: Statistical Principles for Practical Applications. - Example 12.3. Page 323.

    -

    References

    - -

    H. P. Piepho, R. N. Edmondson. (2018). - A tutorial on the statistical analysis of factorial experiments - with qualitative and quantitative treatment factor levels. - Jour Agronomy and Crop Science, 8, 1-27. - https://doi.org/10.1111/jac.12267

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(mead.turnip)
-dat <- mead.turnip
-
-dat$ratef <- factor(dat$density)
-dat$widthf <- factor(dat$spacing)
-
-m1 <- aov(yield ~ block + ratef + widthf + ratef:widthf, data=dat)
-anova(m1) # table 12.10 in Mead
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>              Df  Sum Sq Mean Sq  F value    Pr(>F)    
-#> block         2  3.4168  1.7084  43.6173 1.443e-10 ***
-#> ratef         4 25.6476  6.4119 163.7023 < 2.2e-16 ***
-#> widthf        3  0.9167  0.3056   7.8011  0.000353 ***
-#> ratef:widthf 12  0.9976  0.0831   2.1224  0.038910 *  
-#> Residuals    38  1.4884  0.0392                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# Similar to Piepho fig 10
-libs(lattice)
-xyplot(yield ~ log(spacing)|ratef, data=dat,
-       auto.key=list(columns=5),
-       main="mead.turnip - log(yield) for each density",
-       group=ratef)
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mercer.mangold.uniformity-1.png b/docs/reference/mercer.mangold.uniformity-1.png deleted file mode 100644 index b1fe9eef..00000000 Binary files a/docs/reference/mercer.mangold.uniformity-1.png and /dev/null differ diff --git a/docs/reference/mercer.mangold.uniformity-2.png b/docs/reference/mercer.mangold.uniformity-2.png deleted file mode 100644 index 45bb6442..00000000 Binary files a/docs/reference/mercer.mangold.uniformity-2.png and /dev/null differ diff --git a/docs/reference/mercer.mangold.uniformity-3.png b/docs/reference/mercer.mangold.uniformity-3.png deleted file mode 100644 index 67f8f728..00000000 Binary files a/docs/reference/mercer.mangold.uniformity-3.png and /dev/null differ diff --git a/docs/reference/mercer.mangold.uniformity.html b/docs/reference/mercer.mangold.uniformity.html deleted file mode 100644 index 716823d7..00000000 --- a/docs/reference/mercer.mangold.uniformity.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Uniformity trial of mangolds — mercer.mangold.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of mangolds

    - - -
    - -
    -

    Uniformity trial of mangolds at Rothamsted Experiment Station, - England, 1910.

    -
    - - 
    data("mercer.mangold.uniformity")
    - - -

    Format

    - -

    A data frame with 200 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    roots

    root yields, pounds

    -
    leaves

    leaf yields, pounds

    - -
    - -

    Details

    - -

    Grown in 1910.

    -

    Each plot was 3 drills, each drill being 2.4 feet wide. - Plots were 1/200 acres, 7.2 feet by 30.25 feet long - The "length of the plots runs with the horizontal lines of figures [in - Table I], this being also the direction of the drills across the field."

    -

    Field width: 10 plots * 30.25ft = 302.5 feet

    -

    Field length: 20 plots * 7.25 ft = 145 feet

    -

    Source

    - -

    Mercer, WB and Hall, AD, 1911. The experimental error of field trials - The Journal of Agricultural Science, 4, 107-132. Table 1. - https://doi.org/10.1017/S002185960000160X

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(mercer.mangold.uniformity)
-  dat <- mercer.mangold.uniformity
-  
-  libs(desplot)
-  desplot(dat, leaves~col*row,
-          aspect=145/302, # true aspect
-          main="mercer.mangold.uniformity - leaves")
-
    
-  libs(desplot)
-  desplot(dat, roots~col*row,
-          aspect=145/302, # true aspect
-          main="mercer.mangold.uniformity - roots")
-
    
-  libs(lattice)
-  xyplot(roots~leaves, data=dat)
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/mercer.wheat.uniformity-1.png b/docs/reference/mercer.wheat.uniformity-1.png deleted file mode 100644 index 3e4e539d..00000000 Binary files a/docs/reference/mercer.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/mercer.wheat.uniformity-2.png b/docs/reference/mercer.wheat.uniformity-2.png deleted file mode 100644 index dca3bda4..00000000 Binary files a/docs/reference/mercer.wheat.uniformity-2.png and /dev/null differ diff --git a/docs/reference/mercer.wheat.uniformity-3.png b/docs/reference/mercer.wheat.uniformity-3.png deleted file mode 100644 index 7b30c02f..00000000 Binary files a/docs/reference/mercer.wheat.uniformity-3.png and /dev/null differ diff --git a/docs/reference/mercer.wheat.uniformity-4.png b/docs/reference/mercer.wheat.uniformity-4.png deleted file mode 100644 index c4a2f9cd..00000000 Binary files a/docs/reference/mercer.wheat.uniformity-4.png and /dev/null differ diff --git a/docs/reference/mercer.wheat.uniformity-5.png b/docs/reference/mercer.wheat.uniformity-5.png deleted file mode 100644 index df15f67c..00000000 Binary files a/docs/reference/mercer.wheat.uniformity-5.png and /dev/null differ diff --git a/docs/reference/mercer.wheat.uniformity.html b/docs/reference/mercer.wheat.uniformity.html deleted file mode 100644 index 780d59ca..00000000 --- a/docs/reference/mercer.wheat.uniformity.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — mercer.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat at Rothamsted Experiment Station, England, 1910.

    -
    - - - -

    Format

    - -

    A data frame with 500 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    grain

    grain yield, pounds

    -
    straw

    straw yield, pounds

    - -
    - -

    Details

    - -

    The wheat crop was grown in the summer of 1910 at Rothamsted - Experiment Station (Harpenden, Hertfordshire, England). In the Great Knott, a - seemingly uniform area of 1 acre was harvested in separate plots, each - 1/500th acre in size. The grain and straw from each plot was weighed - separately.

    -

    McCullagh gives more information about the plot size.

    -

    Field width: 25 plots * 8 ft = 200 ft

    -

    Field length: 20 plots * 10.82 ft = 216 ft

    -

    D. G. Rossiter (2014) uses this data for an extensive data analysis - tutorial.

    -

    Source

    - -

    Mercer, WB and Hall, AD, (1911). - The experimental error of field trials - The Journal of Agricultural Science, 4, 107-132. Table 5. - https://doi.org/10.1017/S002185960000160X

    -

    References

    - - - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    -

    D. G. Rossiter (2014). Tutorial: Using the R Environment for Statistical Computing - An example with the Mercer & Hall wheat yield dataset.

    -

    G. A. Baker (1941). - Fundamental Distribution of Errors for Agricultural Field Trials. - National Mathematics Magazine, 16, 7-19. - https://doi.org/10.2307/3028105

    -

    The 'spdep' package includes the grain yields - (only) and spatial positions of plot centres in its example dataset - 'wheat'.

    -

    Note, checked that all '4.03' values in this data match the original - document.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(mercer.wheat.uniformity)
-  dat <- mercer.wheat.uniformity
-
-  
-  libs(desplot)
-  desplot(dat, grain ~ col*row,
-          aspect=216/200, # true aspect
-          main="mercer.wheat.uniformity - grain yield")
-
    
-  
-  libs(lattice)
-  xyplot(straw ~ grain, data=dat, type=c('p','r'),
-         main="mercer.wheat.uniformity - regression")
-
    
-  
-  libs(hexbin)
-  hexbinplot(straw ~ grain, data=dat)
-
    
-
-  libs(sp, gstat)
-  plot.wid <- 2.5
-  plot.len <- 3.2
-  nr <- length(unique(dat$row))
-  nc <- length(unique(dat$col))
-  
-  xy <- expand.grid(x = seq(plot.wid/2, by=plot.wid, length=nc),
-                    y = seq(plot.len/2, by=plot.len, length=nr))
-  dat.sp <- dat
-  coordinates(dat.sp) <- xy
-  
-  # heatmap
-  spplot(dat.sp, zcol = "grain", cuts=8,
-         cex = 1.6,
-         col.regions =  bpy.colors(8),
-         main = "Grain yield", key.space = "right")
-
    
-  # variogram
-  # Need gstat::variogram to get the right method
-  vg <- gstat::variogram(grain ~ 1, dat.sp, cutoff = plot.wid * 10, width = plot.wid)
-  plot(vg, plot.numbers = TRUE,
-       main="mercer.wheat.uniformity - variogram")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/miguez.biomass-1.png b/docs/reference/miguez.biomass-1.png deleted file mode 100644 index 8ad6ede9..00000000 Binary files a/docs/reference/miguez.biomass-1.png and /dev/null differ diff --git a/docs/reference/miguez.biomass-2.png b/docs/reference/miguez.biomass-2.png deleted file mode 100644 index 25d96c36..00000000 Binary files a/docs/reference/miguez.biomass-2.png and /dev/null differ diff --git a/docs/reference/miguez.biomass-3.png b/docs/reference/miguez.biomass-3.png deleted file mode 100644 index 04e3078b..00000000 Binary files a/docs/reference/miguez.biomass-3.png and /dev/null differ diff --git a/docs/reference/miguez.biomass-4.png b/docs/reference/miguez.biomass-4.png deleted file mode 100644 index 95bd57a6..00000000 Binary files a/docs/reference/miguez.biomass-4.png and /dev/null differ diff --git a/docs/reference/miguez.biomass.html b/docs/reference/miguez.biomass.html deleted file mode 100644 index 9b53cd7b..00000000 --- a/docs/reference/miguez.biomass.html +++ /dev/null @@ -1,269 +0,0 @@ - - - - - - - - -Biomass of 3 crops in Greece — miguez.biomass • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - -
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    -

    Biomass of 3 crops in Greece

    - - -
    - -
    -

    Biomass of 3 crops in Greece

    -
    - - 
    data("miguez.biomass")
    - - -

    Format

    - -

    A data frame with 212 observations on the following 5 variables.

    -
    doy

    day of year

    -
    block

    block, 1-4

    -
    input

    management input, Lo/Hi

    -
    crop

    crop type

    -
    yield

    yield tons/ha

    - -
    - -

    Details

    - -

    Experiment was conducted in Greece in 2009. Yield values are - destructive Measurements of above-ground biomass for fiber sorghum, - maize, sweet sorghum.

    -

    Hi management refers to weekly irrigation and high nitrogen applications. - Lo management refers to bi-weekly irrigation and low nitrogen.

    -

    The experiment had 4 blocks.

    -

    Crops were planted on DOY 141 with 0 yield.

    -

    Source

    - -

    Fernando E. Miguez. R package nlraa. - https://github.com/femiguez/nlraa

    -

    References

    - -

    Sotirios V. Archontoulis and Fernando E. Miguez (2013). - Nonlinear Regression Models and Applications in Agricultural Research. - Agron. Journal, 105:1-13. - https://doi.org/10.2134/agronj2012.0506

    -

    Hamze Dokoohaki. - https://www.rpubs.com/Para2x/100378 - https://rstudio-pubs-static.s3.amazonaws.com/100440_26eb9108524c4cc99071b0db8e648e7d.html

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(miguez.biomass)
-  dat <- miguez.biomass
-  dat <- subset(dat, doy > 141)
-
-  libs(lattice)
-  xyplot(yield ~ doy | crop*input,  data = dat,
-         main="miguez.biomass",
-         groups = crop,
-         type=c('p','smooth'),
-         auto.key=TRUE)
-
    
-
-  # ----------
-  # Archontoulis et al fit some nonlinear models.
-  # Here is a simple example which does NOT account for crop/input
-  # Slow, so dont run
-  if(0){
-    dat2 <- transform(dat, eu = paste(block, input, crop))
-    dat2 <- groupedData(yield ~ doy | eu, data = dat2)
-    fit.lis <- nlsList(yield ~ SSfpl(doy, A, B, xmid, scal),
-                       data = dat2,
-                       control=nls.control(maxiter=100))
-    print(plot(intervals(fit.lis)))
-    
-    libs(nlme)
-    # use all data to get initial values
-    inits <- getInitial(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2)
-    inits
-    xvals <- 150:325
-    y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal))
-    plot(yield ~ doy, dat2)
-    lines(xvals,y1)
-    
-    # must have groupedData object to use augPred
-    dat2 <- groupedData(yield ~ doy|eu, data=dat2)
-    plot(dat2)
-    
-    # without 'random', all effects are included in 'random'
-    m1 <- nlme(yield ~ SSfpl(doy, A, B, xmid,scale),
-               data= dat2,
-               fixed= A + B + xmid + scale ~ 1,
-               # random = B ~ 1|eu, # to make only B random
-               random = A + B + xmid + scale ~ 1|eu,
-               start=inits)
-    fixef(m1)
-    summary(m1)
-    plot(augPred(m1, level=0:1),
-         main="miguez.biomass - observed/predicted data") # only works with groupedData object
-  }
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/minnesota.barley.weather-1.png b/docs/reference/minnesota.barley.weather-1.png deleted file mode 100644 index 047fb1a7..00000000 Binary files a/docs/reference/minnesota.barley.weather-1.png and /dev/null differ diff --git a/docs/reference/minnesota.barley.weather-2.png b/docs/reference/minnesota.barley.weather-2.png deleted file mode 100644 index 19725a9c..00000000 Binary files a/docs/reference/minnesota.barley.weather-2.png and /dev/null differ diff --git a/docs/reference/minnesota.barley.weather.html b/docs/reference/minnesota.barley.weather.html deleted file mode 100644 index 0f5607ee..00000000 --- a/docs/reference/minnesota.barley.weather.html +++ /dev/null @@ -1,277 +0,0 @@ - - - - - - - - -Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Monthly weather at 6 sites in Minnesota 1927-1936.

    - - -
    - -
    -

    This is monthly weather summaries for the 6 sites where barley yield - trials were conducted.

    -
    - - - -

    Format

    - -

    A data frame with 719 observations on the following 8 variables.

    -
    site

    site, 6 levels

    -
    year

    year, 1927-1936

    -
    mo

    month, 1-12, numeric

    -
    cdd

    monthly cooling degree days, Fahrenheit

    -
    hdd

    monthly heating degree days, Fahrenheit

    -
    precip

    monthly precipitation, inches

    -
    min

    monthly average daily minimum temp, Fahrenheit

    -
    max

    monthly average daily maximum temp, Fahrenheit

    - -
    - -

    Details

    - -

    When the weather data was extracted from the National Climate Data - Center, the following weather stations were chosen, based on - availability of weather data in the given time frame (1927-1936) - and the proximity to the town (site) for the barley data.

    -

    - - - - - - - -
    sitestation namestation
    MorrisMORRIS WC EXPERIMENTAL STATIONUSC00215638
    StPaulMINNEAPOLIS WEATHER BUREAU DOWNTOWNUSC00215433
    CrookstonCROOKSTON NW EXPERIMENTAL STATIONUSC00211891
    GrandRapidsGRAND RAPIDS FRS LABUSC00213303
    WasecaWASECA EXPERIMENTAL STATIONUSC00218692
    DuluthSUPERIORUSC00478349
    - - -

    'cdd' are cooling degree days, which is the number of degree days with - a temperature _above_ 65 Fahrenheit.

    -

    'hdd' are heating degree days, _below_ 65 Fahrenheit.

    -

    No data is available for Duluth in Dec, 1931.

    -

    Source

    - -

    National Climate Data Center, https://www.ncdc.noaa.gov/.

    -

    References

    - -

    Kevin Wright. 2013. Revisiting Immer's Barley Data. - The American Statistitician, 67, 129-133. - https://doi.org/10.1080/00031305.2013.801783

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(minnesota.barley.yield)
-dat <- minnesota.barley.yield
-data( minnesota.barley.weather)
-datw <- minnesota.barley.weather
-
-  # Weather trends over time
-  libs(latticeExtra)
-  useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
-                        main="minnesota.barley",
-                        xlab="month", ylab="Cooling degree days",
-                        subset=(mo > 3 & mo < 10),
-                        scales=list(alternating=FALSE),
-                        type='l', auto.key=list(columns=5)))
-
    
-
-# Total cooling/heating/precip in Apr-Aug for each site/yr
-ww <- subset(datw, mo>=4 & mo<=8)
-ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
-
-# Average yield per each site/env
-yy <- aggregate(yield~site+year, dat, mean)
-
-minn <- merge(ww, yy)
-
-
-# Higher yields generally associated with cooler temps, more precip
-libs(reshape2)
-me <- melt(minn, id.var=c('site','year'))
-mey <- subset(me, variable=="yield")
-mey <- mey[,c('site','year','value')]
-names(mey) <- c('site','year','y')
-mec <- subset(me, variable!="yield")
-names(mec) <- c('site','year','covar','x')
-mecy <- merge(mec, mey)
-mecy$yr <- factor(mecy$year)
-foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
-              par.settings=list(superpose.symbol=list(pch=substring(levels(mecy$yr),4))),
-              xlab="", ylab="yield", main="minnesota.barley",
-              panel=function(x,y,...) {
-                panel.lmline(x,y,..., col="gray")
-                panel.superpose(x,y,...)
-              },
-              scales=list(x=list(relation="free")))
-  libs(latticeExtra)
-  foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
-  combineLimits(foo, margin.x=2L) # Use a common x axis for all rows
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/minnesota.barley.yield-1.png b/docs/reference/minnesota.barley.yield-1.png deleted file mode 100644 index 06f65f66..00000000 Binary files a/docs/reference/minnesota.barley.yield-1.png and /dev/null differ diff --git a/docs/reference/minnesota.barley.yield.html b/docs/reference/minnesota.barley.yield.html deleted file mode 100644 index 172602cd..00000000 --- a/docs/reference/minnesota.barley.yield.html +++ /dev/null @@ -1,199 +0,0 @@ - -Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield • agridat - - -
    -
    - - - -
    -
    -
    -

    Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936.

    - - -
    - -
    -

    This is an expanded version of the barley data that is often - used to illustrate dot plots.

    -
    - - -
    -

    Format

    -

    A data frame with 647 observations on the following 4 variables.

    yield
    -

    yield in bu/ac

    - -
    gen
    -

    genotype (variety) factor

    - -
    year
    -

    year

    - -
    site
    -

    site factor, 6 levels

    - - -
    -
    -

    Details

    - - -

    The lattice package contains a smaller version of this data for - the years 1931 and 1932.

    -

    The following comments are in reference to the mentioned source - documents.

    -

    Notes about Immer (1934).

    -

    The University Farm location is at the city of Saint Paul.

    -

    This source provides the yield data for each of the three blocks at - each location in 1931 and 1932. The following registration numbers - and names are given:

    -

    C.I. numberVariety name
    Minn 184Manchuria
    Minn 445Glabron
    Minn 440Svansota
    Minn 447Velvet
    Minn 448Trebi
    Minn 457Manchuria x Smooth Awn
    Minn 462Smooth Awn x Manchuria
    Minn 452Peatland
    Minn 475Svanhals x Lion
    Minn 529Wisconsin No 38

    Notes about Harlan (1935):

    -

    The 1931 yields match the average values of Immer (1934).

    -

    The Minnesota 474 and 475 cultivars are both 'Svanhals x Lion' crosses.

    -

    No yields are reported at Crookston in 1928 because of a crop failure. (Page 20)

    -

    Also, in the report for North Dakota it says - "the zero yields at Williston, ND in 1931 were caused by drought". (Page 31)

    - -

    Notes about Wiebe (1940):

    -

    The 1932 data generally match the average values from Immer (1934) - with the following notes.

    -

    The data for Glabron at St Paul in 1932 are missing, but given as 36.8 - in Immer (1934). This value is treated as missing in this R dataset.

    -

    The data for Svansota at Morris in 1932 are missing, but given as 35.0 - in Immer (1934). This value is treated as missing in this R dataset.

    -

    The yield for 'Wisconsin 38' at St Paul in 1932 is shown as 3.80, but - 38 in Immer (1934). The latter value is used in this R dataset.

    -

    The yields for No475 in 1932 are not reported in Wiebe (1940), but are - reported in Immer (1934).

    -

    No yields are reported at Morris in 1933 and 1934, because of a crop - failure owing to drought.

    -

    Notes about Hayes (1942).

    -

    This sources gives the block-level yield data for 5 cultivars at 4 - sites in 1932 and 1935. Cultivar 'Barbless' is the same as 'Wisconsin - No38'.

    -
    -
    -

    Source

    -

    H V Harlan and P R Cowan and Lucille Reinbach. (1935). - Yields of barley varieties in the United States and Canada, 1927-1931. - United States Dept of Agriculture. - https://naldc.nal.usda.gov/download/CAT86200440/PDF

    -

    Gustav A. Wiebe, Philip Russell Cowan, Lucille Reinbach-Welch. (1940). - Yields of barley varieties in the United States and Canada, 1932-36. - United States Dept of Agriculture. - https://books.google.com/books?id=OUfxLocnpKkC&pg=PA19

    -
    -
    -

    References

    -

    Immer, R. F. and H. K. Hayes and LeRoy Powers. (1934). - Statistical Determination of Barley Varietal Adaptation. - Journal of the American Society of Agronomy, 26, 403-419. - https://doi.org/10.2134/agronj1934.00021962002600050008x

    -

    Hayes, H.K. and Immer, F.R. (1942). - Methods of plant breeding. McGraw Hill.

    -

    Kevin Wright. (2013). Revisiting Immer's Barley Data. - The American Statistitician, 67, 129-133. - https://doi.org/10.1080/00031305.2013.801783

    -
    - -
    -

    Examples

    - 
    
-library(agridat)
-
-data(minnesota.barley.yield)
-dat <- minnesota.barley.yield
-dat$yr <- factor(dat$year)
-
-# Drop Dryland, Jeans, CompCross, MechMixture because they have less than 5
-# year-loc values
-dat <- droplevels(subset(dat, !is.element(gen, c("CompCross","Dryland","Jeans","MechMixture"))))
-
-# 1934 has huge swings from one loc to the next
-libs(lattice)
-dotplot(gen~yield|site, dat, groups=yr,
-        main="minnesota.barley.yield",
-        auto.key=list(columns=5), scales=list(y=list(cex=.5)))
-
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/montgomery.wheat.uniformity-1.png b/docs/reference/montgomery.wheat.uniformity-1.png deleted file mode 100644 index ad20b079..00000000 Binary files a/docs/reference/montgomery.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/montgomery.wheat.uniformity-2.png b/docs/reference/montgomery.wheat.uniformity-2.png deleted file mode 100644 index 9f421b70..00000000 Binary files a/docs/reference/montgomery.wheat.uniformity-2.png and /dev/null differ diff --git a/docs/reference/montgomery.wheat.uniformity-3.png b/docs/reference/montgomery.wheat.uniformity-3.png deleted file mode 100644 index 297e25a5..00000000 Binary files a/docs/reference/montgomery.wheat.uniformity-3.png and /dev/null differ diff --git a/docs/reference/montgomery.wheat.uniformity-4.png b/docs/reference/montgomery.wheat.uniformity-4.png deleted file mode 100644 index 53abd84e..00000000 Binary files a/docs/reference/montgomery.wheat.uniformity-4.png and /dev/null differ diff --git a/docs/reference/montgomery.wheat.uniformity-5.png b/docs/reference/montgomery.wheat.uniformity-5.png deleted file mode 100644 index bf7eabe2..00000000 Binary files a/docs/reference/montgomery.wheat.uniformity-5.png and /dev/null differ diff --git a/docs/reference/montgomery.wheat.uniformity.html b/docs/reference/montgomery.wheat.uniformity.html deleted file mode 100644 index e3e02dab..00000000 --- a/docs/reference/montgomery.wheat.uniformity.html +++ /dev/null @@ -1,277 +0,0 @@ - - - - - - - - -Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of wheat, 2 years on the same land

    - - -
    - -
    -

    Uniformity trial of wheat at Nebraska Experiment Station, 1909 & 1911.

    -
    - - 
    data("montgomery.wheat.uniformity")
    - - -

    Format

    - -

    A data frame with 448 observations on the following 3 variables.

    -
    year

    year

    -
    col

    column

    -
    row

    row

    -
    yield

    yield, grams

    - -
    - -

    Details

    - - - -

    Experiments were conducted by the Nebraska Experiment Station.

    -

    A field was sown to Turkey winter wheat in the fall of 1908 and - harvested in 1909. The drill, 5.5 feet wide, was driven across the - first series of 14 blocks, the boundaries of the blocks being later - established. Each series was sown the same way, no space was allowed - between the blocks. Each block was 5.5 ft square.

    -

    The experiment was done 3 times with harvests in 1909, 1910, 1911. - A simple heatmap of the 3 years' yields are shown in Montgomery - (1912), figure 3, p. 178.

    -

    The 1909 data are given by Montgomery (1913), figure 10, page 37. - NOTE: North is at the right side of this diagram (as determined by - comparing yield values with the fertility map in Montgomery 1912, p. 178).

    -

    The 1910 data are not available.

    -

    The 1911 data are given by Montgomery (1912), figure 1, page - 165. NOTE: North is at the top of this diagram.

    -

    Surface & Pearl (1916) give a simple method for adjusting yield due to - fertility effects using the 1909 data.

    -

    Field width: 14 plots * 5.5 feet

    -

    Field length: 16 blocks * 5.5 feet

    -

    Source

    - -

    E. G. Montgomery (1912). - Variation in Yield and Methods of Arranging Plats To Secure - Comparative Results. - Twenty-Fifth Annual Report of the Agricultural Experiment Station of Nebraska, 164-180. - https://books.google.com/books?id=M-5BAQAAMAAJ&pg=RA4-PA164

    -

    E. G. Montgomery (1913). - Experiments in Wheat Breeding: Experimental Error In The Nursery and - Variation in Nitrogen and Yield. - U.S. Dept of Agriculture, Bureau of Plant Industry, Bulletin 269. - Figure 10, page 37. - https://doi.org/10.5962/bhl.title.43602

    -

    References

    - -

    Surface & Pearl, (1916). - A method of correcting for soil heterogeneity in variety tests. - Journal of Agricultural Research, 5, 22, 1039-1050. - Figure 2. - https://books.google.com/books?id=BVNyoZXFVSkC&pg=PA1039

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(montgomery.wheat.uniformity)
-  dat <- montgomery.wheat.uniformity
-  dat09 <- subset(dat, year==1909)
-  dat11 <- subset(dat, year==1911)
-
-  # Match the figures of Montgomery 1912 Fig 3, p. 178
-  libs(desplot)
-  desplot(dat09, yield ~ col*row,
-          aspect=1, # true aspect
-          main="montgomery.wheat.uniformity - 1909 yield")
-
      desplot(dat, yield ~ col*row, subset= year==1911,
-          aspect=1, # true aspect
-          main="montgomery.wheat.uniformity - 1911 yield")
-
    
-  # Surface & Pearl adjust 1909 yield for fertility effects.
-  # They calculate smoothed yield as (row sum)*(column sum)/(total)
-  # and subtract this from the overall mean to get 'deviation'.
-  # We can do something similar with a linear model with rows and columns
-  # as factors, then predict yield to get the smooth trend.
-  # Corrected yield = observed - deviation = observed - (smooth-mean)
-  
-  m1 <- lm(yield ~ factor(col) + factor(row), data=dat09)
-  dev1 <- predict(m1) - mean(dat09$yield)
-  # Corrected.  Similar (but not exact) to Surface, fig 2.
-  dat09$correct <- round(dat09$yield - dev1,0)
-
-  libs(desplot)
-  desplot(dat09, yield ~ col*row,
-          shorten="none", text=yield,
-          main="montgomery.wheat.uniformity 1909 observed")
-
      desplot(dat09, correct ~ col*row, text=correct,
-          cex=0.8, shorten="none",
-          main="montgomery.wheat.uniformity 1909 corrected")
-
      # Corrected yields are slightly shrunk toward overall mean
-  plot(correct~yield,dat09, xlim=c(350,1000), ylim=c(350,1000))
-
      abline(0,1)
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/moore.uniformity-1.png b/docs/reference/moore.uniformity-1.png deleted file mode 100644 index d765d7ed..00000000 Binary files a/docs/reference/moore.uniformity-1.png and /dev/null differ diff --git a/docs/reference/moore.uniformity-2.png b/docs/reference/moore.uniformity-2.png deleted file mode 100644 index d2014a98..00000000 Binary files a/docs/reference/moore.uniformity-2.png and /dev/null differ diff --git a/docs/reference/moore.uniformity-3.png b/docs/reference/moore.uniformity-3.png deleted file mode 100644 index ea71764d..00000000 Binary files a/docs/reference/moore.uniformity-3.png and /dev/null differ diff --git a/docs/reference/moore.uniformity-4.png b/docs/reference/moore.uniformity-4.png deleted file mode 100644 index a514d4e5..00000000 Binary files a/docs/reference/moore.uniformity-4.png and /dev/null differ diff --git a/docs/reference/moore.uniformity-5.png b/docs/reference/moore.uniformity-5.png deleted file mode 100644 index ab7f700a..00000000 Binary files a/docs/reference/moore.uniformity-5.png and /dev/null differ diff --git a/docs/reference/moore.uniformity-6.png b/docs/reference/moore.uniformity-6.png deleted file mode 100644 index cf0aaa14..00000000 Binary files a/docs/reference/moore.uniformity-6.png and /dev/null differ diff --git a/docs/reference/moore.uniformity.html b/docs/reference/moore.uniformity.html deleted file mode 100644 index 40619cdc..00000000 --- a/docs/reference/moore.uniformity.html +++ /dev/null @@ -1,309 +0,0 @@ - - - - - - - - -Uniformity trials of pole beans, bush beans, sweet corn, carrots, - spring and fall cauliflower — moore.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of pole beans, bush beans, sweet corn, carrots, - spring and fall cauliflower

    - - -
    - -
    -

    Uniformity trials of pole beans, bush beans, sweet corn, carrots, - spring and fall cauliflower at Washington, 1952-1955.

    -
    - - - -

    Format

    - -

    Each data frame has the following columns at a minimum. Some datasets - have an additional trait column.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - - - -

    All trials were grown on sandy loam soil in the Puyallup valley of - Washington. In most experiments a gradient in soil fertility was - evident. Moore & Darroch appear to have assigned 4 treatments to the - plots and used the residual variation to calculate a CV. In the - examples below a 'raw' CV is calculated and is always higher than the - CV given by Moore & Darroch.

    -

    Blue Lake Pole Beans.

    -

    Conducted 1952. Seven pickings were made at about 5-day intervals. - Table 26.

    -

    Field width: 12 rows x 5 feet = 60 feet.

    -

    Field length: 12 ranges x 10 feet = 120 feet.

    -

    Bush Beans.

    -

    Conducted in 1955. Two harvests. Table 27.

    -

    Field width: 24 rows x 3 feet = 72 feet.

    -

    Field length: 24 ranges x 5 feet = 120 feet.

    -

    Sweet Corn.

    -

    Conducted 1952. Table 28-29.

    -

    Field width: 24 rows x 3 feet = 72 feet.

    -

    Field length: 12 ranges x 10 feet = 120 feet.

    -

    Carrot.

    -

    Conducted 1952. Table 30.

    -

    Field width: 24 rows * 1.5 feet = 36 feet.

    -

    Field length: 12 ranges * 5 feet = 60 feet.

    -

    Spring Cauliflower.

    -

    Conducted spring 1951. Five harvests. Table 31-32.

    -

    Field width: 12 rows x 3 feet = 36 feet.

    -

    Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet.

    -

    Fall Cauliflower.

    -

    Conducted fall 1951. Five harvests. Table 33-34.

    -

    Field width: 12 rows x 3 feet = 36 feet.

    -

    Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet.

    -

    Source

    - -

    Moore, John F and Darroch, JG. (1956). - Field plot technique with Blue Lake pole beans, bush beans, carrots, - sweet corn, spring and fall cauliflower. - Washington Agricultural Experiment Stations, Institute of Agricultural Sciences, State College of Washington.

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  cv <- function(x) sd(x)/mean(x)
-
-  libs(desplot)
-  
-  # Pole Bean
-  data(moore.polebean.uniformity)
-  cv(moore.polebean.uniformity$yield) # 8.00. Moore says 6.73.
-
    #> [1] 0.07998926
      desplot(moore.polebean.uniformity, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=120/60, # true aspect
-          main="moore.polebean.uniformity - yield")
-
    
-  # Bush bean
-  data(moore.bushbean.uniformity)
-  cv(moore.bushbean.uniformity$yield) # 12.1. Moore says 10.8
-
    #> [1] 0.1209518
      desplot(moore.bushbean.uniformity, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=120/72, # true aspect
-          main="moore.bushbean.uniformity - yield")
-
    
-  # Sweet corn
-  data(moore.sweetcorn.uniformity)
-  cv(moore.sweetcorn.uniformity$yield) # 17.5. Moore says 13.6
-
    #> [1] 0.1746622
      desplot(moore.sweetcorn.uniformity, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=120/72, # true aspect
-          main="moore.sweetcorn.uniformity - yield")
-
      ## desplot(moore.sweetcorn.uniformity, ears~col*row,
-  ##         flip=TRUE, tick=TRUE, aspect=120/72, # true aspect
-  ##         main="moore.sweetcorn.uniformity - ears")
-  ## libs(lattice)
-  ## xyplot(yield ~ ears, moore.sweetcorn.uniformity)
-  
-  libs(desplot)
-  # Carrot
-  data(moore.carrot.uniformity)
-  cv(moore.carrot.uniformity$yield) # 33.4. Moore says 27.6
-
    #> [1] 0.3345489
      desplot(moore.carrot.uniformity, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=60/36, # true aspect
-          main="moore.carrot.uniformity - yield")
-
    
-  libs(desplot)
-  # Spring cauliflower
-  data(moore.springcauliflower.uniformity)
-  cv(moore.springcauliflower.uniformity$yield) # 21. Moore says 19.5
-
    #> [1] 0.2100339
      desplot(moore.springcauliflower.uniformity, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect=300/36, # true aspect
-          main="moore.springcauliflower.uniformity - yield")
-
      ## desplot(moore.springcauliflower.uniformity, heads~col*row, 
-  ##         flip=TRUE, tick=TRUE, aspect=300/36, # true aspect
-  ##         main="moore.springcauliflower.uniformity - heads")
-  ## libs(lattice)
-  ## xyplot(yield ~ heads, moore.springcauliflower.uniformity)
-
-  libs(desplot)
-  # Fall cauliflower
-  data(moore.fallcauliflower.uniformity)
-  cv(moore.fallcauliflower.uniformity$yield) # 17.7. Moore says 17.0
-
    #> [1] 0.1772603
      desplot(moore.fallcauliflower.uniformity, yield~col*row, 
-          flip=TRUE, tick=TRUE, aspect=300/36, # true aspect
-          main="moore.fallcauliflower.uniformity - yield")
-
      ## desplot(moore.fallcauliflower.uniformity, heads~col*row, 
-  ##         flip=TRUE, tick=TRUE, aspect=300/36, # true aspect
-  ##         main="moore.fallcauliflower.uniformity - heads")
-  ## libs(lattice)
-  ## xyplot(yield ~ heads, moore.fallcauliflower.uniformity)
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nagai.strawberry.uniformity-1.png b/docs/reference/nagai.strawberry.uniformity-1.png deleted file mode 100644 index d1fa0024..00000000 Binary files a/docs/reference/nagai.strawberry.uniformity-1.png and /dev/null differ diff --git a/docs/reference/nagai.strawberry.uniformity.html b/docs/reference/nagai.strawberry.uniformity.html deleted file mode 100644 index c8e31e73..00000000 --- a/docs/reference/nagai.strawberry.uniformity.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - - -Uniformity trial of strawberry — nagai.strawberry.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of strawberry

    - - -
    - -
    -

    Uniformity trial of strawberry in Brazil.

    -
    - - 
    data("nagai.strawberry.uniformity")
    - - -

    Format

    - -

    A data frame with 432 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield grams/plot

    - -
    - -

    Details

    - -

    A uniformity trial of strawberry, at Jundiai, Brazil, in April 1976.

    -

    The spacing between plants and rows was 0.3 m. Test area was 233.34 m^2. -There were 18 rows of 144 plants. Each plat consisted of 6 consecutive plants. -There were 432 plats, each 0.54 m^2.

    -

    Field length: 18 rows * 0.3 m = 5.4 m.

    -

    Field width: 24 columns * 6 plants * 0.3 m = 43.2 m.

    -

    Source

    - -

    Violeta Nagai (1978). - Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size and number of repetitions in experiments with strawberry). - Bragantia, 37, 71-81. Table 2, page 75. - https://dx.doi.org/10.1590/S0006-87051978000100009

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(nagai.strawberry.uniformity)
-  dat <- nagai.strawberry.uniformity
-  
-  # CV matches Nagai
-  # with(dat, sd(yield)/mean(yield))
-  # 23.42
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=(5.4)/(43.2), # true aspect
-          main="nagai.strawberry.uniformity")
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nair.turmeric.uniformity.html b/docs/reference/nair.turmeric.uniformity.html deleted file mode 100644 index 31fded58..00000000 --- a/docs/reference/nair.turmeric.uniformity.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -Uniformity trial of turmeric. — nair.turmeric.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of turmeric.

    - - -
    - -
    -

    Uniformity trial of turmeric in India, 1984.

    -
    - - 
    data("nair.turmeric.uniformity")
    - - -

    Format

    - -

    A data frame with 864 observations on the following 3 variables.

    -
    row

    row ordinate

    -
    col

    column ordinate

    -
    yield

    yield per plot, kg

    - -
    - -

    Details

    - - - -

    An experiment conducted at the College of Horticulture, Vellanikkara, - India, in 1984. The crop was grown in raised beds.

    -

    The gross experimental area was 74.2 m long x 15.2 m wide. Small - elevated beds 0.6 m x 1.5 m were raised providing channels of 0.4 m - around each bed. One row of beds all around the experiment was - discarded to eliminate border effects. After discarding the borders, - there were 432 beds in the experiment. At the time of harvest, each - bed was divided into equal plots of size .6 m x .75 m, and the yield - from each plot was recorded.

    - -

    Field map on page 64 of Nair.

    -

    Field length: 14 plots * .6 m + 13 alleys * .4 m = 13.6 m

    -

    Field width: 72 plots * .75 m + 35 alleys * .4 m = 68 m

    -

    Data found in the appendix.

    -

    Source

    - -

    Nair, B. Gopakumaran (1984). - Optimum plot size for field experiments on turmeric. - Thesis, Kerala Agriculture University. - https://krishikosh.egranth.ac.in/handle/1/5810147397

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(nair.turmeric.uniformity)
-  dat <- gopakumaran.turmeric.uniformity
-
    #> Error in eval(expr, envir, enclos): object 'gopakumaran.turmeric.uniformity' not found
      
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=13.6/68,
-          main="nair.turmeric.uniformity")
-
    #> Error in desplot(dat, yield ~ col * row, flip = TRUE, aspect = 13.6/68,     main = "nair.turmeric.uniformity"): object 'dat' not found
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/narain.sorghum.uniformity-1.png b/docs/reference/narain.sorghum.uniformity-1.png deleted file mode 100644 index 5cdc1507..00000000 Binary files a/docs/reference/narain.sorghum.uniformity-1.png and /dev/null differ diff --git a/docs/reference/narain.sorghum.uniformity.html b/docs/reference/narain.sorghum.uniformity.html deleted file mode 100644 index d931645b..00000000 --- a/docs/reference/narain.sorghum.uniformity.html +++ /dev/null @@ -1,225 +0,0 @@ - - - - - - - - -Uniformity trial of sorghum — narain.sorghum.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sorghum

    - - -
    - -
    -

    Uniformity trial of sorghum in Pakistan, 1936.

    -
    - - 
    data("narain.sorghum.uniformity")
    - - -

    Format

    - -

    A data frame with 160 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, maunds per 1/40 acre

    - -
    - -

    Details

    - - - -

    A uniformity trial with chari (sorghum) at Rawalpindi Agricultural - Station (Pakistan) in kharif (monsoon season) in 1936. Each plot was - 36 feet by 30.25 feet. The source document does not describe the - orientation of the plots, but the fertility map shown in Narain figure - 1 shows the plots are taller than wide.

    -

    Field width: 10 plots * 30.25 feet

    -

    Field length: 16 plots * 36 feet

    -

    Source

    - -

    R. Narain and A. Singh, (1940). - A Note on the Shape of Blocks in Field Experiments. - Ind. J. Agr. Sci., 10, 844-853. Page 845. - https://archive.org/stream/in.ernet.dli.2015.271745

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(narain.sorghum.uniformity)
-  dat <- narain.sorghum.uniformity
-  
-  # Narain figure 1
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=(16*36)/(10*30.25),
-          main="narain.sorghum.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nass.corn-1.png b/docs/reference/nass.corn-1.png deleted file mode 100644 index 87cecd0f..00000000 Binary files a/docs/reference/nass.corn-1.png and /dev/null differ diff --git a/docs/reference/nass.corn-2.png b/docs/reference/nass.corn-2.png deleted file mode 100644 index 812344bc..00000000 Binary files a/docs/reference/nass.corn-2.png and /dev/null differ diff --git a/docs/reference/nass.corn.html b/docs/reference/nass.corn.html deleted file mode 100644 index 49ce6338..00000000 --- a/docs/reference/nass.corn.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - - - -U.S. historical crop yields by state — nass.corn • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    U.S. historical crop yields by state

    - - -
    - -
    -

    Yields and acres harvested in each state for the major agricultural - crops in the United States, from approximately 1900 to 2011. - Crops include: barley, corn, cotton, hay, rice, sorghum, soybeans, wheat.

    -
    - - 
    nass.barley
-  nass.corn
-  nass.cotton
-  nass.hay
-  nass.sorghum
-  nass.wheat
-  nass.rice
-  nass.soybean
    - - -

    Format

    - -

    -
    year

    year

    -
    state

    state factor

    -
    acres

    acres harvested

    -
    yield

    average yield

    - -
    - -

    Details

    - -

    Be cautious with yield values for states with small acres harvested.

    -

    Yields are in bushels/acre, except: cotton pounds/acre, hay tons/acre, - rice pounds/acre.

    -

    Each crop is in a separate dataset: nass.barley, nass.corn, nass.cotton, - nass.hay, nass.sorghum, nass.wheat, nass.rice, nass.soybean.

    -

    Source

    - -

    United States Department of Agriculture, - National Agricultural Statistics Service. - https://quickstats.nass.usda.gov/

    - -

    Examples

    - 
    
-library(agridat)
-
-data(nass.corn)
-dat <- nass.corn
-
-# Use only states that grew at least 100K acres of corn in 2011
-keep <- droplevels(subset(dat, year == 2011 & acres > 100000))$state
-dat <- droplevels(subset(dat, is.element(state, keep)))
-
-# Acres of corn grown each year
-libs(lattice)
-xyplot(acres ~ year|state, dat, type='l', as.table=TRUE,
-       main="nass.corn: state trends in corn acreage")
-
    
-## Plain levelplot, using only states
-## libs(reshape2)
-## datm <- acast(dat, year~state, value.var='yield')
-## redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-## levelplot(datm, aspect=.7, col.regions=redblue,
-##           main="nass.corn",
-##           scales=list(x=list(rot=90, cex=.7)))
-
-# Model the rate of genetic gain in Illinois as a piecewise regression
-# Breakpoints define periods of open-pollinated varieties, double-cross,
-# single-cross, and transgenic hybrids.
-
-dil <- subset(nass.corn, state=="Illinois" & year >= 1900)
-m1 <- lm(yield ~ pmin(year,1932) + pmax(1932, pmin(year, 1959)) +
-         pmax(1959, pmin(year, 1995)) + pmax(1995, year), dil)
-signif(coef(m1)[-1],3) # Rate of gain for each segment
-
    #>             pmin(year, 1932) pmax(1932, pmin(year, 1959)) 
-#>                        -0.19                         1.48 
-#> pmax(1959, pmin(year, 1995))             pmax(1995, year) 
-#>                         1.75                         2.30 
    plot(yield ~ year, dil, main="nass.corn: piecewise linear model of Illinois corn yields")
-
    lines(dil$year, fitted(m1))
-
    abline(v=c(1932,1959,1995), col="wheat")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nebraska.farmincome-1.png b/docs/reference/nebraska.farmincome-1.png deleted file mode 100644 index 519ceffd..00000000 Binary files a/docs/reference/nebraska.farmincome-1.png and /dev/null differ diff --git a/docs/reference/nebraska.farmincome-2.png b/docs/reference/nebraska.farmincome-2.png deleted file mode 100644 index 3f02e392..00000000 Binary files a/docs/reference/nebraska.farmincome-2.png and /dev/null differ diff --git a/docs/reference/nebraska.farmincome.html b/docs/reference/nebraska.farmincome.html deleted file mode 100644 index a2bec9ff..00000000 --- a/docs/reference/nebraska.farmincome.html +++ /dev/null @@ -1,250 +0,0 @@ - - - - - - - - -Nebraska farm income in 2007 by county — nebraska.farmincome • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Nebraska farm income in 2007 by county

    - - -
    - -
    -

    Nebraska farm income in 2007 by county

    -
    - - - -

    Format

    - -

    A data frame with 93 observations on the following 4 variables.

    -
    county

    county

    -
    crop

    crop income, thousand dollars

    -
    animal

    livestock and poultry income, thousand dollars

    -
    area

    area of each county, square miles

    - -
    - -

    Details

    - -

    The variables for each county are:

    -

    Value of farm products sold - crops (NAICS) 2007 (adjusted)

    -

    Value of farm products sold - livestock, 2007 (adjusted).

    -

    Area in square miles.

    -

    Note: Cuming county is a very important beef-producing county. - Some counties are not reported to protect privacy. - Western Nebraska is dryer and has lower income. - South-central Nebraska is irrigated and has higher crop income per - square mile.

    -

    Source

    - -

    U.S. Department of Agriculture-National Agriculture Statistics - Service. https://censtats.census.gov/usa/usa.shtml

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(nebraska.farmincome)
-dat <- nebraska.farmincome
-
-libs(maps, mapproj, latticeExtra)
-# latticeExtra for mapplot
-
-dat$stco <- paste0('nebraska,', dat$county)
-# Scale to million dollars per county
-dat <- transform(dat, crop=crop/1000, animal=animal/1000)
-
-# Raw, county-wide incomes.  Note the outlier Cuming county
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-mapplot(stco ~ crop + animal, data = dat, colramp=redblue,
-        main="nebraska.farmincome",
-        xlab="Farm income from animals and crops (million $ per county)",
-        scales = list(draw = FALSE), 
-        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
-          projection = "mercator") )
-
    
-# Now scale to income/mile^2
-dat <- within(dat, {
-  crop.rate <- crop/area
-  animal.rate <- animal/area
-})
-# And use manual breakpoints.
-mapplot(stco ~ crop.rate + animal.rate, data = dat, colramp=redblue,
-        main="nebraska.farmincome: income per square mile (percentile breaks)",
-        xlab="Farm income (million $ / mi^2) from animals and crops",
-        scales = list(draw = FALSE), 
-        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
-          projection = "mercator"),
-        # Percentile break points
-        # breaks=quantile(c(dat$crop.rate, dat$animal.rate),
-        #                 c(0,.1,.2,.4,.6,.8,.9,1), na.rm=TRUE)
-        # Fisher-Jenks breakpoints via classInt package
-        # breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)),
-        #                       n=7, style='fisher')$brks
-        breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31))
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nonnecke.peas.uniformity-1.png b/docs/reference/nonnecke.peas.uniformity-1.png deleted file mode 100644 index 10746a1b..00000000 Binary files a/docs/reference/nonnecke.peas.uniformity-1.png and /dev/null differ diff --git a/docs/reference/nonnecke.peas.uniformity-2.png b/docs/reference/nonnecke.peas.uniformity-2.png deleted file mode 100644 index c36b8720..00000000 Binary files a/docs/reference/nonnecke.peas.uniformity-2.png and /dev/null differ diff --git a/docs/reference/nonnecke.peas.uniformity-3.png b/docs/reference/nonnecke.peas.uniformity-3.png deleted file mode 100644 index 1eeb99f7..00000000 Binary files a/docs/reference/nonnecke.peas.uniformity-3.png and /dev/null differ diff --git a/docs/reference/nonnecke.peas.uniformity.html b/docs/reference/nonnecke.peas.uniformity.html deleted file mode 100644 index 8c87557c..00000000 --- a/docs/reference/nonnecke.peas.uniformity.html +++ /dev/null @@ -1,241 +0,0 @@ - - - - - - - - -Uniformity trial of canning peas — nonnecke.peas.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of canning peas

    - - -
    - -
    -

    Uniformity trial of canning peas in southern Alberta, 1957.

    -
    - - 
    data("nonnecke.peas.uniformity")
    - - -

    Format

    - -

    A data frame with 540 observations on the following 5 variables.

    -
    block

    block factor

    -
    row

    row

    -
    col

    column

    -
    vines

    vines weight, pounds

    -
    peas

    shelled peas weight, pounds

    - -
    - -

    Details

    - - - -

    Width of basic plot was 10 feet, length was 5 feet, as limited by the - viner. At each of two blocks/locations, planting consisted of 18 rows - (only 15 rows were harvested) that were 10 feet wide and 90 feet - long. Rows were separated by 7 foot bare ground to facilitate - harvesting. Nonnecke 1960 shows a map of one block.

    -

    Plots were harvested with a five foot mower. Vines from - each plot were weighed, then shelled. The two blocks/locations were - side by side and combined by Nonnecke. The optimum plot size was found - to be 5 feet long and 10 feet wide.

    -

    Field width: 15 rows * 10 ft/row + 14 gaps * 7 ft/gap = 248 feet

    -

    Field length: 18 plots * 5 ft/plot = 90 feet

    -

    Source

    - -

    Ib Libner Nonnecke. 1958. - Yield variability of sweet corn and canning peas as affected by plot size and shape. - Thesis at Oregon State College. - https://hdl.handle.net/1957/23367

    -

    References

    - -

    I. L. Nonnecke, 1960. - The precision of field experiments with vegetable crops as influenced by plot and block size and shape: II. Canning peas. - Canadian Journal of Plant Science, 40(2): 396-404. - https://doi.org/10.4141/cjps60-053

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(nonnecke.peas.uniformity)
-  dat <- nonnecke.peas.uniformity
-  
-  libs(desplot)
-  desplot(dat, vines~col*row|block,
-          tick=TRUE, flip=TRUE, aspect=248/90, # true aspect
-          main="nonnecke.peas.uniformity - vines")
-
      desplot(dat, peas~col*row|block,
-          tick=TRUE, flip=TRUE, aspect=248/90, # true aspect
-          main="nonnecke.peas.uniformity - peas")
-
    
-  libs(lattice)
-  xyplot(peas~vines|block,dat,
-         xlab="vine weight", ylab="shelled pea weight",
-         main="nonnecke.peas.uniformity")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nonnecke.sweetcorn.uniformity-1.png b/docs/reference/nonnecke.sweetcorn.uniformity-1.png deleted file mode 100644 index 41d3c71a..00000000 Binary files a/docs/reference/nonnecke.sweetcorn.uniformity-1.png and /dev/null differ diff --git a/docs/reference/nonnecke.sweetcorn.uniformity.html b/docs/reference/nonnecke.sweetcorn.uniformity.html deleted file mode 100644 index bb2c0f21..00000000 --- a/docs/reference/nonnecke.sweetcorn.uniformity.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sweet corn

    - - -
    - -
    -

    Uniformity trials of sweet corn in Alberta, 1956.

    -
    - - 
    data("nonnecke.sweetcorn.uniformity")
    - - -

    Format

    - -

    A data frame:

    -
    loc

    location

    -
    row

    row

    -
    col

    column

    -
    yield

    yield of marketable ears, pounds

    - -
    - -

    Details

    - - - -

    Experiments were conducted at three locations in Southern Alberta at - Lethbridge, Vauxhall, and Cranford in 1956. Plot layout was 32 rows, - each 179 feet long, allowing 18 ten-foot plots per row. Rows were 3 - feet apart, thinned to one foot between plants. A double guard row - surrounded the entire plot. The same two persons were assigned to - harvest the corn from all locations. All 576 plots were harvested in - one day. Optimal plot sizes were found to be 10ft x 6ft or 20ft by - 3ft. The R data uses row/column for plot/row.

    -

    Field width: 18 plots * 10 ft = 180 feet

    -

    Field length: 32 rows * 3 ft = 96 feet

    -

    Source

    - -

    Ib Libner Nonnecke. 1958. - Yield variability of sweet corn and canning peas as affected by plot size and shape. - Thesis at Oregon State College. - https://hdl.handle.net/1957/23367

    -

    References

    - -

    I. L. Nonnecke, 1959. - The precision of field experiments with vegetable crops as influenced by plot and block size and shape: I. Sweet corn. - Canadian Journal of Plant Science, 39(4): 443-457. Tables 1-7. - https://doi.org/10.4141/cjps59-061

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  # Corn 1
-  data(nonnecke.sweetcorn.uniformity)
-  dat <- nonnecke.sweetcorn.uniformity
-
-  libs(desplot)
-  desplot(dat, yield~col*row|loc,
-          flip=TRUE, tick=TRUE, aspect=96/180, # true aspect
-          main="nonnecke.sweetcorn.uniformity")
-
        
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/obsi.potato.uniformity-1.png b/docs/reference/obsi.potato.uniformity-1.png deleted file mode 100644 index c95cf6c4..00000000 Binary files a/docs/reference/obsi.potato.uniformity-1.png and /dev/null differ diff --git a/docs/reference/obsi.potato.uniformity-2.png b/docs/reference/obsi.potato.uniformity-2.png deleted file mode 100644 index e756c22e..00000000 Binary files a/docs/reference/obsi.potato.uniformity-2.png and /dev/null differ diff --git a/docs/reference/obsi.potato.uniformity.html b/docs/reference/obsi.potato.uniformity.html deleted file mode 100644 index c56fa96d..00000000 --- a/docs/reference/obsi.potato.uniformity.html +++ /dev/null @@ -1,232 +0,0 @@ - - - - - - - - -Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of potato in Africa 2001

    - - -
    - -
    -

    Uniformity trial of potato in Africa in 2001

    -
    - - 
    data("obsi.potato.uniformity")
    - - -

    Format

    - -

    A data frame with 2569 observations on the following 4 variables.

    -
    loc

    location, 2 levels

    -
    row

    row

    -
    col

    column

    -
    yield

    yield

    - -
    - -

    Details

    - -

    Data collected from potato uniformity trials at Hollota (L1) and Kulumsa (L2). - Each field was 0.15 hectares. - In each field, 75cm between rows and 60cm between plants. The basic - units harvested were 1.2m x 1.5m. It is not clear which way the plots - are oriented in the field with respect to the rows and columns.

    -

    At location L1, plot (10,7) was 22.5 in the source document, but was - changed to 2.25 for this electronic data.

    -

    Note the horizontal banding of 8 or 9 rows at location L1.

    -

    Source

    - -

    Dechassa Obsi. 2008. - Application of Spatial Modeling to the Study of Soil Fertility Pattern. - MS Thesis, Addis Ababa University. Page 122-125. - https://etd.aau.edu.et/handle/123456789/3221

    -

    References

    - -

    None.

    - -

    Examples

    - 
    library(agridat)
-data(obsi.potato.uniformity)
-dat <- obsi.potato.uniformity
-
-# libs(dplyr)
-# dat <- group_by(dat, loc)
-# summarize(dat, yield=mean(yield))
-##   loc   yield
-##   <fct> <dbl>
-## 1 L1     2.54 # Obsi says 2.55
-## 2 L2     5.31 # Obsi says 5.36
-
-# \dontrun{
-libs(desplot)
-desplot(dat, yield ~ col*row, subset=loc=="L1",
-  main="obsi.potato.uniformity - loc L1",
-  flip=TRUE, tick=TRUE)
-
    desplot(dat, yield ~ col*row, subset=loc=="L2",
-  main="obsi.potato.uniformity - loc L2",
-  flip=TRUE, tick=TRUE)
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/odland.soy.uniformity-1.png b/docs/reference/odland.soy.uniformity-1.png deleted file mode 100644 index efb256b4..00000000 Binary files a/docs/reference/odland.soy.uniformity-1.png and /dev/null differ diff --git a/docs/reference/odland.soy.uniformity-2.png b/docs/reference/odland.soy.uniformity-2.png deleted file mode 100644 index 7d8949d9..00000000 Binary files a/docs/reference/odland.soy.uniformity-2.png and /dev/null differ diff --git a/docs/reference/odland.soy.uniformity.html b/docs/reference/odland.soy.uniformity.html deleted file mode 100644 index 8987c7d6..00000000 --- a/docs/reference/odland.soy.uniformity.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Uniformity trials of soy hay and soybeans — odland.soybean.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of soy hay and soybeans

    - - -
    - -
    -

    Uniformity trials of soy hay and soybeans at Virginia Experiment - Station, 1925-1926.

    -
    - - - -

    Format

    - -

    Data frames with 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield: hay in tons, beans in bushels

    - -
    - -

    Details

    - - - -

    Grown at West Virginia Experiment Station in 1925 & 1926. - Soybeans were planted in rows 30 inches apart.

    -

    Soy hay

    -

    In 1925 the crop was harvested for forage, 42 rows, each 200 feet long. - Yields of 8-foot plats recorded.

    -

    Field width: 42 plots * 30 in / 12in/ft = 105 ft

    -

    Field length: 24 plots * 8 feet = 192 feet + border = total 200 feet.

    -

    Soy beans

    -

    In 1926 the crop was harvested for seed, 55 rows, each 232 feet - long. Yields of 8-foot plats were recorded. - In 1926, data for the last row on page 96 seems to be missing.

    -

    Field width: 55 plots * 30 in / 12in/ft = 137.5 feet

    -

    Field length: 28 plots * 8 feet = 224 feet + border = total 232 feet.

    -

    Odland and Garber provide no agronomic context for the yield variation.

    -

    Source

    - -

    Odland, TE and Garber, RJ. 1928. - Size of Plat and Number of Replications in Field Experiments with - Soybeans. - Agronomy Journal, 20, 93--108. - https://doi.org/10.2134/agronj1928.00021962002000020002x

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  libs(desplot)
-
-  data(odland.soyhay.uniformity)
-  dat1 <- odland.soyhay.uniformity
-  desplot(dat1, yield ~ col*row,
-          flip=TRUE, aspect=200/105, # true aspect
-          main="odland.soyhay.uniformity")
-
    
-  data(odland.soybean.uniformity)
-  dat2 <- odland.soybean.uniformity
-  desplot(dat2, yield ~ col*row,
-          flip=TRUE, aspect = 232/137, 
-          main="odland.soybean.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/odland.soybean.uniformity-1.png b/docs/reference/odland.soybean.uniformity-1.png deleted file mode 100644 index 5e897faa..00000000 Binary files a/docs/reference/odland.soybean.uniformity-1.png and /dev/null differ diff --git a/docs/reference/odland.soybean.uniformity-2.png b/docs/reference/odland.soybean.uniformity-2.png deleted file mode 100644 index 05a0f7af..00000000 Binary files a/docs/reference/odland.soybean.uniformity-2.png and /dev/null differ diff --git a/docs/reference/odland.soybean.uniformity.html b/docs/reference/odland.soybean.uniformity.html deleted file mode 100644 index 5ea4f72c..00000000 --- a/docs/reference/odland.soybean.uniformity.html +++ /dev/null @@ -1,166 +0,0 @@ - -Uniformity trials of soy hay and soybeans — odland.soybean.uniformity • agridat - - -
    -
    - - - -
    -
    -
    -

    Uniformity trials of soy hay and soybeans

    - - -
    - -
    -

    Uniformity trials of soy hay and soybeans at Virginia Experiment - Station, 1925-1926.

    -
    - - -
    -

    Format

    -

    Data frames with 3 variables.

    row
    -

    row

    - -
    col
    -

    column

    - -
    yield
    -

    yield: hay in tons, beans in bushels

    - - -
    -
    -

    Details

    - - -

    Grown at West Virginia Experiment Station in 1925 & 1926. - Soybeans were planted in rows 30 inches apart.

    -

    Soy hay

    -

    In 1925 the crop was harvested for forage, 42 rows, each 200 feet long. - Yields of 8-foot plats recorded.

    -

    Field width: 42 plots * 30 in / 12in/ft = 105 ft

    -

    Field length: 24 plots * 8 feet = 192 feet + border = total 200 feet.

    -

    Soy beans

    -

    In 1926 the crop was harvested for seed, 55 rows, each 232 feet - long. Yields of 8-foot plats were recorded. - In 1926, data for the last row on page 96 seems to be missing.

    -

    Field width: 55 plots * 30 in / 12in/ft = 137.5 feet

    -

    Field length: 28 plots * 8 feet = 224 feet + border = total 232 feet.

    -

    Odland and Garber provide no agronomic context for the yield variation.

    -
    -
    -

    Source

    -

    Odland, TE and Garber, RJ. 1928. - Size of Plat and Number of Replications in Field Experiments with - Soybeans. - Agronomy Journal, 20, 93--108. - https://doi.org/10.2134/agronj1928.00021962002000020002x

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  libs(desplot)
-
-  data(odland.soyhay.uniformity)
-  dat1 <- odland.soyhay.uniformity
-  desplot(dat1, yield ~ col*row,
-          flip=TRUE, aspect=200/105, # true aspect
-          main="odland.soyhay.uniformity")
-
-
-  data(odland.soybean.uniformity)
-  dat2 <- odland.soybean.uniformity
-  desplot(dat2, yield ~ col*row,
-          flip=TRUE, aspect = 232/137, 
-          main="odland.soybean.uniformity")
-
-  
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/omer.sorghum.html b/docs/reference/omer.sorghum.html deleted file mode 100644 index f45bbd4b..00000000 --- a/docs/reference/omer.sorghum.html +++ /dev/null @@ -1,259 +0,0 @@ - - - - - - - - -Multi-environment trial of sorghum, 6 environments — omer.sorghum • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of sorghum, 6 environments

    - - -
    - -
    -

    Multi-environment trial of sorghum, 6 environments

    -
    - - 
    data("omer.sorghum")
    - - -

    Format

    - -

    A data frame with 432 observations on the following 4 variables.

    -
    env

    environment

    -
    rep

    replication

    -
    gen

    genotype factor

    -
    yield

    yield, kg/ha

    - -
    - -

    Details

    - -

    Trials were conducted in Sudan, 3 years at 2 locations, 4 reps in RCBD - at each location. The year and location have been combined to form 6 - environments. Only environments are given in the data, not the - individual year and location.

    -

    Source

    - -

    Siraj Osman Omer, Abdel Wahab Hassan Abdalla, Mohammed Hamza Mohammed, Murari Singh (2015). - Bayesian estimation of genotype-by-environment interaction in sorghum variety trials - Communications in Biometry and Crop Science, 10 (2), 82-95.

    -

    Electronic data provided by Siraj Osman Omer.

    -

    References

    - -

    None.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(omer.sorghum)
-dat <- omer.sorghum
-
-# REML approach
-libs(lme4)
-libs(lucid)
-
-# 1 loc, 2 years. Match Omer table 1.
-m1 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env),
-           data=subset(dat, is.element(env, c('E2','E4'))))
-vc(m1)
-
    #>       grp        var1 var2    vcov  sdcor
-#>   gen:env (Intercept) <NA> 17050   130.6 
-#>       gen (Intercept) <NA>  2760    52.54
-#>   env:rep (Intercept) <NA>   959.2  30.97
-#>  Residual        <NA> <NA> 43090   207.6 
    ##      grp        var1 var2    vcov  sdcor
-##  gen:env (Intercept) <NA> 17050   130.6
-##      gen (Intercept) <NA>  2760    52.54
-##  env:rep (Intercept) <NA>   959.1  30.97
-## Residual        <NA> <NA> 43090   207.6
-
-# 1 loc, 3 years. Match Omer table 1.
-m2 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env),
-           data=subset(dat, is.element(env, c('E2','E4','E6'))))
-vc(m2)
-
    #>       grp        var1 var2  vcov  sdcor
-#>   gen:env (Intercept) <NA> 22210 149   
-#>       gen (Intercept) <NA>  9287  96.37
-#>   env:rep (Intercept) <NA>  1332  36.5 
-#>  Residual        <NA> <NA> 40270 200.7 
    ##      grp        var1 var2  vcov  sdcor
-##  gen:env (Intercept) <NA> 22210 149
-##      gen (Intercept) <NA>  9288  96.37
-##  env:rep (Intercept) <NA>  1332  36.5
-## Residual        <NA> <NA> 40270 200.7
-
-# all 6 locs. Match Omer table 3, frequentist approach
-m3 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env),
-           data=dat)
-vc(m3)
-
    #>       grp        var1 var2  vcov  sdcor
-#>   gen:env (Intercept) <NA> 21340 146.1 
-#>   env:rep (Intercept) <NA>  1152  33.95
-#>       gen (Intercept) <NA>  1169  34.19
-#>  Residual        <NA> <NA> 24660 157   
    ##       grp        var1 var2  vcov  sdcor
-##   gen:env (Intercept) <NA> 21340 146.1
-##   env:rep (Intercept) <NA>  1152  33.95
-##       gen (Intercept) <NA>  1169  34.2
-##  Residual        <NA> <NA> 24660 157
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/onofri.winterwheat-1.png b/docs/reference/onofri.winterwheat-1.png deleted file mode 100644 index 0b0bee1b..00000000 Binary files a/docs/reference/onofri.winterwheat-1.png and /dev/null differ diff --git a/docs/reference/onofri.winterwheat.html b/docs/reference/onofri.winterwheat.html deleted file mode 100644 index a1b99034..00000000 --- a/docs/reference/onofri.winterwheat.html +++ /dev/null @@ -1,237 +0,0 @@ - - - - - - - - -Multi-environment trial of winter wheat, 7 years — onofri.winterwheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of winter wheat, 7 years

    - - -
    - -
    -

    Multi-environment trial of winter wheat, 7 years, 8 gen

    -
    - - 
    data("onofri.winterwheat")
    - - -

    Format

    - -

    A data frame with 168 observations on the following 5 variables.

    -
    year

    year, numeric

    -
    block

    block, 3 levels

    -
    plot

    plot, numeric

    -
    gen

    genotype, 7 levels

    -
    yield

    yield for each plot

    - -
    - -

    Details

    - -

    Yield of 8 durum winter wheat varieties across 7 years with 3 reps.

    -

    Downloaded electronic version from here Nov 2015: - https://www.casaonofri.it/Biometry/index.html

    -

    Used with permission of Andrea Onofri.

    -

    Source

    - -

    Andrea Onofri, Egidio Ciriciofolo (2007). - Using R to Perform the AMMI Analysis on Agriculture Variety Trials. - R News, Vol. 7, No. 1, pp. 14-19.

    -

    References

    - -

    F. Mendiburu. - AMMI. - https://tarwi.lamolina.edu.pe/~fmendiburu/AMMI.htm

    -

    A. Onofri. - https://accounts.unipg.it/~onofri/RTutorial/CaseStudies/WinterWheat.htm

    - -

    Examples

    - 
    
-library(agridat)
-data(onofri.winterwheat)
-dat <- onofri.winterwheat
-dat <- transform(dat, year=factor(dat$year))
-
-m1 <- aov(yield ~ year + block:year + gen + gen:year, dat)
-anova(m1) # Matches Onofri figure 1
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>            Df  Sum Sq Mean Sq  F value    Pr(>F)    
-#> year        6 159.279 26.5466 178.3996 < 2.2e-16 ***
-#> gen         7  11.544  1.6491  11.0824 2.978e-10 ***
-#> year:block 14   3.922  0.2801   1.8826   0.03738 *  
-#> year:gen   42  27.713  0.6598   4.4342 6.779e-10 ***
-#> Residuals  98  14.583  0.1488                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# \dontrun{
-  libs(agricolae)
-  m2 <- AMMI(dat$year, dat$gen, dat$block, dat$yield)
-  plot(m2)
-
      title("onofri.winterwheat - AMMI biplot")
-
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ortiz.tomato-1.png b/docs/reference/ortiz.tomato-1.png deleted file mode 100644 index 53034eb2..00000000 Binary files a/docs/reference/ortiz.tomato-1.png and /dev/null differ diff --git a/docs/reference/ortiz.tomato.html b/docs/reference/ortiz.tomato.html deleted file mode 100644 index 0f3f36b2..00000000 --- a/docs/reference/ortiz.tomato.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - - -Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates

    - - -
    - -
    -

    Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates

    -
    - - 
    data("ortiz.tomato.covs")
-  data("ortiz.tomato.yield")
    - - -

    Format

    - - - -

    The ortiz.tomato.covs data frame has 18 observations on the following 18 variables.

    -
    env

    environment

    -
    Day

    degree days (base 10)

    -
    Dha

    days to harvest

    -
    Driv

    drivings (0/1)

    -
    ExK

    extra potassium (kg / ha)

    -
    ExN

    extra nitrogen (kg / ha)

    -
    ExP

    extra phosphorous (kg / ha)

    -
    Irr

    irrigation (0/1)

    -
    K

    potassium (me/100 g)

    -
    Lat

    latitude

    -
    Long

    longitude

    -
    MeT

    mean temperature (C)

    -
    MnT

    min temperature (C)

    -
    MxT

    max temperature (C)

    -
    OM

    organic matter (percent)

    -
    P

    phosphorous (ppm)

    -
    pH

    soil pH

    -
    Prec

    precipitation (mm)

    -
    Tri

    trimming (0/1)

    - -
    - -

    The ortiz.tomato.yield data frame has 270 observations on the following 4 variables.

    -
    env

    environment

    -
    gen

    genotype

    -
    yield

    marketable fruit yield t/ha

    -
    weight

    fruit weight, g

    - -
    - - -

    Details

    - - - -

    The environment locations are:

    - - - - - - - - - - - - - - - - - - -
    E04Estanzuela, Guatemala
    E05Baja Verapaz, Guatemala
    E06Cogutepeque, El Salvador
    E07San Andres, El Salvador
    E11Comayagua, Honduras
    E14Valle de Sabaco, Nicaragua
    E15San Antonio de Belen, Costa Rica
    E20San Cristobal, Dominican Republic
    E21Constanza, Dominican Republic
    E27Palmira, Colombia
    E40La Molina, Peru
    E41Santiago, Chile
    E42Chillan, Chile
    E43Curacavi, Chile
    E44Colina, Chile
    E50Belem, Brazil
    E51Caacupe, Paraguay
    E53Centeno, Trinidad Tobago
    - - - -

    Used with permission of Rodomiro Ortiz.

    -

    Source

    - -

    Rodomiro Ortiz and Jose Crossa and Mateo Vargas and Juan Izquierdo, 2007. - Studying the Effect of Environmental Variables On the Genotype x - Environment Interaction of Tomato. - Euphytica, 153, 119--134. - https://doi.org/10.1007/s10681-006-9248-7

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(ortiz.tomato.covs)
-data(ortiz.tomato.yield)
-
-libs(pls, reshape2)
-# Double-centered yield matrix
-Y <- acast(ortiz.tomato.yield, env ~ gen, value.var='yield')
-Y <- sweep(Y, 1, rowMeans(Y, na.rm=TRUE))
-Y <- sweep(Y, 2, colMeans(Y, na.rm=TRUE))
-
-# Standardized covariates
-X <- ortiz.tomato.covs
-rownames(X) <- X$env
-X <- X[,c("MxT", "MnT", "MeT", "Prec", "Day", "pH", "OM", "P", "K",
-          "ExN", "ExP", "ExK", "Trim", "Driv", "Irr", "Dha")]
-X <- scale(X)
-
-# Now, PLS relating the two matrices.
-# Note: plsr deletes observations with missing values
-
-m1 <- plsr(Y~X)
-# Inner-product relationships similar to Ortiz figure 1.
-biplot(m1, which="x", var.axes=TRUE, main="ortiz.tomato - env*cov biplot")
-
    #biplot(m1, which="y", var.axes=TRUE)
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/pacheco.soybean-1.png b/docs/reference/pacheco.soybean-1.png deleted file mode 100644 index ba4ec84d..00000000 Binary files a/docs/reference/pacheco.soybean-1.png and /dev/null differ diff --git a/docs/reference/pacheco.soybean.html b/docs/reference/pacheco.soybean.html deleted file mode 100644 index 2eed53c6..00000000 --- a/docs/reference/pacheco.soybean.html +++ /dev/null @@ -1,220 +0,0 @@ - - - - - - - - -Multi-environment trial of soybean in Brazil. — pacheco.soybean • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of soybean in Brazil.

    - - -
    - -
    -

    Yields of 18 soybean genotypes at 11 environments in Brazil.

    -
    - - - -

    Format

    - -

    -
    gen

    genotype, 18 levels

    -
    env

    environment, 11 levels

    -
    yield

    yield, kg/ha

    - -
    - -

    Details

    - -

    In each environment was used an RCB design with 3 reps. The means of - the reps are shown here.

    -

    Used with permission of Robert Pacheco.

    -

    Source

    - -

    R M Pacheco, J B Duarte, R Vencovsky, J B Pinheiro, A B Oliveira, - (2005). Use of supplementary genotypes in AMMI analysis. - Theor Appl Genet, 110, 812-818. - https://doi.org/10.1007/s00122-004-1822-6

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(pacheco.soybean)
-dat <- pacheco.soybean
-
-# AMMI biplot similar to Fig 2 of Pacheco et al.
-libs(agricolae)
-m1 <- with(dat, AMMI(env, gen, REP=1, yield))
-bip <- m1$biplot[,1:3]
-# Fig 1 of Pacheco et al.
-with(bip, plot(yield, PC1, cex=0.0,
-               text(yield,PC1,labels=row.names(bip), col="blue"),
-               xlim=c(1000,3000),main="pacheco.soybean - AMMI biplot",frame=TRUE))
-
    with(bip[19:29,], points(yield, PC1, cex=0.0,
-                         text(yield,PC1,labels=row.names(bip[19:29,]),
-                              col="darkgreen")))
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/paez.coffee.uniformity-1.png b/docs/reference/paez.coffee.uniformity-1.png deleted file mode 100644 index de7933d7..00000000 Binary files a/docs/reference/paez.coffee.uniformity-1.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity-2.png b/docs/reference/paez.coffee.uniformity-2.png deleted file mode 100644 index 12343cf1..00000000 Binary files a/docs/reference/paez.coffee.uniformity-2.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity-3.png b/docs/reference/paez.coffee.uniformity-3.png deleted file mode 100644 index 8752a7d1..00000000 Binary files a/docs/reference/paez.coffee.uniformity-3.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity-4.png b/docs/reference/paez.coffee.uniformity-4.png deleted file mode 100644 index 5869f06f..00000000 Binary files a/docs/reference/paez.coffee.uniformity-4.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity-5.png b/docs/reference/paez.coffee.uniformity-5.png deleted file mode 100644 index 43ce4cdf..00000000 Binary files a/docs/reference/paez.coffee.uniformity-5.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity-6.png b/docs/reference/paez.coffee.uniformity-6.png deleted file mode 100644 index 3d8edc6a..00000000 Binary files a/docs/reference/paez.coffee.uniformity-6.png and /dev/null differ diff --git a/docs/reference/paez.coffee.uniformity.html b/docs/reference/paez.coffee.uniformity.html deleted file mode 100644 index d5fe5087..00000000 --- a/docs/reference/paez.coffee.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trial of coffee — paez.coffee.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of coffee

    - - -
    - -
    -

    Uniformity trial of coffee in Caldas Columbia

    -
    - - 
    data("paez.coffee.uniformity")
    - - -

    Format

    - -

    A data frame with 4190 observations on the following 5 variables.

    -
    plot

    plot number

    -
    row

    row

    -
    col

    column

    -
    year

    year

    -
    yield

    yield per tree, kilograms

    - -
    - -

    Details

    - -

    The field map on Paez page 56, has plots 1 to 838. - The data tables on page 79-97 have data for plots 1 to 900.

    -

    Note: The 'row' ordinate in this data would imply that the rows and - columns are perpendicular. But the field map on page 56 of Paez shows - that the rows are not at a 90-degree angle compared to the columns, - but only at a 60-degree angle compared to the columns. In other - words, the columns are vertical, and the rows are sloping up and right - at about 30 degrees.

    -

    Paez looks at blocks that are 1,2,...36 trees in size. - Page 30 shows annual CV.

    -

    Source

    - -

    Gilberto Paez Bogarin (1962). - Estudios sobre tamano y forma de parcela para ensayos en cafe. - Instituto Interamericano de Ciencias Agricolas de la O.E.A. Centro Tropical de Investigacion y Ensenanza para Graduados. Costa Rica. - https://hdl.handle.net/11554/1892

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(paez.coffee.uniformity)
-dat <- paez.coffee.uniformity
-
-libs(reshape2, corrgram)
-  datt <- acast(dat, plot ~ year)
-
    #> Using yield as value column: use value.var to override.
      corrgram(datt, lower.panel=panel.pts,
-           main="paez.coffee.uniformity")
-
    
-
-# Not quite right. The rows are not actually horizontal.  See notes above.
-libs(desplot)
-desplot(dat, yield ~ col*row,subset=year=="Y1",
-        tick=TRUE, aspect=1,
-        main="paez.coffee.uniformity - Y1")
-
    desplot(dat, yield ~ col*row,subset=year=="Y2",
-        tick=TRUE, aspect=1,
-        main="paez.coffee.uniformity - Y2")
-
    desplot(dat, yield ~ col*row,subset=year=="Y3",
-        tick=TRUE, aspect=1,
-        main="paez.coffee.uniformity - Y3")
-
    desplot(dat, yield ~ col*row,subset=year=="Y4",
-        tick=TRUE, aspect=1,
-        main="paez.coffee.uniformity - Y4")
-
    desplot(dat, yield ~ col*row,subset=year=="Y5",
-        tick=TRUE, aspect=1,
-        main="paez.coffee.uniformity - Y5")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/panse.cotton.uniformity-1.png b/docs/reference/panse.cotton.uniformity-1.png deleted file mode 100644 index 4fe9ddbf..00000000 Binary files a/docs/reference/panse.cotton.uniformity-1.png and /dev/null differ diff --git a/docs/reference/panse.cotton.uniformity.html b/docs/reference/panse.cotton.uniformity.html deleted file mode 100644 index 82ec2282..00000000 --- a/docs/reference/panse.cotton.uniformity.html +++ /dev/null @@ -1,237 +0,0 @@ - - - - - - - - -Uniformity trial of cotton — panse.cotton.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of cotton

    - - -
    - -
    -

    Uniformity trial of cotton in India in 1934.

    -
    - - 
    data("panse.cotton.uniformity")
    - - -

    Format

    - -

    A data frame with 1280 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    total yield per plot, grams

    - -
    - -

    Details

    - -

    A uniformity trial of cotton at the Institute of Plant Industry, Indore, India.

    -

    The trial consisted of 128 rows of cotton with a spacing of 14 in between rows and length 186 feet 8 in.

    -

    Each harvested plot was 4 rows wide and 4 ft 8 in long, measuring 1/2000 acre.

    -

    Four pickings were made between Nov 1933 and Jan 1934. The data here are the total yields.

    -

    The fertility map shows appreciable variation, not following any - systematic pattern.

    -

    Field length: 40 plots * 4 feet 8 inches = 206 feet 8 inches

    -

    Field width: 32 plots * 4 rows/plot * 14 inches/row = 150 feet

    -

    Conclusions: Lower error was obtained when the plots were long rows - instead of across the rows.

    -

    Source

    - -

    V. G. Panse (1941). - Studies in the technique of field experiments. V. Size and shape of - blocks and arrangements of plots in cotton trials. - The Indian Journal Of Agricultural Science, 11, 850-867 - https://archive.org/details/in.ernet.dli.2015.271747/page/n955

    -

    References

    - -

    Hutchinson, J. B. and V. G. Panse (1936). - Studies in the technique of field experiments. I. Size, shape and - arrangement of plots in cotton trials. - Indian J. Agric. Sci., 5, 523-538. - https://archive.org/details/in.ernet.dli.2015.271739/page/n599

    -

    V.G. Panse and P.V. Sukhatme. (1954). - Statistical Methods for Agricultural Workers. - First edition page 137. Fourth edition, page 131.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(panse.cotton.uniformity)
-dat <- panse.cotton.uniformity
-  
-# match the CV of Panse 1954
-# sd(dat$yield)/mean(dat$yield) * 100
-# 32.1
-  
-# match the fertility map of Hutchinson, fig 1
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, aspect=207/150, # true aspect
-        main="panse.cotton.uniformity")
-
    
-# }  
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/parker.orange.uniformity-1.png b/docs/reference/parker.orange.uniformity-1.png deleted file mode 100644 index 933b0661..00000000 Binary files a/docs/reference/parker.orange.uniformity-1.png and /dev/null differ diff --git a/docs/reference/parker.orange.uniformity-2.png b/docs/reference/parker.orange.uniformity-2.png deleted file mode 100644 index fbb3df69..00000000 Binary files a/docs/reference/parker.orange.uniformity-2.png and /dev/null differ diff --git a/docs/reference/parker.orange.uniformity-3.png b/docs/reference/parker.orange.uniformity-3.png deleted file mode 100644 index 0ee2b3be..00000000 Binary files a/docs/reference/parker.orange.uniformity-3.png and /dev/null differ diff --git a/docs/reference/parker.orange.uniformity.html b/docs/reference/parker.orange.uniformity.html deleted file mode 100644 index 59b8a7d1..00000000 --- a/docs/reference/parker.orange.uniformity.html +++ /dev/null @@ -1,313 +0,0 @@ - - - - - - - - -Uniformity trial of oranges — parker.orange.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of oranges

    - - -
    - -
    -

    Uniformity trial of oranges at Riverside, CA, 1921-1927.

    -
    - - 
    data("parker.orange.uniformity")
    - - -

    Format

    - -

    A data frame with 1364 observations on the following 4 variables.

    -
    year

    year

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds/tree for plot

    - -
    - -

    Details

    - - - -

    An orchard of naval oranges was planted in 1917 at the University of - California Citrus Experiment Station at Riverside. The orchard was - maintained under uniform conditions for 10 years.

    -

    Eight Washington Navel orange trees in a single row constituted a - plot. The planting distance is 20 feet between trees within the row - and 24 feet between rows. Every other row was a guard row, so row 2 - and row 4 were observational units, while row 3 was a guard row. For - example, from row 2 to row 4 is 2*24 = 48 feet. Another way to think - of this is that each plot was 48 feet wide, but only the middle 24 - feet was harvested. At each end of the plot was one guard - tree. Including guard trees at the row ends, each row plot was 10 - trees * 20 feet = 200 feet long.

    -

    Field width (west-east) 10 plots * 200 feet = 2000 feet.

    -

    Field length (north-south) 27 plots * 48 feet = 1296 feet.

    -

    An investigation into the variability between plots included - systematic soil surveys, soil moisture, soil nitrates, and inspection - for differences in infestation of the citrus nematode. None of these - factors was considered to be the primary cause of the variations in - yield.

    -

    After the 7 years of uniformity trials, different treatments were - applied to the plots.

    -

    Parker et al. state that soil heterogeneity is considerable and - first-year yields are not predictive of future yields.

    -

    Table 25 has mean top volume per tree for each plot in 1926. Table 26 - has mean area of trunk cross section.

    -

    Source

    - -

    E. R. Parker & L. D. Batchelor. (1932). - Variation in the Yields of Fruit Trees in Relation to the Planning of Future Experiments. - Hilgardia, 7(2), 81-161. Tables 3-9. - https://doi.org/10.3733/hilg.v07n02p081

    -

    References

    - -

    Batchelor, L. D. (Leon Dexter), b. 1884; Parker, E. R. (Edwin Robert), 1896-1952; McBride, Robert, d. 1927. (1928) - Studies preliminary to the establishment of a series of fertilizer - trials in a bearing citrus grove. - Vol B451. Berkeley, Cal. : Agricultural Experiment Station - https://archive.org/details/studiesprelimina451batc

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(parker.orange.uniformity)
-dat <- parker.orange.uniformity
-
-# Parker fig 2, field plan
-libs(desplot)
-dat$year <- factor(dat$year)
-# 27 rows * 48 ft x 10 cols * 200 feet
-desplot(dat, yield ~ col*row|year,
-        flip = TRUE, aspect = 27*48/(10*200), # true aspect
-        main = "parker.orange.uniformity")
-
    
-# CV across plots in each year. Similar to Parker table 11
-cv <- function(x) {
-  x <- na.omit(x)
-  sd(x)/mean(x)
-}
-round(100*tapply(dat$yield, dat$year, cv),2)
-
    #>  1921  1922  1923  1924  1925  1926  1927 
-#> 38.68 21.09 18.74 14.38 13.81 22.32 13.86 
    
-
-# Correlation of plot yields across years. Similar to Parker table 15.
-# Paker et al may have calculated correlation differently.
-libs(reshape2)
-libs(corrgram)
-dat2 <- acast(dat, row+col ~ year, value.var = 'yield')
-round(cor(dat2, use = "pair"),3)
-
    #>        1921  1922  1923   1924  1925   1926   1927
-#> 1921  1.000 0.660 0.168 -0.046 0.116 -0.290 -0.046
-#> 1922  0.660 1.000 0.177  0.275 0.470  0.025  0.121
-#> 1923  0.168 0.177 1.000  0.650 0.422  0.433  0.361
-#> 1924 -0.046 0.275 0.650  1.000 0.759  0.681  0.605
-#> 1925  0.116 0.470 0.422  0.759 1.000  0.651  0.558
-#> 1926 -0.290 0.025 0.433  0.681 0.651  1.000  0.617
-#> 1927 -0.046 0.121 0.361  0.605 0.558  0.617  1.000
    corrgram(dat2, lower = panel.pts, upper = panel.conf,
-         main="parker.orange.uniformity")
-
    
-
-# Fertility index. Mean across years (ignoring 1921). Parker table 16
-dat3 <- aggregate(yield ~ row+col, data = subset(dat, year !=1921 ),
-                  FUN = mean, na.rm = TRUE)
-round(acast(dat3, row ~ col, value.var = 'yield'),0)
-
    #>      1   2   3   4   5   6   7   8   9  10
-#> 1   87 103  96 103 113  NA  NA  NA 100 121
-#> 2   95 106 119 104 120  NA  NA  NA 127 140
-#> 3  111 110 109  99 140  NA  NA  NA 116 141
-#> 4   78 112  98 114 128  NA  NA  NA 139 137
-#> 5   98 113 116 110 122  NA  NA  NA 130 128
-#> 6  102 109 122 128 113  NA  NA  NA 140 133
-#> 7   93 126 137 128 108 132  NA  NA 132 138
-#> 8  100 116 129 119 113 130  NA  NA 100 107
-#> 9   92 115 112 119 120 132  NA  NA 124 105
-#> 10  98 116 127 135 124 131  NA  NA 116 104
-#> 11  98 138 132 137 138 116 120  NA 122 124
-#> 12 103  NA 121 119 115 114 123  NA 118 121
-#> 13  97 121 104 110 117 115 103  NA  NA 130
-#> 14 119 119 129 132 129 138 140  NA  NA 116
-#> 15 108 131 148 130 152 153 140  NA  NA 136
-#> 16 127 127 135 134 126 129 133 152  NA 139
-#> 17 119  NA 140 134 121 139 134 128  NA 128
-#> 18 135 146 146 147 150 143 127 138  NA 136
-#> 19 120 137 119 124 136 142 148 128  NA 133
-#> 20 121 138 135 139 142 150 132 130  NA 122
-#> 21 114 128 157 146 144 142 155 153  NA 138
-#> 22 107 126 143 134 126  NA 144 165  NA 144
-#> 23  NA  NA  NA  NA  NA 126 155 155  NA 146
-#> 24  NA  NA  NA  NA  NA 129 150 140  NA 118
-#> 25  NA  NA  NA  NA  NA 134 142 131  NA 115
-#> 26  NA  NA  NA  NA  NA 114 150 149  NA 122
-#> 27  NA  NA  NA  NA  NA 114 136  NA  NA 108
    
-libs(desplot)
-desplot(dat3, yield ~ col*row,
-        flip = TRUE, aspect = 27*48/(10*200), # true aspect
-        main = "parker.orange.uniformity - mean across years")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/patterson.switchback-1.png b/docs/reference/patterson.switchback-1.png deleted file mode 100644 index f55dd7e0..00000000 Binary files a/docs/reference/patterson.switchback-1.png and /dev/null differ diff --git a/docs/reference/patterson.switchback.html b/docs/reference/patterson.switchback.html deleted file mode 100644 index da3fa52e..00000000 --- a/docs/reference/patterson.switchback.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Switchback experiment on dairy cattle, milk yield for 4 treatments

    - - -
    - -
    -

    Switchback experiment on dairy cattle, milk yield for 4 treatments

    -
    - - 
    data("patterson.switchback")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 4 variables.

    -
    y

    response, milk FCM

    -
    trt

    treatment factor, 4 levels

    -
    period

    period factor, 3 levls

    -
    cow

    cow factor, 12 levels

    - -
    - -

    Details

    - -

    There are three periods. Each cow is assigned to one treatment cycle - like T1-T2-T1, where T1 is the treatment in period P1 and P3, and T2 - is the treatment in period P2.

    -

    There are four treatments.

    -

    All 4*3 = 12 treatment cycles are represented.

    -

    Data were extracted from Lowry, page 70.

    -

    Source

    - -

    Patterson, H.D. and Lucas, H.L. 1962. - Change-over designs. - Technical Bulletin 147, North Carolina Agricultural Experimental Station.

    -

    References

    - -

    Lowry, S.R. 1989. - Statistical design and analysis of dairy nutrition experiments to - improve detection of milk response differences. - Proceedings of the Conference on Applied Statistics in Agriculture, 1989. - https://newprairiepress.org/agstatconference/1989/proceedings/7/

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(patterson.switchback)
-dat <- patterson.switchback
-
-# Create groupings for first treatment, second treatment
-datp1 <- subset(dat, period=="P1")
-datp2 <- subset(dat, period=="P2")
-dat$p1trt <- datp1$trt[match(dat$cow, datp1$cow)]
-dat$p2trt <- datp2$trt[match(dat$cow, datp2$cow)]
-                     
-libs(latticeExtra)
-useOuterStrips(xyplot(y ~ period|p1trt*p2trt, data=dat,
-                      group=cow, type=c('l','r'),
-                      auto.key=list(columns=5),
-                      main="patterson.switchback",
-                      xlab="First/Third period treatment", 
-                      ylab="Second period treatment"))
-
    
-
-# Create a numeric period variable
-dat$per <- as.numeric(substring(dat$period,2))
-
-# Need to use 'terms' to preserve the order of the model terms
-m1 <- aov(terms(y ~ cow + per:cow + period + trt, keep.order=TRUE), data=dat)
-anova(m1) # Match table 2 of Lowry
-
    #> Analysis of Variance Table
-#> 
-#> Response: y
-#>           Df Sum Sq Mean Sq F value    Pr(>F)    
-#> cow       11 3466.0 315.091 57.1773 2.258e-06 ***
-#> cow:per   12  953.5  79.455 14.4182 0.0004017 ***
-#> period     1   19.7  19.740  3.5821 0.0950382 .  
-#> trt        3   58.3  19.418  3.5237 0.0685092 .  
-#> Residuals  8   44.1   5.511                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## Analysis of Variance Table
-##           Df Sum Sq Mean Sq F value    Pr(>F)
-## cow       11 3466.0 315.091 57.1773 2.258e-06 ***
-## cow:per   12  953.5  79.455 14.4182 0.0004017 ***
-## period     1   19.7  19.740  3.5821 0.0950382 .
-## trt        3   58.3  19.418  3.5237 0.0685092 .
-## Residuals  8   44.1   5.511
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/pearce.apple-1.png b/docs/reference/pearce.apple-1.png deleted file mode 100644 index 24998126..00000000 Binary files a/docs/reference/pearce.apple-1.png and /dev/null differ diff --git a/docs/reference/pearce.apple.html b/docs/reference/pearce.apple.html deleted file mode 100644 index 764d21de..00000000 --- a/docs/reference/pearce.apple.html +++ /dev/null @@ -1,242 +0,0 @@ - - - - - - - - -Apple tree yields for 6 treatments with covariate — pearce.apple • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Apple tree yields for 6 treatments with covariate

    - - -
    - -
    -

    Apple tree yields for 6 treatments with covariate of previous yield.

    -
    - - - -

    Format

    - -

    A data frame with 24 observations on the following 4 variables.

    -
    block

    block factor, 4 levels

    -
    trt

    treatment factor, 6 levels

    -
    prev

    previous yield in boxes

    -
    yield

    yield per plot

    - -
    - -

    Details

    - -

    Treatment 'S' is the standard practice in English apple orchards of - keeping the land clean in the summer.

    -

    The previous yield is the number of boxes of fruit, for the four - seasons previous to the application of the treatments.

    -

    Source

    - -

    S. C. Pearce (1953). - Field Experiments With Fruit Trees and - Other Perennial Plants. - Commonwealth Bureau of Horticulture and Plantation Crops, Farnham - Royal, Slough, England, App. IV.

    -

    References

    - -

    James G. Booth, Walter T. Federer, Martin T. Wells and Russell - D. Wolfinger (2009). A Multivariate Variance Components Model for - Analysis of Covariance in Designed Experiments. - Statistical Science, 24, 223-237.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(pearce.apple)
-dat <- pearce.apple
-
-libs(lattice)
-xyplot(yield~prev|block, dat, main="pearce.apple", xlab="previous yield")
-
    
-# Univariate fixed-effects model of Booth et al, using previous
-# yield as a covariate.
-m1 <- lm(yield ~ trt + block + prev, data=dat)
-# Predict values, holding the covariate at its overall mean of 8.3
-newdat <- expand.grid(trt=c('A','B','C','D','E','S'),
-                      block=c('B1','B2','B3','B4'), prev=8.308333)
-newdat$pred <- predict(m1, newdata=newdat)
-# Average across blocks to get the adjusted mean, Booth et al. Table 1
-tapply(newdat$pred, newdat$trt, mean)
-
    #>        A        B        C        D        E        S 
-#> 280.4765 266.5666 274.0666 281.1370 300.9175 251.3357 
    #        A        B        C        D        E        S
-# 280.4765 266.5666 274.0666 281.1370 300.9175 251.3357
-
-# Same thing, but with blocks random
-libs(lme4)
-m2 <- lmer(yield ~ trt + (1|block) + prev, data=dat)
-newdat$pred2 <- predict(m2, newdata=newdat)
-tapply(newdat$pred2, newdat$trt, mean)
-
    #>        A        B        C        D        E        S 
-#> 280.4041 266.5453 274.0453 281.3329 301.3432 250.8291 
    #        A        B        C        D        E        S
-# 280.4041 266.5453 274.0453 281.3329 301.3432 250.8291
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/pearl.kernels-1.png b/docs/reference/pearl.kernels-1.png deleted file mode 100644 index 755b2a1b..00000000 Binary files a/docs/reference/pearl.kernels-1.png and /dev/null differ diff --git a/docs/reference/pearl.kernels-2.png b/docs/reference/pearl.kernels-2.png deleted file mode 100644 index 6638364b..00000000 Binary files a/docs/reference/pearl.kernels-2.png and /dev/null differ diff --git a/docs/reference/pearl.kernels.html b/docs/reference/pearl.kernels.html deleted file mode 100644 index 5a83cd1a..00000000 --- a/docs/reference/pearl.kernels.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - - -Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Counts of yellow/white and sweet/starchy maize kernels by 15 observers

    - - -
    - -
    -

    Counts of yellow/white and sweet/starchy kernels on each of 4 maize - ears by 15 observers.

    -
    - - - -

    Format

    - -

    A data frame with 59 observations on the following 6 variables.

    -
    ear

    ear, 8-11

    -
    obs

    observer, 1-15

    -
    ys

    number of yellow starchy kernels

    -
    yt

    yellow sweet

    -
    ws

    white starchy

    -
    wt

    white sweet

    - -
    - -

    Details

    - -

    An ear of white sweet corn was crossed with an ear of yellow starchy - corn. The F1 kernels of the cross were grown and a sample of four - ears was harvested. The F2 kernels of these ears were classified by - each of 15 observers into white/yellow and sweet/starchy.

    -

    By Mendelian genetics, the kernels should occur in the - ratio 9 yellow starch, 3 white starch, 3 yellow sweet, - 1 white sweet.

    -

    The observers had the following positions:

    - - - - - - - - - - - - - - - -
    1Plant pathologist
    2Asst plant pathologist
    3Prof agronomy
    4Asst prof agronomy
    5Prof philosophy
    6Biologist
    7Biologist
    8Asst biologist
    9Computer
    10Farmer
    11Prof plant physiology
    12Instructor plant physiology
    13Asst plant physiology
    14Asst plant physiology
    15Prof biology
    - - - -

    Source

    - -

    Raymond Pearl, 1911. - The Personal Equation In Breeding Experiments Involving Certain - Characters of Maize, Biol. Bull., 21, 339-366. - https://www.biolbull.org/cgi/reprint/21/6/339.pdf

    - -

    Examples

    - 
    
-library(agridat)
-
-data(pearl.kernels)
-dat <- pearl.kernels
-
-libs(lattice)
-xyplot(ys+yt+ws+wt~obs|ear, dat, type='l', as.table=TRUE,
-       auto.key=list(columns=4),
-       main="pearl.kernels", xlab="observer",ylab="kernels",
-       layout=c(4,1), scales=list(x=list(rot=90)))
-
    
-# Test hypothesis that distribution is 'Mendelian' 9:3:3:1
-dat$pval <- apply(dat[, 3:6], 1, function(x)
-                  chisq.test(x, p=c(9,3,3,1)/16)$p.val)
-dotplot(pval~obs|ear, dat, layout=c(1,4), main="pearl.kernels",
-        ylab="P-value for test of 9:3:3:1 distribution")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/pederson.lettuce.repeated-1.png b/docs/reference/pederson.lettuce.repeated-1.png deleted file mode 100644 index 7711ba0b..00000000 Binary files a/docs/reference/pederson.lettuce.repeated-1.png and /dev/null differ diff --git a/docs/reference/pederson.lettuce.repeated.html b/docs/reference/pederson.lettuce.repeated.html deleted file mode 100644 index cb4879b7..00000000 --- a/docs/reference/pederson.lettuce.repeated.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - - -Repeated measurements of lettuce growth — pederson.lettuce.repeated • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Repeated measurements of lettuce growth

    - - -
    - -
    -

    Repeated measurements of lettuce growth for 3 treatments.

    -
    - - 
    data("pederson.lettuce.repeated")
    - - -

    Format

    - -

    A data frame with 594 observations on the following 4 variables.

    -
    plant

    plant number

    -
    day

    day of observation

    -
    trt

    treatment

    -
    weight

    weight

    - -
    - -

    Details

    - -

    Experiment conducted in a greenhouse in Silver Bay, Minnesota. Plants were grown hydroponically. -Treatment 1 had 9 plants per raft. Treatment 2 had 18 plants, treatment 3 had 36 plants. -The response variable is weight of plant, roots, soil, cup, and water. The plants were measured repeatedly beginning Dec 1, and ending Jan 9, when the plants were harvested.

    -

    Source

    - -

    Levi Dawson Pederson (2015). -Mixed Model Analysis for Repeated Measures of Lettuce Growth -Thesis at University of Minnesota. Appendix C. -https://scse.d.umn.edu/sites/scse.d.umn.edu/files/pedersonprojectthesis.pdf

    -

    References

    - -

    None

    - -

    Examples

    - 
    
-library(agridat)
-data(pederson.lettuce.repeated)
-dat <- pederson.lettuce.repeated
-
-libs(lattice)
-dat <- dat[order(dat$day),]
-xyplot(weight ~ day|trt, dat, type='l', group=plant, layout=c(3,1),
-main="pederson.lettuce.repeated")
-
    
-# \dontrun{
-# Pederson used this SAS MIXED model for unstructured covariance
-# proc mixed data=Project.Spacingdata; 
-# class trt plant day; 
-# model weight=trt day trt*day; 
-# repeated day / subject=plant type=un r rcorr;   
-
-# This should give the same results as SAS, but does not.
-libs(nlme)
-dat <- transform(dat, plant=factor(plant), day=factor(day))
-datg <- groupedData(weight ~ day|plant, data=dat)
-un1 <- gls(weight ~ trt * day, data=datg,
-           correlation=corSymm(value=rep(.6,55), form = ~ 1 | plant),
-           control=lmeControl(opt="optim", msVerbose=TRUE,
-                              maxIter=500, msMaxIter=500))
-
    #> initial  value 2429.796794 
-#> iter  10 value 2043.265582
-#> iter  20 value 2026.878345
-#> iter  30 value 1991.844910
-#> iter  40 value 1974.144855
-#> iter  50 value 1962.086595
-#> iter  60 value 1957.390081
-#> final  value 1957.093673 
-#> converged
    logLik(un1)*2 # nlme has 1955, SAS had 1898.6
-
    #> 'log Lik.' -1955.263 (df=89)
    
-# Comparing the SAS results in Pederson (page 16) and the nlme results, we notice
-# the SAS correlations in table 5.2 are unusually low for the first
-# column. The nlme results have a higher correlation in the first column
-# and just "look" better
-un1
-
    #> Generalized least squares fit by REML
-#>   Model: weight ~ trt * day 
-#>   Data: datg 
-#>   Log-restricted-likelihood: -977.6315
-#> 
-#> Coefficients:
-#> (Intercept)       trtT2       trtT3        day5        day8       day12 
-#> 49.26666667 -0.46111111  0.69444444  0.73888889  2.57222222  4.42777778 
-#>       day15       day18       day21       day26       day33       day36 
-#>  5.66666667  7.05555556 11.14444444 19.70555556 26.26111111 32.78888889 
-#>       day40  trtT2:day5  trtT3:day5  trtT2:day8  trtT3:day8 trtT2:day12 
-#> 38.73333333  0.75555556  1.56111111  0.56111111  0.76111111  0.06111111 
-#> trtT3:day12 trtT2:day15 trtT3:day15 trtT2:day18 trtT3:day18 trtT2:day21 
-#>  0.51666667 -0.05555556  0.69444444 -0.27777778  0.96666667 -0.66666667 
-#> trtT3:day21 trtT2:day26 trtT3:day26 trtT2:day33 trtT3:day33 trtT2:day36 
-#> -0.86666667 -1.72222222 -3.48333333 -2.55000000 -0.43888889 -2.55000000 
-#> trtT3:day36 trtT2:day40 trtT3:day40 
-#> -1.89444444 -3.07222222 -6.53888889 
-#> 
-#> Correlation Structure: General
-#>  Formula: ~1 | plant 
-#>  Parameter estimate(s):
-#>  Correlation: 
-#>    1     2     3     4     5     6     7     8     9     10   
-#> 2  0.945                                                      
-#> 3  0.942 0.980                                                
-#> 4  0.938 0.962 0.986                                          
-#> 5  0.919 0.944 0.973 0.990                                    
-#> 6  0.901 0.922 0.951 0.974 0.994                              
-#> 7  0.883 0.900 0.929 0.956 0.971 0.979                        
-#> 8  0.803 0.803 0.845 0.879 0.895 0.909 0.955                  
-#> 9  0.706 0.701 0.741 0.777 0.799 0.821 0.882 0.954            
-#> 10 0.504 0.515 0.555 0.602 0.629 0.656 0.743 0.866 0.944      
-#> 11 0.537 0.551 0.587 0.633 0.657 0.683 0.763 0.864 0.929 0.963
-#> Degrees of freedom: 594 total; 561 residual
-#> Residual standard error: 5.564987 
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/perry.springwheat-1.png b/docs/reference/perry.springwheat-1.png deleted file mode 100644 index 8a08ff27..00000000 Binary files a/docs/reference/perry.springwheat-1.png and /dev/null differ diff --git a/docs/reference/perry.springwheat.html b/docs/reference/perry.springwheat.html deleted file mode 100644 index 6852d37d..00000000 --- a/docs/reference/perry.springwheat.html +++ /dev/null @@ -1,301 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat cultivars introduced 1860-1982.

    - - -
    - -
    -

    Yields of wheat cultivars introduced 1860-1982. Grown in 20 environments.

    -
    - - 
    data("perry.springwheat")
    - - -

    Format

    - -

    A data frame with 560 observations on the following 6 variables.

    -
    yield

    yield, kg/ha

    -
    gen

    genotype/cultivar factor, 28 levels

    -
    env

    environment factor, 20 levels

    -
    site

    site factor

    -
    year

    year, 1979-1982

    -
    yor

    year of release, 1860-1982

    - -
    - -

    Details

    - - - -

    Twenty-eight of the most significant wheat cultivars of the past - century in Western Australia, were grown in 20 field trials over 4 - years in the Central and Eastern wheat-belt of Australia.

    -

    At the Wongan Hills site there were separate early and late sown - trials in 1979 and 1980. Later sowing dates generally have lower - yields.

    -

    Note: Although not indicated by the original paper, it may be that the - Merredin site in 1979 also had early/late sowing dates.

    - -

    Used with permission of Mario D'Antuono and CSIRO Publishing.

    -

    Source

    - -

    MW Perry and MF D'Antuono. (1989). - Yield improvement and associated characteristics of some Australian - spring wheat cultivars introduced between 1860 and 1982. - Australian Journal of Agricultural Research, 40(3), 457--472. - https://www.publish.csiro.au/nid/43/issue/1237.htm

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(perry.springwheat)
-  dat <- perry.springwheat
-
-  libs(lattice)
-  xyplot(yield~yor|env, dat, type=c('p','r'), xlab="year of release",
-         main="perry.springwheat")
-
      
-  # Show a table of sites*year
-  # libs(latticeExtra)
-  # useOuterStrips(xyplot(yield~yor|site*factor(year), dat,
-  #                       type=c('p','r')))
-
-
-  # Perry reports a rate of gain of 5.8 kg/ha/year.  No model is given.
-  # We fit a model with separate intercept/slope for each env
-  m1 <- lm(yield ~ env + yor + env:yor, data=dat)
-  # Average slope across environments
-  mean(c(coef(m1)[21], coef(m1)[21]+coef(m1)[22:40]))
-
    #> [1] 5.496781
      ## [1] 5.496781
-
-
-  # ----------
-
-  # Now a mixed-effects model.  Fixed overall int/slope.  Random env int/slope.
-
-  # First, re-scale response so we don't have huge variances
-  dat$y <- dat$yield / 100
-
-  libs(lme4)
-  # Use || for uncorrelated int/slope.  Bad model.  See below.
-  # m2 <- lmer(y ~ 1 + yor + (1+yor||env), data=dat)
-  ## Warning messages:
-  ## 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
-  ##   Model failed to converge with max|grad| = 0.55842 (tol = 0.002, component 1)
-  ## 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
-  ##   Model is nearly unidentifiable: very large eigenvalue
-  ##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
-  ##  - Rescale variables?
-
-  # Looks like lme4 is having trouble with variance of intercepts
-  # There is nothing special about 1800 years, so change the
-  # intercept -- 'correct' yor by subtracting 1800 and try again.
-  dat$yorc <- dat$yor - 1800
-  m3 <- lmer(y ~ 1 + yorc + (1+yorc||env), data=dat)
-
    #> Warning: Model failed to converge with max|grad| = 1.01901 (tol = 0.002, component 1)
      
-  # Now lme4 succeeds.  Rate of gain is 100*0.0549 = 5.49
-  fixef(m3)
-
    #> (Intercept)        yorc 
-#>   5.8757200   0.0549384 
      ## (Intercept)        yorc
-  ##  5.87492444  0.05494464
-
-  # asreml3 & asreml4
-  libs(asreml,lucid)
-  m3a <- asreml(y ~ 1 + yorc, data=dat, random = ~ env + env:yorc)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:49:48 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -762.231       3.97642    544 17:49:48    0.0 (1 restrained)
-#>  2      -727.921       3.82476    544 17:49:48    0.0 (1 restrained)
-#>  3      -697.967       3.71607    544 17:49:48    0.0
-#>  4      -694.459       3.81272    544 17:49:48    0.0
-#>  5      -689.505       3.63995    544 17:49:48    0.0
-#>  6      -688.610       3.56952    544 17:49:48    0.0
-#>  7      -688.560       3.55226    544 17:49:48    0.0
-#>  8      -688.560       3.55102    544 17:49:48    0.0
      vc(m3)
-
    #>       grp        var1 var2      vcov   sdcor
-#>       env (Intercept) <NA> 15.09     3.885  
-#>     env.1        yorc <NA>  0.000627 0.02504
-#>  Residual        <NA> <NA>  3.525    1.877  
      ##      grp        var1 var2     vcov   sdcor
-  ##      env (Intercept) <NA> 11.61    3.407
-  ##    env.1        yorc <NA>  0.00063 0.02511
-  ## Residual        <NA> <NA>  3.551   1.884
-
-  vc(m3a)
-
    #>    effect component std.error z.ratio bound %ch
-#>       env  11.61     4.385        2.6     P   0
-#>  env:yorc   0.00063  0.000236     2.7     P   0
-#>   units!R   3.551    0.2231      16       P   0
      ##           effect component std.error z.ratio      con
-  ##      env!env.var  11.61     4.385        2.6 Positive
-  ## env:yorc!env.var   0.00063  0.000236     2.7 Positive
-  ##       R!variance   3.551    0.2231      16   Positive
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/petersen.sorghum.cowpea-1.png b/docs/reference/petersen.sorghum.cowpea-1.png deleted file mode 100644 index fc86bd39..00000000 Binary files a/docs/reference/petersen.sorghum.cowpea-1.png and /dev/null differ diff --git a/docs/reference/petersen.sorghum.cowpea.html b/docs/reference/petersen.sorghum.cowpea.html deleted file mode 100644 index 01ab6525..00000000 --- a/docs/reference/petersen.sorghum.cowpea.html +++ /dev/null @@ -1,158 +0,0 @@ - -Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea • agridat - - -
    -
    - - - -
    -
    -
    -

    Intercropping experiment of sorghum/cowpea

    - - -
    - -
    -

    Intercropping experiment of sorghum/cowpea.

    -
    - -
    - 
    data("petersen.sorghum.cowpea")
    -
    - -
    -

    Format

    -

    A data frame with 18 observations on the following 5 variables.

    block
    -

    block

    - -
    srows
    -

    sorghum rows

    - -
    crows
    -

    cowpea rows

    - -
    syield
    -

    sorghum yield, kg/ha

    - -
    cyield
    -

    cowpea yield, kg/ha

    - - -
    -
    -

    Details

    -

    An intercropping experiment in Tanzania. The treatments consisted of - four ratios of sorghum rows to cowpea rows as 1:4, 2:3, 3:2, 4:1.

    -

    The sole-crop yields with 5 rows per crop are also given (not part of - the blocks).

    -
    -
    -

    Source

    -

    Roger G Petersen (1994). - Agricultural Field Experiments. - Marcel Dekker Inc, New York. - Page 372.

    -
    -
    -

    References

    -

    None

    -
    - -
    -

    Examples

    - 
    libs(agridat)
-data(petersen.sorghum.cowpea)
-dat <- petersen.sorghum.cowpea
-
-# Petersen figure 10.4a
-tmp <- dat 
-
-with(tmp, plot(srows, syield + cyield,
-               col="blue", type='l', xlim=c(0,5), ylim=c(0,4000)) )
-with(tmp, lines(srows, syield) )
-with(tmp, lines(srows, cyield, col="red") )
-title("Cow Pea (red), Sorghum (black), Total (blue)")
-title("petersen.sorghum.cowpea", line=0.5)
-
-
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/piepho.barley.uniformity-1.png b/docs/reference/piepho.barley.uniformity-1.png deleted file mode 100644 index 43e04d85..00000000 Binary files a/docs/reference/piepho.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/piepho.barley.uniformity.html b/docs/reference/piepho.barley.uniformity.html deleted file mode 100644 index ab53d53c..00000000 --- a/docs/reference/piepho.barley.uniformity.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Uniformity trial of barley — piepho.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of barley

    - - -
    - -
    -

    Uniformity trial of barley in Germany

    -
    - - 
    data("piepho.barley.uniformity")
    - - -

    Format

    - -

    A data frame with 1080 observations on the following 5 variables.

    -
    row

    row ordinate

    -
    col

    column ordinate

    -
    yield

    yield per plot

    - -
    - -

    Details

    - -

    Uniformity trial of barley at Ihinger Hof farm, conducted by the - University of Hohenheim, Germany, in 2007.

    -

    Note: The paper by Piepho says "The trial had 30 rows and 36 - columns. Plot widths were 1.90 m along rows and 3.73 m along columns." - However, the SAS code supplement to the paper, called - "PBR_1654_sm_example1.sas", has row=1-36, col=1-30. We cannot - determine which dimension is "row" and which is "column", and - therefore cannot determine the actual dimensions of the field.

    -

    Source

    - -

    H. P. Piepho & E. R. Williams (2010). - Linear variance models for plant breeding trials. - Plant Breeding, 129, 1-8. - https://doi.org/10.1111/j.1439-0523.2009.01654.x

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  data(piepho.barley.uniformity) 
-  dat <- piepho.barley.uniformity
-  libs(desplot)
-  desplot(yield ~ col*row, dat,
-          tick=TRUE, # aspect unknown
-          main="piepho.barley.uniformity.csv")
-
    #> Please use desplot(data,form) instead of desplot(form,data)
    
-  libs(asreml,dplyr)
-  dat <- mutate(dat, x=factor(col), y=factor(row))
-  dat <- arrange(dat, x, y)
-  
-  # Piepho AR1xAR1 model (in random term, NOT residual)
-  m1 <- asreml(data=dat,
-               yield ~ 1, 
-               random = ~ x + y + ar1(x):ar1(y), 
-               residual = ~  units,
-               na.action=na.method(x="keep") )
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:49:49 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -2115.600       15.6955   1075 17:49:49    0.0 (3 restrained)
-#>  2     -2101.978       16.5258   1075 17:49:49    0.0 (3 restrained)
-#>  3     -2087.509       15.9315   1075 17:49:49    0.0 (3 restrained)
-#>  4     -2076.005       15.2437   1075 17:49:49    0.0 (3 restrained)
-#>  5     -2071.390       14.9905   1075 17:49:49    0.0 (3 restrained)
-#>  6     -2069.380       14.7343   1075 17:49:49    0.0 (2 restrained)
-#>  7     -2057.416       13.4665   1075 17:49:49    0.0 (1 restrained)
-#>  8     -2052.089       13.5044   1075 17:49:49    0.0 (1 restrained)
-#>  9     -2049.050       13.7143   1075 17:49:49    0.0 (1 restrained)
-#> 10     -2037.688       13.5521   1075 17:49:49    0.0
-#> 11     -2035.168       13.8721   1075 17:49:49    0.0
-#> 12     -2033.901       13.8242   1075 17:49:49    0.0
-#> 13     -2033.512       13.6703   1075 17:49:49    0.0
    #> Warning: Log-likelihood not converged
    #> Warning: Some components changed by more than 1% on the last iteration.
      m1 <- update(m1)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:49:49 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1     -2033.492       13.6383   1075 17:49:49    0.0
-#>  2     -2033.491       13.6335   1075 17:49:49    0.0
-#>  3     -2033.489       13.6212   1075 17:49:49    0.0
-#>  4     -2033.489       13.6163   1075 17:49:50    0.0
      # Match Piepho table 3, footnote 4: .9671, .9705 for col,row correlation
-  # Note these parameters are basically at the boundary of the parameter
-  # space. Questionable fit.
-  libs(lucid)
-  lucid::vc(m1) 
-
    #>     effect component std.error z.ratio bound %ch
-#>          x    0.7531   0.5642      1.3     P 0.1
-#>          y    1.2      0.7423      1.6     P 0.2
-#>        x:y   22.75    15.53        1.5     P 0.1
-#>  x:y!x!cor    0.9672   0.02536    38       U 0  
-#>  x:y!y!cor    0.9708   0.01645    59       U 0  
-#>    units!R   13.62     0.654      21       P 0  
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/piepho.cocksfoot-1.png b/docs/reference/piepho.cocksfoot-1.png deleted file mode 100644 index 5bc0944c..00000000 Binary files a/docs/reference/piepho.cocksfoot-1.png and /dev/null differ diff --git a/docs/reference/piepho.cocksfoot-2.png b/docs/reference/piepho.cocksfoot-2.png deleted file mode 100644 index f177c6bd..00000000 Binary files a/docs/reference/piepho.cocksfoot-2.png and /dev/null differ diff --git a/docs/reference/piepho.cocksfoot.html b/docs/reference/piepho.cocksfoot.html deleted file mode 100644 index d3b37f85..00000000 --- a/docs/reference/piepho.cocksfoot.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - -Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years

    - - -
    - -
    -

    Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 yearsyears

    -
    - - 
    data("piepho.cocksfoot")
    - - -

    Format

    - -

    A data frame with 111 observations on the following 3 variables.

    -
    gen

    genotype factor, 25 levels

    -
    year

    year, numeric

    -
    date

    heading date (days from April 1)

    - -
    - -

    Details

    - -

    These data are heading dates (days from April 1 to heading) of 25 cock's foot - Dactylis glomerata varieties in trials at Hannover, Germany, - repeated over seven years. Values are means over replications.

    -

    Piepho fits a model similar to Finlay-Wilkinson regression, but with - genotype and environment swapped.

    -

    Source

    - -

    Hans-Pieter Piepho. (1999). - Fitting a Regression Model for Genotype-by-Environment Data on Heading - Dates in Grasses by Methods for Nonlinear Mixed Models. - Biometrics, 55, 1120-1128. - https://doi.org/10.1111/j.0006-341X.1999.01120.x

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(piepho.cocksfoot)
-  dat <- piepho.cocksfoot
-  dat$year <- factor(dat$year)
-  
-  libs(lattice)
-  # Gaussian, not gamma distn
-  densityplot(~date|year, data=dat, main="piepho.cocksfoot - heading date")
-
      
-  libs(mumm) # The mumm package can reproduce Piepho's results
-
    #> 
-#> Attaching package: 'mumm'
    #> The following object is masked from 'package:asreml':
-#> 
-#>     lrt
    
-  levelplot(date ~ year*gen, dat)
-
      # note mp(random,fixed)
-  mod3 <- mumm(date ~ -1 + gen + (1|year) + mp(year, gen), dat)
-  
-  # Compare to Piepho table 3, "full maximum likelihood"
-  mod3$sigmas^2 # variances for year:gen, residual match
-
    #>        year mp year:gen    Residual 
-#> 17.70292945  0.02944156  0.49024751 
      #        year mp year:gen    Residual 
-  # 17.70287377  0.02944158  0.49024737
-  
-  # mod3$par_fix # fixed genotypes match
-  
-  # mod3$sdreport # estim/stderr
-  #               Estimate Std. Error
-  # nu          49.0393183 1.55038652
-  # nu          42.0889493 1.67597832
-  # nu          45.3411252 1.59818620
-  # etc
-  
-  # mod3$par_rand # random year:gen match
-  # $`mp year:gen`
-  #        1990        1991        1992        1993        1994        1995 
-  #  0.10595661 -0.05298523  0.08228274 -0.09629696 -0.11045540  0.29637268 
-  
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/polson.safflower.uniformity-1.png b/docs/reference/polson.safflower.uniformity-1.png deleted file mode 100644 index c40bae1a..00000000 Binary files a/docs/reference/polson.safflower.uniformity-1.png and /dev/null differ diff --git a/docs/reference/polson.safflower.uniformity-2.png b/docs/reference/polson.safflower.uniformity-2.png deleted file mode 100644 index 8f4a2e04..00000000 Binary files a/docs/reference/polson.safflower.uniformity-2.png and /dev/null differ diff --git a/docs/reference/polson.safflower.uniformity.html b/docs/reference/polson.safflower.uniformity.html deleted file mode 100644 index 0ac7447c..00000000 --- a/docs/reference/polson.safflower.uniformity.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - - -Uniformity trial of safflower — polson.safflower.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Uniformity trial of safflower

    - - -
    - -
    -

    Uniformity trial of safflower at Farmington, Utah, 1962.

    -
    - - 
    data("polson.safflower.uniformity")
    - - -

    Format

    - -

    A data frame with 1716 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield (grams)

    - -
    - -

    Details

    - -

    A uniformity trial of safflower at the Utah State University field - station in Farmington, Utah, in 1962. - The field was approximately 0.5 acres in size, 110 x 189 feet. - A four-row planter was used, 22 inches between rows. - Four rows on either side and 12 feet on both ends were removed before - harvesting.

    -

    Yield of threshed grain was recorded in grams.

    -

    Field width: (52 rows + 8 border rows) * 22 in = 110 ft

    -

    Field length: 33 sections * 5ft + 2 borders * 12 ft = 189 ft

    -

    Source

    - -

    David Polson. 1964. - Estimation of Optimum Size, Shape, and Replicate Number of Safflower Plots for Yield Trials. - Utah State University, All Graduate Theses and Dissertations, 2979. - Table 6. - https://digitalcommons.usu.edu/etd/2979

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(polson.safflower.uniformity)
-  dat <- polson.safflower.uniformity
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          flip=TRUE, aspect=189/110, # true aspect
-          main="polson.safflower.uniformity")
-
    
-  libs(agricolae)
-  libs(reshape2)
-  dmat <- acast(dat, row~col, value.var="yield")
-  # Similar to Polson fig 4.
-  tab <- index.smith(dmat, col="red",
-                     main="polson.safflower.uniformity - Smith Index",
-                     xlab="Plot size in number of basic plots")
-
      
-  # Polson p. 25 said CV decreased from 14.3 to 4.5
-  # for increase from 1 unit to 90 units.  Close match.
-  tab <- data.frame(tab$uniformity)
-
-  # Polson only uses log(Size) < 2 in his Fig 5, obtained slope -0.63
-  coef(lm(log(Vx) ~ log(Size), subset(tab, Size <= 6))) # -0.70
-
    #> (Intercept)   log(Size) 
-#>   7.7582342  -0.7051578 
    
-  # Polson table 2 reported labor for
-  # K1, number of plots, 133 hours 75
-  # K2, size of plot, 43.5 hours 24
-  # Optimum plot size
-  # X = b K1 / ((1-b) K2)
-  # Polson suggests optimum plot size 2.75 to 11 basic plots
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ratkowsky.onions-1.png b/docs/reference/ratkowsky.onions-1.png deleted file mode 100644 index 9f112d53..00000000 Binary files a/docs/reference/ratkowsky.onions-1.png and /dev/null differ diff --git a/docs/reference/ratkowsky.onions.html b/docs/reference/ratkowsky.onions.html deleted file mode 100644 index a95aefee..00000000 --- a/docs/reference/ratkowsky.onions.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - -Onion yields for different densities at two locations — ratkowsky.onions • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Onion yields for different densities at two locations

    - - -
    - -
    -

    Onion yields for different densities at two locations

    -
    - - - -

    Format

    - -

    This data frame contains the following columns:

    -
    density

    planting density (plants per square meter)

    -
    yield

    yield (g / plant)

    -
    loc

    location, Purnong Landing or Virginia

    - -
    - -

    Details

    - -

    Spanish white onions.

    -

    Source

    - -

    Ratkowsky, D. A. (1983). - Nonlinear Regression Modeling: A Unified Practical Approach. - New York: Marcel Dekker.

    -

    References

    - -

    Ruppert, D., Wand, M.P. and Carroll, R.J. (2003). - Semiparametric Regression. Cambridge University Press. - https://stat.tamu.edu/~carroll/semiregbook/

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(ratkowsky.onions)
-dat <- ratkowsky.onions
-
-# Model inverse yield as a quadratic.  Could be better...
-libs(lattice)
-dat <- transform(dat, iyield = 1/yield)
-m1 <- lm(iyield ~ I(density^2)*loc, dat)
-dat$pred <- predict(m1)
-
-libs(latticeExtra)
-foo <- xyplot(iyield ~ density, data=dat, group=loc, auto.key=TRUE,
-               main="ratkowski.onions",ylab="Inverse yield")
-foo + xyplot(pred ~ density, data=dat, group=loc, type='l')
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/reid.grasses-1.png b/docs/reference/reid.grasses-1.png deleted file mode 100644 index 6b4efa24..00000000 Binary files a/docs/reference/reid.grasses-1.png and /dev/null differ diff --git a/docs/reference/reid.grasses-2.png b/docs/reference/reid.grasses-2.png deleted file mode 100644 index e50f9b8c..00000000 Binary files a/docs/reference/reid.grasses-2.png and /dev/null differ diff --git a/docs/reference/reid.grasses-3.png b/docs/reference/reid.grasses-3.png deleted file mode 100644 index 6b6ad5eb..00000000 Binary files a/docs/reference/reid.grasses-3.png and /dev/null differ diff --git a/docs/reference/reid.grasses.html b/docs/reference/reid.grasses.html deleted file mode 100644 index 115a0202..00000000 --- a/docs/reference/reid.grasses.html +++ /dev/null @@ -1,257 +0,0 @@ - - - - - - - - -Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Yields of four grasses for a wide range of nitrogen fertilizer

    - - -
    - -
    -

    Yields of four grasses for a wide range of nitrogen fertilizer, - conducted over 3 years.

    -
    - - 
    data("reid.grasses")
    - - -

    Format

    - -

    A data frame with 210 observations on the following 5 variables.

    -
    nitro

    nitrogen, 21 numeric levels

    -
    year

    Y1, Y2, or Y3

    -
    gen

    genotype

    -
    drymatter

    dry matter content

    -
    protein

    protein content

    - -
    - -

    Details

    - -

    Experiment at the Hannah Research Institute, Ayr.

    -

    Single plots were planted to 4 different kinds of grasses. Within - each plot, 21 nitrogen treatments were randomized.

    -

    Reid modeled the dry matter yield with four-parameter logistic curves - of the form y = a - b exp(-cx^d).

    -

    Source

    - -

    D. Reid (1985). - A comparison of the yield responses of four grasses to a wide range of nitrogen application rates. - J. Agric. Sci., 105, 381-387. Table 1 & 3. - https://doi.org/10.1017/S0021859600056434

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(reid.grasses)
-  dat <- reid.grasses
-
-  libs(latticeExtra)
-  foo <- xyplot(drymatter + protein ~ nitro|year, dat, group=gen,
-                auto.key=list(columns=4),
-                as.table=TRUE, type=c('p','l'),
-                main="reid.grasses",ylab="drymatter/protein trait value",
-                scales=list(y=list(relation="free")))
-  combineLimits(foo)
-
    
-  # devtools::run_examples does NOT like groupedData
-  if (0){
-    libs(nlme)
-    dat2 <- dat
-    dat2$indiv <- paste(dat$year, dat$gen) # individual year+genotype curves
-    
-    # use all data to get initial values
-    inits <- getInitial(drymatter ~ SSfpl(nitro, A, B, xmid, scal), data = dat2)
-    inits
-    ##         A          B       xmid       scal 
-    ## -4.167902  12.139796  68.764796 128.313106 
-    xvals <- 0:800
-    y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal))
-    plot(drymatter ~ nitro, dat2)
-    lines(xvals,y1)
-    
-    # must have groupedData object to use augPred
-    dat2 <- groupedData(drymatter ~ nitro|indiv, data=dat2)
-    plot(dat2)
-    
-    # without 'random', all effects are included in 'random'
-    m1 <- nlme(drymatter ~ SSfpl(nitro, A, B, xmid,scale),
-               data= dat2,
-               fixed= A + B + xmid + scale ~ 1,
-               random = A + B + xmid + scale ~ 1|indiv,
-               start=inits)
-    fixef(m1)
-    summary(m1)
-    plot(augPred(m1, level=0:1),
-         main="reid.grasses - observed/predicted data") # only works with groupedData object
-  } # if(0)  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ridout.appleshoots-1.png b/docs/reference/ridout.appleshoots-1.png deleted file mode 100644 index 3d7de887..00000000 Binary files a/docs/reference/ridout.appleshoots-1.png and /dev/null differ diff --git a/docs/reference/ridout.appleshoots-2.png b/docs/reference/ridout.appleshoots-2.png deleted file mode 100644 index 4371155e..00000000 Binary files a/docs/reference/ridout.appleshoots-2.png and /dev/null differ diff --git a/docs/reference/ridout.appleshoots-3.png b/docs/reference/ridout.appleshoots-3.png deleted file mode 100644 index cea94541..00000000 Binary files a/docs/reference/ridout.appleshoots-3.png and /dev/null differ diff --git a/docs/reference/ridout.appleshoots.html b/docs/reference/ridout.appleshoots.html deleted file mode 100644 index 32b690ad..00000000 --- a/docs/reference/ridout.appleshoots.html +++ /dev/null @@ -1,352 +0,0 @@ - - - - - - - - -Root counts for propagated columnar apple shoots. — ridout.appleshoots • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Root counts for propagated columnar apple shoots.

    - - -
    - -
    - -

    Root counts for propagated columnar apple shoots.

    - -
    - - 
    data("ridout.appleshoots")
    - -

    Format

    - -

    A data frame with 270 observations on the following 4 variables.

    -
    roots

    number of roots per shoot

    -
    trtn

    number of shoots per treatment combination

    -
    photo

    photoperiod, 8 or 16

    -
    bap

    BAP concentration, numeric

    -
    - - -

    Details

    - - - -

    There were 270 micropropagated shoots from the columnar apple cultivar - Trajan. During the rooting period, shoot tips of length 1.0-1.5 cm were - cultured on media with different concentrations of the cytokinin BAP in - two growth chambers with 8 or 16 hour photoperiod.

    -

    The response variable is the number of roots after 4 weeks at 22 degrees C.

    -

    Almost all of the shoots in the 8 hour photoperiod rooted. Under the 16 - hour photoperiod only about half rooted.

    -

    High BAP concentrations often inhibit root formation of apples, but - perhaps not for columnar varieties.

    - -

    Source

    - -

    Ridout, M. S., Hinde, J. P., and Demetrio, C. G. B. (1998). - Models for Count Data with Many Zeros. - Proceedings of the 19th International Biometric Conference, - 179-192.

    -

    Used with permission of Martin Ridout.

    - -

    References

    - -

    SAS. Fitting Zero-Inflated Count Data Models by Using PROC GENMOD. - support.sas.com/rnd/app/examples/stat/GENMODZIP/roots.pdf

    - - -

    Examples

    - 
    
-library(agridat)
-data(ridout.appleshoots)
-dat <- ridout.appleshoots
-
-# Change photo and bap to factors
-dat <- transform(dat, photo=factor(photo), bap=factor(bap))
-
-libs(lattice)
-# histogram(~roots, dat, breaks=0:18-0.5)
-
-# For photo=8, Poisson distribution looks reasonable.
-# For photo=16, half of the shoots had no roots
-# Also, photo=8 has very roughly 1/45 as many zeros as photo=8,
-# so we anticipate prob(zero) is about 1/45=0.22 for photo=8.
-histogram(~roots|photo, dat, breaks=0:18-0.5, main="ridout.appleshoots")
    
-  libs(latticeExtra)
-  foo.obs <- histogram(~roots|photo*bap, dat, breaks=0:18-0.5, type="density",
-                       xlab="Number of roots for photoperiod 8, 16",
-                       ylab="Density for BAP levels",
-                       main="ridout.appleshoots")
-  useOuterStrips(foo.obs)
    
-  # Ordinary (non-ZIP) Poisson GLM
-  m1 <- glm(roots ~ bap + photo + bap:photo, data=dat,
-            family="poisson")
-  summary(m1) # Appears to have overdispersion
    #> 
-#> Call:
-#> glm(formula = roots ~ bap + photo + bap:photo, family = "poisson", 
-#>     data = dat)
-#> 
-#> Deviance Residuals: 
-#>     Min       1Q   Median       3Q      Max  
-#> -3.7815  -2.2136  -0.2798   0.8681   4.1197  
-#> 
-#> Coefficients:
-#>                 Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)      1.76359    0.07559  23.330  < 2e-16 ***
-#> bap4.4           0.28625    0.10003   2.862  0.00421 ** 
-#> bap8.8           0.25131    0.09512   2.642  0.00824 ** 
-#> bap17.6          0.20352    0.09597   2.121  0.03395 *  
-#> photo16         -0.57982    0.12617  -4.596 4.31e-06 ***
-#> bap4.4:photo16  -0.46450    0.18001  -2.580  0.00987 ** 
-#> bap8.8:photo16  -0.29299    0.17289  -1.695  0.09014 .  
-#> bap17.6:photo16 -0.49121    0.17210  -2.854  0.00432 ** 
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for poisson family taken to be 1)
-#> 
-#>     Null deviance: 1077.75  on 269  degrees of freedom
-#> Residual deviance:  813.04  on 262  degrees of freedom
-#> AIC: 1572.9
-#> 
-#> Number of Fisher Scoring iterations: 6
-#> 
    
-
-# ----- Fit a Zero-Inflated Poisson model -----
-
-libs(pscl)
    #> Classes and Methods for R developed in the
-#> Political Science Computational Laboratory
-#> Department of Political Science
-#> Stanford University
-#> Simon Jackman
-#> hurdle and zeroinfl functions by Achim Zeileis
    
-# Use SAS contrasts to match SAS output
-oo <- options(contrasts=c('contr.SAS','contr.poly'))
-
-# There are unequal counts for each trt combination, which obviously affects
-# the distribution of counts, so use log(trtn) as an offset.
-dat$ltrtn <- log(dat$trtn)
-
-# Ordinary Poisson GLM: 1 + bap*photo.
-# Zero inflated probability depends only on photoperiod: 1 + photo
-
-m2 <- zeroinfl(roots ~ 1 + bap*photo | 1 + photo, data=dat,
-          dist="poisson", offset=ltrtn)
-logLik(m2)      # -622.2283 matches SAS Output 1
    #> 'log Lik.' -622.2283 (df=10)
    -2 * logLik(m2) # 1244.457 Matches Ridout Table 2, ZIP, H*P, P
    #> 'log Lik.' 1244.457 (df=10)
    summary(m2)     # Coefficients match SAS Output 3.
    #> 
-#> Call:
-#> zeroinfl(formula = roots ~ 1 + bap * photo | 1 + photo, data = dat, offset = ltrtn, 
-#>     dist = "poisson")
-#> 
-#> Pearson residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -2.5913 -0.8843 -0.2266  0.7357  4.5116 
-#> 
-#> Count model coefficients (poisson with log link):
-#>               Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)    -2.1581     0.1033 -20.884  < 2e-16 ***
-#> bap2.2          0.6322     0.1449   4.364 1.28e-05 ***
-#> bap4.4          0.5209     0.1521   3.425 0.000616 ***
-#> bap8.8          0.4058     0.1468   2.765 0.005691 ** 
-#> photo8          0.4857     0.1193   4.072 4.67e-05 ***
-#> bap2.2:photo8  -0.5974     0.1739  -3.434 0.000594 ***
-#> bap4.4:photo8  -0.1998     0.1760  -1.135 0.256244    
-#> bap8.8:photo8  -0.4074     0.1686  -2.416 0.015686 *  
-#> 
-#> Zero-inflation model coefficients (binomial with logit link):
-#>             Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)  -0.1033     0.1766  -0.585    0.559    
-#> photo8       -4.1698     0.7611  -5.478 4.29e-08 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
-#> 
-#> Number of iterations in BFGS optimization: 18 
-#> Log-likelihood: -622.2 on 10 Df
    
-exp(coef(m2, "zero")) # Photo=8 has .015 times as many zeros as photo=16
    #> (Intercept)      photo8 
-#>  0.90186501  0.01545465 
    
-# Get predicted _probabilities_
-
-# Prediction data
-newdat <- expand.grid(photo=c(8,16), bap=c(2.2, 4.4, 8.8, 17.6))
-newdat <- aggregate(trtn~bap+photo, dat, FUN=mean)
-newdat$ltrtn <- log(newdat$trtn)
-
-# The predicted (Poisson + Zero) probabilities
-d2 <- cbind(newdat[,c('bap','photo')], predict(m2, newdata=newdat, type="prob"))
-libs(reshape2)
-d2 <- melt(d2, id.var = c('bap','photo')) # wide to tall
-d2$xpos <- as.numeric(as.character(d2$variable))
-foo.poi <- xyplot(value~xpos|photo*bap, d2, col="black", pch=20, cex=1.5)
-
-# Plot data and model
-foo.obs <- update(foo.obs, main="ridout.appleshoots: observed (bars) & predicted (dots)")
-useOuterStrips(foo.obs + foo.poi)
    
-# Restore contrasts
-options(oo)
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/robinson.peanut.uniformity-1.png b/docs/reference/robinson.peanut.uniformity-1.png deleted file mode 100644 index 59e5b63f..00000000 Binary files a/docs/reference/robinson.peanut.uniformity-1.png and /dev/null differ diff --git a/docs/reference/robinson.peanut.uniformity.html b/docs/reference/robinson.peanut.uniformity.html deleted file mode 100644 index c1a08639..00000000 --- a/docs/reference/robinson.peanut.uniformity.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - - -Uniformity trial of peanuts — robinson.peanut.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of peanuts

    - - -
    - -
    -

    Uniformity trial of peanuts in North Carolina in 1939, 1940.

    -
    - - 
    data("robinson.peanut.uniformity")
    - - -

    Format

    - -

    A data frame with 1152 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield in grams/plot

    -
    year

    year

    - -
    - -

    Details

    - -

    Two crops of peanuts were grown in North Carolina in 1939 and 1940. - A different field was used each year.

    -

    A block of 36 rows 3 feet wide and 200 feet long were harvested in 12.5 foot lengths.

    -

    Field length: 36 plots * 12.5 feet = 200 feet

    -

    Field width: 16 plots * 3 feet = 48 feet

    -

    Widening the plot was not as effective as increasing the plot length - in order to reduce error. This agrees with the results of other - uniformity studies.

    -

    Assuming 30 percent of the total cost of an experiment is proportional - to the size of the plots used, the optimum plot size is approximately - 3.2 units.

    -

    Source

    - -

    H.F. Robinson and J.A.Rigney and P.H.Harvey (1948). - Investigations In Peanut Plot Technique With Peanuts. - Univ California Tech. Bul. No 86.

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(robinson.peanut.uniformity)
-  dat <- robinson.peanut.uniformity
-
-  # Mean yield per year. Robinson has 703.9, 787.3
-  # tapply(dat$yield, dat$year, mean)
-  #     1939     1940 
-  # 703.7847 787.8125 
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row|year,
-          flip=TRUE, tick=TRUE, aspect=200/48,
-          main="robinson.peanut.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/rothamsted.brussels-1.png b/docs/reference/rothamsted.brussels-1.png deleted file mode 100644 index fc27b605..00000000 Binary files a/docs/reference/rothamsted.brussels-1.png and /dev/null differ diff --git a/docs/reference/rothamsted.brussels-2.png b/docs/reference/rothamsted.brussels-2.png deleted file mode 100644 index e543d12f..00000000 Binary files a/docs/reference/rothamsted.brussels-2.png and /dev/null differ diff --git a/docs/reference/rothamsted.brussels.html b/docs/reference/rothamsted.brussels.html deleted file mode 100644 index 97d9b5b5..00000000 --- a/docs/reference/rothamsted.brussels.html +++ /dev/null @@ -1,223 +0,0 @@ - - - - - - - - -RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of brussels sprouts, 9 fertilizer treatments

    - - -
    - -
    -

    RCB experiment of brussels sprouts, 9 fertilizer treatments

    -
    - - - -

    Format

    - -

    A data frame with 48 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield of saleable sprouts, pounds

    -
    trt

    treatment, 9 levels

    -
    block

    block, 4 levels

    - -
    - -

    Details

    - -

    The block numbers are arbitrary, and may not match the orignal source.

    -

    Plots were 10 yards x 14 yards. Plot orientation is not clear.

    -

    Source

    - -

    Rothamsted Experimental Station Report 1934-36. - Brussels sprouts: effect of sulphate of ammonia, poultry manure, soot - and rape dust, pp. 191-192. Harpenden: Lawes Agricultural Trust.

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(rothamsted.brussels)
-dat <- rothamsted.brussels
-
-libs(lattice)
-bwplot(yield~trt, dat, main="rothamsted.brussels")
-
    
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          num=trt, out1=block, cex=1, # aspect unknown
-          main="rothamsted.brussels")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/rothamsted.oats-1.png b/docs/reference/rothamsted.oats-1.png deleted file mode 100644 index cc4b16a5..00000000 Binary files a/docs/reference/rothamsted.oats-1.png and /dev/null differ diff --git a/docs/reference/rothamsted.oats-2.png b/docs/reference/rothamsted.oats-2.png deleted file mode 100644 index a73a80f6..00000000 Binary files a/docs/reference/rothamsted.oats-2.png and /dev/null differ diff --git a/docs/reference/rothamsted.oats-3.png b/docs/reference/rothamsted.oats-3.png deleted file mode 100644 index 2e2e2bf1..00000000 Binary files a/docs/reference/rothamsted.oats-3.png and /dev/null differ diff --git a/docs/reference/rothamsted.oats.html b/docs/reference/rothamsted.oats.html deleted file mode 100644 index 6325d326..00000000 --- a/docs/reference/rothamsted.oats.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - - -RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    RCB experiment of oats, straw and grain, 9 fertilizer treatments

    - - -
    - -
    -

    RCB experiment of oats, straw and grain, 9 fertilizer treatments

    -
    - - 
    data("rothamsted.oats")
    - - -

    Format

    - -

    A data frame with 96 observations on the following 6 variables.

    -
    block

    block

    -
    trt

    fertilizer treatment with 9 levels

    -
    grain

    grain, pounds per plot

    -
    straw

    straw, pounds per plot

    -
    row

    row

    -
    col

    column

    - -
    - -

    Details

    - -

    Oats (Grey Winter) grown at Rothamsted, Long Hoos field 1926.

    -

    Values of grain and straw are actual weights in pounds. Each plot was 1/40 acre. - The plot dimensions are not given, but the Rothamsted report shows the field being square.

    -

    The treatment codes are: - OA,OB,OC,OD = No top dressing. - E/L = Early/late application. - S/M = Sulphate or muriate of ammonia. - 1/2 = Single or double dressing.

    -

    Source

    - -

    Rothamsted Report 1925-26, p. 146. - https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1925-26-138-155 - Electronic version of data supplied by David Clifford.

    -

    References

    - -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(rothamsted.oats)
-  dat <- rothamsted.oats
-
-  
-  libs(desplot)
-  desplot(dat, grain~col*row,
-          out1=block, text=trt, cex=1, shorten=FALSE,
-          aspect=1,
-          main="rothamsted.oats")
-
      desplot(dat, straw~col*row,
-          out1=block, text=trt, cex=1, shorten=FALSE,
-          aspect=1,
-          main="rothamsted.oats")
-
    
-  
-  libs(lattice)
-  xyplot(grain~straw, dat,
-         main="rothamsted.oats") # traits are correlated
-
    
-
-  if(0){
-    # compare to summary at bottom of page 146, first 3 columns
-    libs(dplyr)
-    dat = mutate(dat,
-                 nfert=trt, # number of fertilizer applications
-                 nfert=dplyr::recode(nfert,
-                                     "oa"="None", "ob"="None",
-                                     "oc"="None", "od"="None",
-                                     "1se"="Single", "1sl"="Single",
-                                     "1me"="Single", "1ml"="Single",
-                                     "2se"="Double", "2sl"="Double",
-                                     "2me"="Double", "2ml"="Double"))
-    # English ton = 2240 pounds, cwt = 112 pounds
-    # multiply by 40 to get pounds/acre
-    # divide by: 112 to get hundredweight/acre, 42 to get bushels/acre
-    # Avoid pipe operator in Rd examples!
-    dat <- group_by(dat, nfert)
-    dat <- summarize(dat, straw=mean(straw), grain=mean(grain))
-    dat <- mutate(dat, straw= straw * 40/112, grain = grain * 40/42)
-    ## # A tibble: 3 x 3
-    ##   nfert  straw grain
-    ##   <fct>  <dbl> <dbl>
-    ## 1 Single  50.3  78.9
-    ## 2 Double  53.7  77.7
-    ## 3 None    44.1  75.4
-  }
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/ryder.groundnut-1.png b/docs/reference/ryder.groundnut-1.png deleted file mode 100644 index a5175345..00000000 Binary files a/docs/reference/ryder.groundnut-1.png and /dev/null differ diff --git a/docs/reference/ryder.groundnut-2.png b/docs/reference/ryder.groundnut-2.png deleted file mode 100644 index b159c6bc..00000000 Binary files a/docs/reference/ryder.groundnut-2.png and /dev/null differ diff --git a/docs/reference/ryder.groundnut.html b/docs/reference/ryder.groundnut.html deleted file mode 100644 index 7a165202..00000000 --- a/docs/reference/ryder.groundnut.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -RCB experiment of groundut, wet and dry yields — ryder.groundnut • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    -

    RCB experiment of groundut, wet and dry yields

    - - -
    - -
    -

    RCB experiment of groundut, wet and dry yields

    -
    - - - -

    Format

    - -

    A data frame with 24 observations on the following 6 variables.

    -
    block

    block

    -
    row

    row

    -
    col

    column

    -
    gen

    genotype factor

    -
    wet

    wet yield, kg/plot

    -
    dry

    dry yield, kg/plot

    - -
    - -

    Details

    - -

    Ryder (1981) uses this data to discuss the importance of looking at - the field plan for an experiment. Based on analysis of the residuals, - he suggests that varieties A and B in block 3 may have had their data - swapped.

    -

    Source

    - -

    K. Ryder (1981). Field plans: why the biometrician finds them useful, - Experimental Agriculture, 17, 243--256.

    -

    https://doi.org/10.1017/S0014479700011601

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(ryder.groundnut)
-  dat <- ryder.groundnut
-
-  # RCB model
-  m1 <- lm(dry~block+gen,dat)
-  dat$res1 <- resid(m1)
-
-  # Table 3 of Ryder.  Scale up from kg/plot to kg/ha
-  round(dat$res1 * 596.6,0)
-
    #>  [1] -137  206  -92  -77  132  -32 -191  181 -355   32  167  167 -534  -12 -669
-#> [16]  778  152  286 -316 -346  -62  490  296  -62
      
-  # Visually.  Note largest positive/negative residuals are adjacent
-  libs(desplot)
-  desplot(dat, res1 ~ col + row,
-          text=gen, # aspect unknown
-          main="ryder.groundnut - residuals")
-
      
-  
-  libs(desplot)
-  # Swap the dry yields for two plots and re-analyze
-  dat[dat$block=="B3" & dat$gen=="A", "dry"] <- 2.8
-  dat[dat$block=="B3" & dat$gen=="B", "dry"] <- 1.4
-  m2 <- lm(dry~block+gen, dat)
-  dat$res2 <- resid(m2)
-  desplot(dat, res2 ~ col+row,
-          # aspect unknown
-          text=gen, main="ryder.groundnut")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/salmon.bunt-1.png b/docs/reference/salmon.bunt-1.png deleted file mode 100644 index e049c7a4..00000000 Binary files a/docs/reference/salmon.bunt-1.png and /dev/null differ diff --git a/docs/reference/salmon.bunt-2.png b/docs/reference/salmon.bunt-2.png deleted file mode 100644 index 52340c32..00000000 Binary files a/docs/reference/salmon.bunt-2.png and /dev/null differ diff --git a/docs/reference/salmon.bunt.html b/docs/reference/salmon.bunt.html deleted file mode 100644 index e4a1d830..00000000 --- a/docs/reference/salmon.bunt.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Fungus infection in varieties of wheat — salmon.bunt • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Fungus infection in varieties of wheat

    - - -
    - -
    -

    Fungus infection in varieties of wheat

    -
    - - - -

    Format

    - -

    A data frame with 400 observations on the following 4 variables.

    -
    bunt

    bunt factor, 20 levels

    -
    pct

    percent infected

    -
    rep

    rep factor, 2 levels

    -
    gen

    genotype factor, 10 levels

    - -
    - -

    Details

    - -

    Note: Salmon (1938) gives results for all 69 types of bunt, not just - the 20 shown in the paper.

    -

    H. A. Rodenhiser and C. S. Holton (1937) say that races from two - different species of bunt were used, Tilletia tritici and T. levis.

    -

    This data gives the results with 20 types of bunt (fungus) for winter - wheat varieties at Kearneysville, W. Va., in 1935. Altogether there - were 69 types of bunt included in the experiment, of which the 20 in - this data are representative. Each type of wheat was grown in a short - row (5 to 8 feet), the seed of which had been innoculated with the - spores of bunt. The entire seeding was then repeated in the same - order.

    -

    Infection was recorded as a percentage of the total number of heads - counted at or near harvest. The number counted was seldom less than 200 - and sometimes more than 400 per row.

    -

    Source

    - -

    S.C. Salmon, 1938. - Generalized standard errors for evaluating bunt experiments with wheat. - Agronomy Journal, 30, 647--663. Table 1. - https://doi.org/10.2134/agronj1938.00021962003000080003x

    -

    References

    - -

    Salmon says the data came from:

    -

    H. A. Rodenhiser and C. S. Holton (1937). - Physiologic races of Tilletia tritici and T. levis. - Journal of Agricultural Research, 55, 483-496. - naldc.nal.usda.gov/download/IND43969050/PDF

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(salmon.bunt)
-dat <- salmon.bunt
-
-d2 <- aggregate(pct~bunt+gen, dat, FUN=mean) # average reps
-d2$gen <- reorder(d2$gen, d2$pct)
-d2$bunt <- reorder(d2$bunt, d2$pct)
-# Some wheat varieties (Hohenheimer) are resistant to all bunts, and some (Hybrid128)
-# are susceptible to all bunts.  Note the groups of bunt races that are similar,
-# such as the first 4 rows of this plot.  Also note the strong wheat*bunt interaction.
-libs(lattice)
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-levelplot(pct~gen+bunt,d2, col.regions=redblue,
-          main="salmon.bunt percent of heads infected",
-          xlab="Wheat variety", ylab="bunt line")
-
    
-  # We don't have individual counts, so use beta regression
-  libs(betareg)
-  dat$y <- dat$pct/100 + .001 # Beta regression does not allow 0
-  dat$gen <- reorder(dat$gen, dat$pct) # For a prettier dot plot
-  
-  m1 <- betareg(y ~ gen + bunt + gen:bunt, data=dat)
-  
-  # Construct 95 percent confidence intervals
-  p1 <- cbind(dat,
-              lo = predict(m1, type='quantile', at=.025),
-              est = predict(m1, type='quantile', at=.5),
-              up = predict(m1, type='quantile', at=.975))
-  p1 <- subset(p1, rep=="R1")
-  
-  # Plot the model intervals over the original data
-  libs(latticeExtra)
-  dotplot(bunt~y|gen, data=dat, pch='x', col='red',
-          main="Observed data and 95 pct intervals for bunt infection") +
-            segplot(bunt~lo+up|gen, data=p1, centers=est, draw.bands=FALSE)
-
    
-
-  # To evaluate wheat, we probably want to include bunt as a random effect...
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/sawyer.multi.uniformity-1.png b/docs/reference/sawyer.multi.uniformity-1.png deleted file mode 100644 index 9da0672a..00000000 Binary files a/docs/reference/sawyer.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/sawyer.multi.uniformity-2.png b/docs/reference/sawyer.multi.uniformity-2.png deleted file mode 100644 index 177b903c..00000000 Binary files a/docs/reference/sawyer.multi.uniformity-2.png and /dev/null differ diff --git a/docs/reference/sawyer.multi.uniformity-3.png b/docs/reference/sawyer.multi.uniformity-3.png deleted file mode 100644 index 169f5245..00000000 Binary files a/docs/reference/sawyer.multi.uniformity-3.png and /dev/null differ diff --git a/docs/reference/sawyer.multi.uniformity-4.png b/docs/reference/sawyer.multi.uniformity-4.png deleted file mode 100644 index 67f812a6..00000000 Binary files a/docs/reference/sawyer.multi.uniformity-4.png and /dev/null differ diff --git a/docs/reference/sawyer.multi.uniformity.html b/docs/reference/sawyer.multi.uniformity.html deleted file mode 100644 index b1437f7c..00000000 --- a/docs/reference/sawyer.multi.uniformity.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - -Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trials of wheat, swedes, oats, 3 years on the same land

    - - -
    - -
    -

    Uniformity trials of wheat, swedes, oats at Rothamsted, England, 1925-1927.

    -
    - - 
    data("sawyer.multi.uniformity")
    - - -

    Format

    - -

    A data frame with 48 observations on the following 7 variables.

    -
    year

    year

    -
    crop

    crop

    -
    row

    row

    -
    col

    column

    -
    grain

    wheat/oats grain weight, pounds

    -
    straw

    wheat/oats straw weight, pounds

    -
    leafwt

    swedes leaf weight, pounds

    -
    rootwt

    swedes root weight, pounds

    -
    rootct

    swedes root count

    - -
    - -

    Details

    - - - -

    An experiment conducted at Rothamsted, England, in 1925-1927, in Sawyers Field.

    -

    Row 6, column 1 was not planted in any year.

    -

    Wheat was harvested in 1925. - Row 1, column 1 had partially missing data for the wheat values in - 1925 and was not used in the Rothamsted summary statistics on page 155.

    -

    Swedes were harvested in 1926.

    -

    Oats were harvested in 1927.

    -

    Note the summaries statistics at the bottom of the page in each report - are calibrated to ACRES.

    -

    Field width: 8 plots * 22 feet = 528 feet

    -

    Field length: 6 plots * 22 feet = 396 feet

    -

    The field is 8 plots wide, 6 plots long. The plots are drawn in the - source documents as squares .098 acres each (1 chain = 66 feet on each - side).

    -

    Eden & Maskell (page 165) say the field was clover, and ploughed in - the autumn of 1924. The field was laid out uniformly in lands of one - chain width and each plot width made to coincide with the land width - from ridge to ridge. The length of each plot was also one chain and - from the point of view of yield data the trial comprised 47 plots in - 8x6 except that the run of the hedge only allowed a rank of five plots - at one of the ends.

    -

    Source

    - -

    Rothamsted Experimental Station, Report 1925-26. - Lawes Agricultural Trust, p. 154-155. - https://www.era.rothamsted.ac.uk/eradoc/book/84

    -

    Rothamsted Experimental Station, Report 1927-1928. - Lawes Agricultural Trust, p. 153. - https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1927-28-131-175

    -

    References

    - -

    Eden, T. and E. J. Maskell. (1928). - The influence of soil heterogeneity on the growth and yield of - successive crops. - Jour of Agricultural Science, 18, 163-185. - https://archive.org/stream/in.ernet.dli.2015.25895/2015.25895.Journal-Of-Agricultural-Science-Vol-xviii-1928#page/n175

    -

    McCullagh, P. and Clifford, D., (2006). - Evidence for conformal invariance of crop yields, - Proceedings of the Royal Society A: Mathematical, Physical and - Engineering Science, 462, 2119--2143. - https://doi.org/10.1098/rspa.2006.1667

    -

    Winifred A. Mackenzie. (1926) - Note on a remarkable correlation between grain and straw, obtained at - Rothamsted. - Journal of Agricultural Science, 16, 275-279. - https://doi.org/10.1017/S0021859600018256

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data("sawyer.multi.uniformity")
-  dat <- sawyer.multi.uniformity
-  
-  libs(desplot)
-  # The field plan shows square plots
-  desplot(dat, grain~col*row,
-          subset= year==1925,
-          main="sawyer.multi.uniformity - 1925 wheat grain yield",
-          aspect=(6)/(8)) # true aspect
-
      
-  desplot(dat, rootwt~col*row,
-          subset= year==1926,
-          main="sawyer.multi.uniformity - 1926 root weight of swedes",
-          aspect=(6)/(8))
-
    
-  desplot(dat, grain~col*row, subset= year==1927,
-          main="sawyer.multi.uniformity - 1927 oats grain yield",
-          aspect=(6)/(8))
-
    
-
-  # This plot shows the "outlier" in the wheat data reported by Mackenzie.
-  libs(lattice)
-  xyplot(grain ~ straw, data=subset(dat, year==1925))
-
      
-  round(cor(dat[,7:9], use="pair"),2) # Matches McCullagh p 2121
-
    #>        leafwt rootwt rootct
-#> leafwt   1.00   0.66   0.47
-#> rootwt   0.66   1.00   0.43
-#> rootct   0.47   0.43   1.00
      ##        leafwt rootwt rootct
-  ## leafwt   1.00   0.66   0.47
-  ## rootwt   0.66   1.00   0.43
-  ## rootct   0.47   0.43   1.00
-  
-  ## pairs(dat[,7:9],
-  ##       main="sawyer.multi.uniformity")
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/sayer.sugarcane.uniformity-1.png b/docs/reference/sayer.sugarcane.uniformity-1.png deleted file mode 100644 index 546805e4..00000000 Binary files a/docs/reference/sayer.sugarcane.uniformity-1.png and /dev/null differ diff --git a/docs/reference/sayer.sugarcane.uniformity-2.png b/docs/reference/sayer.sugarcane.uniformity-2.png deleted file mode 100644 index e2adca31..00000000 Binary files a/docs/reference/sayer.sugarcane.uniformity-2.png and /dev/null differ diff --git a/docs/reference/sayer.sugarcane.uniformity.html b/docs/reference/sayer.sugarcane.uniformity.html deleted file mode 100644 index b409ff3a..00000000 --- a/docs/reference/sayer.sugarcane.uniformity.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - - - -Uniformity trial of sugarcane in India, 1933 & 1934. — sayer.sugarcane.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of sugarcane in India, 1933 & 1934.

    - - -
    - -
    -

    Uniformity trial of sugarcane in India, 1933 & 1934.

    -
    - - 
    data("sayer.sugarcane.uniformity")
    - - -

    Format

    - -

    A data frame with 2056 observations on the following 4 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds/plot

    -
    year

    year

    - -
    - -

    Details

    - - - -

    1933 Experiment

    -

    First experiment was conducted in 1933 at Meghaul (Monghyr). -A road was cut through the field, creating blocks 480 ft x 315 ft and 480 ft x 93 ft. (See Plate XLI). -There were 136 rows, 3 feet apart, 480 feet long each. It required 16 days to harvest the 1088 plots. -Each plot was 1/242 acre. The authors conclude that long narrow plots of 12/242 to 16/242 acre would be best.

    -

    Field width: 8 plots * 60 feet = 480 feet

    -

    Field length: 136 rows * 3 feet = 408 feet

    -

    1934 Experiment

    -

    This experiment was conducted at the New Area, Pusa. The experiment was laid out in 6 blocks, each separated by a 3-foot bund. The cutting of the canes began in Jan 1934, taking 24 days. (An earthquake 15 January delayed harvesting). -Conclusion: Variation is reduced by increasing the plot size up to 9/242 acre.

    -

    Field width: 8 plots * 60 feet = 480 feet

    -

    Field length: 121 rows * 3 feet = 363 feet

    -

    Source

    - -

    1933 Data

    -

    Wynne Sayer, M. Vaidyanathan and S. Subrammonia Iyer (1936). - Ideal size and shape of sugar-cane experimental plots based upon tonnage experiments with Co 205 and Co 213 conducted in Pusa. - Indian J. Agric. Sci., 1936, 6, 684-714. Appendix, page 712. - https://archive.org/details/in.ernet.dli.2015.271737

    -

    1934 data

    -

    Wynne Sayer and Krishna Iyer. (1936). - On some of the factors that influence the error of field experiments with special reference to sugar cane. - Indian J. Agric. Sci., 1936, 6, 917-929. Appendix, page 927. - https://archive.org/details/in.ernet.dli.2015.271737

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(sayer.sugarcane.uniformity)
-  dat33 <- subset(sayer.sugarcane.uniformity, year==1933)
-  dat34 <- subset(sayer.sugarcane.uniformity, year==1934)
-  
-  b1 <- subset(dat33, row<31)
-  b2 <- subset(dat33, row > 30 & row < 61)
-  b3 <- subset(dat33, row > 60 & row < 91)
-  b4 <- subset(dat33, row > 105 & row < 136)
-  mean(b1$yield) # 340.7 vs Sayer 340.8
-
    #> [1] 340.7021
      mean(b2$yield) # 338.2 vs Sayer 338.6
-
    #> [1] 338.1708
      mean(b3$yield) # 331.3 vs Sayer 330.2
-
    #> [1] 331.35
      mean(b4$yield) # 295.4 vs Sayer 295.0
-
    #> [1] 295.3833
    
-  mean(dat34$yield) # 270.83 vs Sayer 270.83
-
    #> [1] 270.8296
      
-  libs(desplot)
-  
-  desplot(dat33, yield ~ col*row,
-          flip=TRUE, aspect=408/480, # true aspect
-          main="sayer.sugarcane.uniformity 1933")
-
    
-  desplot(dat34, yield ~ col*row,
-          flip=TRUE, aspect=363/480, # true aspect
-          main="sayer.sugarcane.uniformity 1934")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/senshu.rice-1.png b/docs/reference/senshu.rice-1.png deleted file mode 100644 index a6c6aaa0..00000000 Binary files a/docs/reference/senshu.rice-1.png and /dev/null differ diff --git a/docs/reference/senshu.rice.html b/docs/reference/senshu.rice.html deleted file mode 100644 index 1dbf1625..00000000 --- a/docs/reference/senshu.rice.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - - -Multi-environment trial of rice, with solar radiation and temperature — senshu.rice • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of rice, with solar radiation and temperature

    - - -
    - -
    -

    Response of rice to solar radiation and temperature

    -
    - - - -

    Format

    - -

    A data frame with 40 observations on the following 7 variables.

    -
    country

    country

    -
    loc

    location

    -
    year

    year of planting, last two digits

    -
    month

    month of planting

    -
    rad

    solar radiation

    -
    mint

    minimum temperature

    -
    yield

    yield t/ha

    - -
    - -

    Details

    - -

    Minimum temperature is the average across 30 days post flowering.

    -

    Opinion: Fitting a quadratic model to this data makes no sense.

    -

    Source

    - -

    Seshu, D. V. and Cady, F. B. 1984. - Response of rice to solar radiation and temperature estimated from international yield trials. - Crop Science, 24, 649-654. - https://doi.org/10.2135/cropsci1984.0011183X002400040006x

    -

    References

    - -

    Walter W. Piegorsch, A. John Bailer. (2005) - Analyzing Environmental Data, Wiley.

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-
-data(senshu.rice)
-dat <- senshu.rice
-
-# Model 1 of Senshu & Cady
-m1 <- lm(yield ~ 1 + rad + mint + I(mint^2), dat)
-coef(m1)
-
    #>  (Intercept)          rad         mint    I(mint^2) 
-#> 36.784898356  0.004618273 -2.726776448  0.051599360 
    
-# Use Fieller to calculate conf int around optimum minimum temp
-# See: Piegorsch & Bailer, p. 31.
-# Calculation derived from vegan:::fieller.MOStest
-m2 <- lm(yield ~ 1 + mint + I(mint^2), dat)
-b1 <- coef(m2)[2]
-b2 <- coef(m2)[3]
-vc <- vcov(m2)
-sig11 <- vc[2,2]
-sig12 <- vc[2,3]
-sig22 <- vc[3,3]
-u <- -b1/2/b2
-tval <- qt(1-.05/2, nrow(dat)-3)
-gam <- tval^2 * sig22 / b2^2
-x <- u + gam * sig12 / (2 * sig22)
-f <- tval / (-2*b2)
-sq <- sqrt(sig11 + 4*u*sig12 + 4*u^2*sig22 - gam * (sig11 - sig12^2 / sig22) )
-ci <- (x + c(1,-1)*f*sq) / (1-gam)
-plot(yield ~ mint, dat, xlim=c(17, 32),
-main="senshu.rice: Quadratic fit and Fieller confidence interval",
-     xlab="Minimum temperature", ylab="Yield")
-
    lines(17:32, predict(m2, new=data.frame(mint=17:32)))
-
    abline(v=ci, col="blue")
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/shafi.tomato.uniformity.html b/docs/reference/shafi.tomato.uniformity.html deleted file mode 100644 index 2be2f619..00000000 --- a/docs/reference/shafi.tomato.uniformity.html +++ /dev/null @@ -1,219 +0,0 @@ - - - - - - - - -Uniformity trial of tomato — shafi.tomato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of tomato

    - - -
    - -
    -

    Uniformity trial of tomato in India.

    -
    - - 
    data("shafi.tomato.uniformity")
    - - -

    Format

    - -

    A data frame with 200 observations on the following 3 variables.

    -
    row

    row ordinate

    -
    col

    column ordinate

    -
    yield

    yield, kg/plot

    - -
    - -

    Details

    - -

    The original data was collected on 1m x 1m plots. The data here are - aggregated 2m x 2m plots.

    -

    Field length: 20 row * 2 m = 40 m

    -

    Field width: 10 col * 2 m = 20 m

    -

    Source

    - -

    Shafi, Sameera (2007). - On Some Aspects of Plot Techniques in Field Experiments on Tomato (Lycopersicon esculentum mill.) in Soils of Kashmir. - Thesis. Univ. of Ag. Sciences & Technology of Kashmir. Table 2.2.1. - https://krishikosh.egranth.ac.in/handle/1/5810083035

    -

    References

    - -

    Shafi, Sameera; S.A.Mir, Nageena Nazir, and Anjum Rashid. (2010). - Optimum plot size for tomato by using S-PLUS and R-software's in the soils of Kashmir. - Asian J. Soil Sci., 4, 311-314. - https://www.researchjournal.co.in/upload/assignments/4_311-314.pdf

    - -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  data(shafi.tomato.uniformity)
-  shafi.tomato.uniformity <- dat
-
    #> Error in eval(expr, envir, enclos): object 'dat' not found
    
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          aspect=40/20, # true aspect
-          main="shafi.tomato.uniformity")
-
    #> Error in desplot(dat, yield ~ col * row, aspect = 40/20, main = "shafi.tomato.uniformity"): object 'dat' not found
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/shafii.rapeseed-1.png b/docs/reference/shafii.rapeseed-1.png deleted file mode 100644 index f3e9c51f..00000000 Binary files a/docs/reference/shafii.rapeseed-1.png and /dev/null differ diff --git a/docs/reference/shafii.rapeseed-2.png b/docs/reference/shafii.rapeseed-2.png deleted file mode 100644 index 27efc41a..00000000 Binary files a/docs/reference/shafii.rapeseed-2.png and /dev/null differ diff --git a/docs/reference/shafii.rapeseed.html b/docs/reference/shafii.rapeseed.html deleted file mode 100644 index 6ee0dfe3..00000000 --- a/docs/reference/shafii.rapeseed.html +++ /dev/null @@ -1,260 +0,0 @@ - - - - - - - - -Multi-environment trial of rapeseed in U.S. — shafii.rapeseed • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of rapeseed in U.S.

    - - -
    - -
    -

    Rapeseed yield multi-environment trial, 6 genotypes, 3 years, 14 loc, 3 rep

    -
    - - - -

    Format

    - -

    A data frame with 648 observations on the following 5 variables.

    -
    year

    year, numeric: 87, 88, 89

    -
    loc

    location, 14 levels

    -
    rep

    rep, 3 levels

    -
    gen

    genotype, 6 levels

    -
    yield

    yield, kg/ha

    - -
    - -

    Details

    - -

    The data are from the U.S. National Winter Rapeseed trials conducted - in 1986, 1987, and 1988. Trial locations included Georgia (GGA, TGA), - Idaho (ID), Kansas (KS), Mississippi (MS), Montana (MT), New York - (NY), North Carolina (NC), Oregon (OR), South Carolina (SC), Tennessee - (TN), Texas (TX), Virginia (VA), and Washington (WA).

    -

    SAS codes for the analysis can be found at - https://webpages.uidaho.edu/cals-statprog/ammi/index.html

    - -

    Electronic version from: - https://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data

    -

    Used with permission of Bill Price.

    -

    Source

    - -

    Bahman Shafii and William J Price, 1998. - Analysis of Genotype-by-Environment Interaction Using the - Additive Main Effects and Multiplicative Interaction Model - and Stability Estimates. - JABES, 3, 335--345. - https://doi.org/10.2307/1400587

    -

    References

    - -

    Matthew Kramer (2018). - Using the Posterior Predictive Distribution as a Diagnostic Tool for Mixed Models. - Joint Statistical Meetings 2018, Biometrics Section. - https://www.ars.usda.gov/ARSUserFiles/3122/KramerProceedingsJSM2018.pdf

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(shafii.rapeseed)
-dat <- shafii.rapeseed
-
-dat$gen <- with(dat, reorder(gen, yield, mean))
-dat$loc <- with(dat, reorder(loc, yield, mean))
-dat$yield <- dat$yield/1000
-
-dat <- transform(dat, rep=factor(rep), year=as.factor(as.character(year)))
-dat$locyr = paste(dat$loc, dat$year, sep="")
-
-# The 'means' of reps
-datm <- aggregate(yield~gen+year+loc+locyr, data=dat, FUN=mean)
-datm <- datm[order(datm$gen),]
-datm$gen <- as.character(datm$gen)
-datm$gen <- factor(datm$gen,
-                       levels=c("Bienvenu","Bridger","Cascade",
-                         "Dwarf","Glacier","Jet"))
-dat$locyr <- reorder(dat$locyr, dat$yield, mean)
-
-libs(lattice)
-# This picture tells most of the story
-dotplot(loc~yield|gen,group=year,data=dat,
-        auto.key=list(columns=3),
-        par.settings=list(superpose.symbol=list(pch = c('7','8','9'))),
-        main="shafii.rapeseed",ylab="Location")
-
    
-
-# AMMI biplot.  Remove gen and locyr effects.
-m1.lm <- lm(yield ~ gen + locyr, data=datm)
-datm$res <- resid(m1.lm)
-# Convert to a matrix
-libs(reshape2)
-dm <- melt(datm, measure.var='res', id.var=c('gen', 'locyr'))
-dmat <- acast(dm, gen~locyr)
-# AMMI biplot.  Figure 1 of Shafii (1998)
-biplot(prcomp(dmat), main="shafii.rapeseed - AMMI biplot")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/shaw.oats-1.png b/docs/reference/shaw.oats-1.png deleted file mode 100644 index a3ee98ad..00000000 Binary files a/docs/reference/shaw.oats-1.png and /dev/null differ diff --git a/docs/reference/shaw.oats.html b/docs/reference/shaw.oats.html deleted file mode 100644 index 592731e0..00000000 --- a/docs/reference/shaw.oats.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - - - -Multi-environment trial of oats in India — shaw.oats • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Multi-environment trial of oats in India

    - - -
    - -
    -

    Multi-environment trial of oats in India, 13 genotypes, 3 year, 2 loc, - 5 reps

    -
    - - 
    data("shaw.oats")
    - - -

    Format

    - -

    A data frame with 390 observations on the following 5 variables.

    -
    env

    environment, 2 levels

    -
    year

    year, 3 levels

    -
    block

    block, 5 levels

    -
    gen

    genotype variety, 13 levels

    -
    yield

    yield of oats, pounds per plot

    - -
    - -

    Details

    - -

    An oat trial in India of 11 hybrid oats compared to 2 established - high-yielding varieties, labeled L and M. The trail was conducted at 2 - locations. The size and exact locations of the plots varied from year - to year.

    -

    At Pusa, the crop was grown without irrigation. At Karnal the crop was - given 2-3 irrigations. Five blocks were used, each plot 1000 square - feet. In 1932, variety L was high-yielding at Pusa, but low-yielding - at Karnal.

    -

    Shaw used this data to illustrate ANOVA for a multi-environment trial.

    -

    Source

    - -

    F.J.F. Shaw (1936). - A Handbook of Statistics For Use In Plant Breeding and Agricultural Problems. - The Imperial Council of Agricultural Research, India. - https://archive.org/details/HandbookStatistics1936/page/n12 - P. 126

    -

    References

    - -

    None

    - -

    Examples

    - 
    
-library(agridat)
-data(shaw.oats)
-dat <- shaw.oats
-# sum(dat$yield) # 16309 matches Shaw p. 125
-# sum( (dat$yield-mean(dat$yield)) ^2) # total SS matches Shaw p. 141
-
-dat$year <- factor(dat$year)
-libs(lattice)
-
-dotplot(yield ~ gen|env, data=dat, groups=year,
-        main="shaw.oats",
-        par.settings=list(superpose.symbol=list(pch=c('2','3','4'))),
-        panel=function(x,y,...){
-          panel.dotplot(x,y,...)
-          panel.superpose(x,y,..., panel.groups=function(x,y,col.line,...) {
-            dd<-aggregate(y~x,data.frame(x,y),mean)
-            panel.xyplot(x=dd$x, y=dd$y, col=col.line, type="l")
-          })},
-        auto.key=TRUE)
-
    
-
-
-# Shaw & Bose meticulously calculate the ANOVA table, p. 141
-m1 <- aov(yield ~ year*env*block*gen - year:env:block:gen, dat)
-anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>                Df  Sum Sq Mean Sq  F value    Pr(>F)    
-#> year            2 14475.3  7237.6 352.8158 < 2.2e-16 ***
-#> env             1   345.4   345.4  16.8352 8.552e-05 ***
-#> block           4   197.8    49.4   2.4102   0.05444 .  
-#> gen            12 12297.2  1024.8  49.9546 < 2.2e-16 ***
-#> year:env        2  5945.6  2972.8 144.9164 < 2.2e-16 ***
-#> year:block      8  3175.2   396.9  19.3480 < 2.2e-16 ***
-#> env:block       4   735.2   183.8   8.9601 3.422e-06 ***
-#> year:gen       24  3839.0   160.0   7.7976 9.418e-14 ***
-#> env:gen        12  1314.2   109.5   5.3386 7.617e-07 ***
-#> block:gen      48  1112.5    23.2   1.1298   0.30246    
-#> year:env:block  8  1012.6   126.6   6.1702 2.067e-06 ***
-#> year:env:gen   24  3123.5   130.1   6.3442 2.344e-11 ***
-#> year:block:gen 96  2241.1    23.3   1.1380   0.26386    
-#> env:block:gen  48   897.2    18.7   0.9112   0.63336    
-#> Residuals      96  1969.3    20.5                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-
    -
    - -
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    -
    -

    Developed by Kevin Wright.

    -
    - -
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    Site built with pkgdown 1.6.1.

    -
    - -
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    - - - - - - - - diff --git a/docs/reference/silva.cotton-1.png b/docs/reference/silva.cotton-1.png deleted file mode 100644 index bbbaad7b..00000000 Binary files a/docs/reference/silva.cotton-1.png and /dev/null differ diff --git a/docs/reference/silva.cotton-2.png b/docs/reference/silva.cotton-2.png deleted file mode 100644 index d61a8a87..00000000 Binary files a/docs/reference/silva.cotton-2.png and /dev/null differ diff --git a/docs/reference/silva.cotton-3.png b/docs/reference/silva.cotton-3.png deleted file mode 100644 index bffe44cf..00000000 Binary files a/docs/reference/silva.cotton-3.png and /dev/null differ diff --git a/docs/reference/silva.cotton-4.png b/docs/reference/silva.cotton-4.png deleted file mode 100644 index ed38a278..00000000 Binary files a/docs/reference/silva.cotton-4.png and /dev/null differ diff --git a/docs/reference/silva.cotton-5.png b/docs/reference/silva.cotton-5.png deleted file mode 100644 index cd3ee4b6..00000000 Binary files a/docs/reference/silva.cotton-5.png and /dev/null differ diff --git a/docs/reference/silva.cotton-6.png b/docs/reference/silva.cotton-6.png deleted file mode 100644 index cd3ee4b6..00000000 Binary files a/docs/reference/silva.cotton-6.png and /dev/null differ diff --git a/docs/reference/silva.cotton.html b/docs/reference/silva.cotton.html deleted file mode 100644 index de3f5bd5..00000000 --- a/docs/reference/silva.cotton.html +++ /dev/null @@ -1,331 +0,0 @@ - - - - - - - - -Number of cotton bolls for different levels of defoliation. — silva.cotton • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Number of cotton bolls for different levels of defoliation.

    - - -
    - -
    -

    Number of cotton bolls, nodes, plant height, and plant weight for different levels of defoliation.

    -
    - - 
    data("silva.cotton")
    - - -

    Format

    - -

    A data frame with 125 observations on the following 4 variables.

    -
    stage

    growth stage

    -
    defoliation

    level of defoliation, 0, 25, 50, 75, 100

    -
    plant

    plant number

    -
    rep

    replicate

    -
    reproductive

    number of reproductive structures

    -
    bolls

    number of bolls

    -
    height

    plant height

    -
    nodes

    number of nodes

    -
    weight

    weight of bolls

    - - -
    - -

    Details

    - - - -

    Data come from a greenhouse experiment with cotton plants. Completely - randomized design with 5 replicates, 2 plants per pot.

    -

    Artificial defoliation was used at levels 0, 25, 50, 75, 100 percent.

    -

    Data was collected per plant at five growth stages: vegetative, - flower-bud, blossom, fig and cotton boll.

    -

    The primary response variable is the number of bolls. The data are - counts, underdispersed, correlated.

    -

    Zeviana et al. used this data to compared Poisson, Gamma-count, and - quasi-Poisson GLMs.

    -

    Bonat & Zeviani used this data to fit multivariate correlated - generalized linear model.

    - -

    Used with permission of Walmes Zeviani.

    -

    Electronic version from: - https://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt

    -

    Source

    - -

    Silva, Anderson Miguel da; Degrande, Paulo Eduardo; Suekane, Renato; - Fernandes, Marcos Gino; & Zeviani, Walmes Marques. (2012). - Impacto de diferentes niveis de desfolha artificial nos estadios fenologicos do algodoeiro. - Revista de Ciencias Agrarias, 35(1), 163-172. - https://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.

    -

    References

    - - - -

    Zeviani, W. M., Ribeiro, P. J., Bonat, W. H., Shimakura, S. E., Muniz, J. A. (2014). - The Gamma-count distribution in the analysis of experimental underdispersed data. - Journal of Applied Statistics, 41(12), 1-11. - https://doi.org/10.1080/02664763.2014.922168 - Online supplement: https://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014

    -

    Regression Models for Count Data. - https://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls

    -

    Wagner Hugo Bonat & Walmes Marques Zeviani (2017). - Multivariate Covariance Generalized Linear Models for the Analysis of - Experimental Data. - Short-cource at: 62nd RBras and 17th SEAGRO meeting/ - https://github.com/leg-ufpr/mcglm4aed

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-data(silva.cotton)
-dat <- silva.cotton
-dat$stage <- ordered(dat$stage,
-                     levels=c("vegetative","flowerbud","blossom","boll","bollopen"))
-# make stage a numeric factors
-dat <- transform(dat,
-                 stage = factor(stage, levels = unique(stage),
-                                labels = 1:nlevels(stage)))
-
-# sum data across plants, 1 pot = 2 plants
-dat <- aggregate(cbind(weight,height,bolls,nodes) ~
-                    stage+defoliation+rep, data=dat, FUN=sum)
-
-  # all traits, plant-level data
-  libs(latticeExtra)
-  foo <- xyplot(weight + height + bolls + nodes ~ defoliation | stage,
-                data = dat, outer=TRUE,
-                xlab="Defoliation percent", ylab="", main="silva.cotton",
-                as.table = TRUE, jitter.x = TRUE, type = c("p", "smooth"),
-                scales = list(y = "free"))
-  combineLimits(useOuterStrips(foo))
-
    
-
-if(0){
-
-  # poisson glm with quadratic effect for defoliation
-  m0 <- glm(bolls ~ 1, data=dat, family=poisson)
-  m1 <- glm(bolls ~ defoliation+I(defoliation^2), data=dat, family=poisson)
-  m2 <- glm(bolls ~ stage:defoliation+I(defoliation^2), data=dat, family=poisson)
-  m3 <- glm(bolls ~ stage:(defoliation+I(defoliation^2)), data=dat, family=poisson)
-  par(mfrow=c(2,2)); plot(m3); layout(1)
-  anova(m0, m1, m2, m3, test="Chisq")
-
-  # predicted values
-  preddat <- expand.grid(stage=levels(dat$stage),
-                         defoliation=seq(0,100,length=20))
-  preddat$pred <- predict(m3, newdata=preddat, type="response")
-
-  # Zeviani figure 3
-  libs(latticeExtra)
-  xyplot(bolls ~ jitter(defoliation)|stage, dat,
-         as.table=TRUE,
-         main="silva.cotton - observed and model predictions",
-         xlab="Defoliation percent",
-         ylab="Number of bolls") +
-    xyplot(pred ~ defoliation|stage, data=preddat,
-           as.table=TRUE,
-           type='smooth', col="black", lwd=2)
-}
-
-if(0){
-  # ----- mcglm -----
-  dat <- transform(dat, deffac=factor(defoliation))
-
-  libs(car)
-
-  vars <- c("weight","height","bolls","nodes")
-  splom(~dat[vars], data=dat,
-        groups = stage,
-        auto.key = list(title = "Growth stage",
-                        cex.title = 1,
-                        columns = 3),
-        par.settings = list(superpose.symbol = list(pch = 4)),
-        as.matrix = TRUE)
-
-  splom(~dat[vars], data=dat,
-        groups = defoliation,
-        auto.key = list(title = "Artificial defoliation",
-                        cex.title = 1,
-                        columns = 3),
-        as.matrix = TRUE)
-
-  # multivariate linear model.
-  m1 <- lm(cbind(weight, height, bolls, nodes) ~ stage * deffac,
-           data = dat)
-  anova(m1)
-
-  summary.aov(m1)
-
-  r0 <- residuals(m1)
-
-  # Checking the models assumptions on the residuals.
-  car::scatterplotMatrix(r0,
-                         gap = 0, smooth = FALSE, reg.line = FALSE, ellipse = TRUE,
-                         diagonal = "qqplot")
-
-  }
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/sinclair.clover-1.png b/docs/reference/sinclair.clover-1.png deleted file mode 100644 index af38693e..00000000 Binary files a/docs/reference/sinclair.clover-1.png and /dev/null differ diff --git a/docs/reference/sinclair.clover.html b/docs/reference/sinclair.clover.html deleted file mode 100644 index 56df0bd7..00000000 --- a/docs/reference/sinclair.clover.html +++ /dev/null @@ -1,295 +0,0 @@ - - - - - - - - -Clover yields in a factorial fertilizer experiment — sinclair.clover • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Clover yields in a factorial fertilizer experiment

    - - -
    - -
    -

    Clover yields in a factorial fertilizer experiment

    -
    - - 
    data("sinclair.clover")
    - - -

    Format

    - -

    A data frame with 25 observations on the following 3 variables.

    -
    yield

    yield t/ha

    -
    P

    phosphorous fertilizer kg/ha

    -
    S

    sulfur fertilizer kg/ha

    - -
    - -

    Details

    - -

    A phosphorous by sulfur factorial experiment at Dipton in Southland, - New Zealand. There were 3 reps. Plots were harvested repeatedly from - Dec 1992 to Mar 1994. Yields reported are the total dry matter across - all cuttings.

    -

    Source

    - -

    Sinclair AG, Risk WH, Smith LC, Morrison JD & Dodds KG (1994) - Sulphur and phosphorus in balanced pasture nutrition. - Proc N Z Grass Assoc, 56, 13-16.

    -

    References

    - -

    Dodds, KG and Sinclair, AG and Morrison, JD. (1995). - A bivariate response surface for growth data. - Fertilizer research, 45, 117-122. - https://doi.org/10.1007/BF00790661

    - -

    Examples

    - 
    
-library(agridat)
-
-data(sinclair.clover)
-dat <- sinclair.clover
-
-libs(lattice)
-xyplot(yield~P|factor(S), dat, layout=c(5,1),
-       main="sinclair.clover - Yield by sulfur levels",
-       xlab="Phosphorous")
-
    
-# Dodds fits a two-dimensional Mitscherlich-like model:
-# z = a*(1+b*{(s+t*x)/(x+1)}^y) * (1+d*{(th+r*y)/(y+1)}^x)
-
-# First, re-scale the problem to a more stable part of the parameter space
-dat <- transform(dat, x=P/10, y=S/10)
-
-# Response value for (x=0, y=maximal), (x=maximal, y=0), (x=max, y=max)
-z0m <- 5
-zm0 <- 5
-zmm <- 10.5
-# The parameters are somewhat sensitive to starting values.
-# I had to try a couple different initial values to match the paper by Dodds
-m1 <- nls(yield ~ alpha*(1 + beta*{(sig+tau*x)/(x+1)}^y) * (1 + del*{(th+rho*y)/(y+1)}^x),
-          data=dat, # trace=TRUE,
-          start=list(alpha=zmm, beta=(zm0/zmm)-1, del=(z0m/zmm)-1,
-                     sig=.51, tau=.6, th=.5, rho=.7))
-summary(m1) # Match Dodds Table 2
-
    #> 
-#> Formula: yield ~ alpha * (1 + beta * {
-#>     (sig + tau * x)/(x + 1)
-#> }^y) * (1 + del * {
-#>     (th + rho * y)/(y + 1)
-#> }^x)
-#> 
-#> Parameters:
-#>       Estimate Std. Error t value Pr(>|t|)    
-#> alpha 11.15148    0.66484  16.773 1.96e-12 ***
-#> beta  -0.61223    0.03759 -16.286 3.23e-12 ***
-#> del   -0.48781    0.04046 -12.057 4.68e-10 ***
-#> sig    0.26783    0.16985   1.577  0.13224    
-#> tau    0.68030    0.06333  10.741 2.94e-09 ***
-#> th     0.59656    0.16716   3.569  0.00219 ** 
-#> rho    0.83273    0.06204  13.421 8.16e-11 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 0.5298 on 18 degrees of freedom
-#> 
-#> Number of iterations to convergence: 7 
-#> Achieved convergence tolerance: 3.913e-06
-#> 
    ## Parameters:
-##       Estimate Std. Error t value Pr(>|t|)
-## alpha 11.15148    0.66484  16.773 1.96e-12 ***
-## beta  -0.61223    0.03759 -16.286 3.23e-12 ***
-## del   -0.48781    0.04046 -12.057 4.68e-10 ***
-## sig    0.26783    0.16985   1.577  0.13224
-## tau    0.68030    0.06333  10.741 2.94e-09 ***
-## th     0.59656    0.16716   3.569  0.00219 **
-## rho    0.83273    0.06204  13.421 8.16e-11 ***
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## Residual standard error: 0.5298 on 18 degrees of freedom
-
-# \dontrun{
-
-pred <- expand.grid(x=0:17, y=0:9)
-pred$z <- predict(m1, pred)
-
-# 3D plot of data with fitted surface.  Matches Dodds figure 2.
-libs(rgl)
-bg3d(color = "white")
-clear3d()
-spheres3d(dat$x, dat$y, dat$yield,
-         radius=.2, col = rep("navy", nrow(dat)))
-surface3d(seq(0, 17, by = 1), seq(0, 9, by = 1), pred$z,
-          alpha=0.9, col="wheat",
-          front="fill", back="fill")
-axes3d()
-title3d("sinclair.clover - yield","", xlab="Phosphorous/10",
-        ylab="Sulfur/10", zlab="", line=3, cex=1.5)
-view3d(userMatrix=matrix(c(.7,.2,-.7,0, -.7,.2,-.6,0, 0,.9,.3,0, 0,0,0,1),ncol=4))
-  # snapshot3d(file, "png")
-
-rgl.close()
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/smith.beans.uniformity-1.png b/docs/reference/smith.beans.uniformity-1.png deleted file mode 100644 index eae07a02..00000000 Binary files a/docs/reference/smith.beans.uniformity-1.png and /dev/null differ diff --git a/docs/reference/smith.beans.uniformity-2.png b/docs/reference/smith.beans.uniformity-2.png deleted file mode 100644 index c107d14a..00000000 Binary files a/docs/reference/smith.beans.uniformity-2.png and /dev/null differ diff --git a/docs/reference/smith.beans.uniformity-3.png b/docs/reference/smith.beans.uniformity-3.png deleted file mode 100644 index 9b2ec395..00000000 Binary files a/docs/reference/smith.beans.uniformity-3.png and /dev/null differ diff --git a/docs/reference/smith.beans.uniformity-4.png b/docs/reference/smith.beans.uniformity-4.png deleted file mode 100644 index 97d8a714..00000000 Binary files a/docs/reference/smith.beans.uniformity-4.png and /dev/null differ diff --git a/docs/reference/smith.beans.uniformity.html b/docs/reference/smith.beans.uniformity.html deleted file mode 100644 index 841016ef..00000000 --- a/docs/reference/smith.beans.uniformity.html +++ /dev/null @@ -1,259 +0,0 @@ - - - - - - - - -Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Uniformity trials of beans, 2 species in 2 years

    - - -
    - -
    -

    Uniformity trials of beans at California, 1954-1955, 2 species in 2 years

    -
    - - 
    data("smith.beans.uniformity")
    - - -

    Format

    - -

    A data frame with 912 observations on the following 4 variables.

    -
    expt

    experiment

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, kg

    - -
    - -

    Details

    - - - -

    Trials were conducted in California.

    -

    In 1955 plots were twice as wide and twice as long as in 1954. - Red Kidney is a bush variety bean, Standard Pink is a viny variety.

    -

    Smith randomly assigned A,B,C,D to plots and used these as 'varieties' - for calculating ANOVA tables. Plots were combined side-by-side and - end-to-end to make larger plots. Decreasing LSDs were observed for - increases in plot sizes. LSDs were seldom above 200, which was - considered to be a noticeable difference for the farmers.

    -

    There are four datasets:

    -

    1954

    -

    Experiment 1: Red Kidney. - Experiment 2: Standard Pink

    -

    Field width: 18 plots * 30 inches = 45 ft

    -

    Field length: 12 plots * 15 ft = 180 ft

    -

    1955

    -

    Experiment 3: Red Kidney. - Experiment 4: Standard Pink

    -

    Field width: 16 plots * 2 rows * 30 in = 80 ft

    -

    Field length: 15 plots * 30 ft = 450 ft

    -

    Source

    - -

    Francis L. Smith, 1958. - Effects of plot size, plot shape, and number of replications on the - efficacy of bean yield trials. - Hilgardia, 28, 43-63. - https://doi.org/10.3733/hilg.v28n02p043

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(smith.beans.uniformity)
-  dat1 <- subset(smith.beans.uniformity, expt=="E1")
-  dat2 <- subset(smith.beans.uniformity, expt=="E2")
-  dat3 <- subset(smith.beans.uniformity, expt=="E3")
-  dat4 <- subset(smith.beans.uniformity, expt=="E4")
-
-  cv <- function(x) { sd(x)/mean(x) }
-  cv(dat1$yield)
-
    #> [1] 0.1409846
      cv(dat2$yield) # Does not match Smith. Checked all values by hand.
-
    #> [1] 0.1137417
      cv(dat3$yield)
-
    #> [1] 0.1531631
      cv(dat4$yield)
-
    #> [1] 0.09932579
    
-  libs("desplot")
-  desplot(dat1, yield ~ col*row,
-          aspect=180/45, flip=TRUE, # true aspect
-          main="smith.beans.uniformity, expt 1 (true aspect)")
-
    
-  desplot(dat2, yield ~ col*row,
-          aspect=180/45, flip=TRUE, # true aspect
-          main="smith.beans.uniformity, expt 2 (true aspect)")
-
    
-  desplot(dat3, yield ~ col*row,
-          aspect=450/80, flip=TRUE, # true aspect
-          main="smith.beans.uniformity, expt 3 (true aspect)")
-
    
-  desplot(dat4, yield ~ col*row,
-          aspect=450/80, flip=TRUE, # true aspect
-          main="smith.beans.uniformity expt 4, (true aspect)")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/smith.corn.uniformity-1.png b/docs/reference/smith.corn.uniformity-1.png deleted file mode 100644 index 4ca2e54d..00000000 Binary files a/docs/reference/smith.corn.uniformity-1.png and /dev/null differ diff --git a/docs/reference/smith.corn.uniformity.html b/docs/reference/smith.corn.uniformity.html deleted file mode 100644 index a5617af8..00000000 --- a/docs/reference/smith.corn.uniformity.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of corn, 3 years on same ground

    - - -
    - -
    -

    Uniformity trial of corn, 3 years on same ground, 1895-1897, in Illinois.

    -
    - - - -

    Format

    - -

    A data frame with 360 observations on the following 5 variables.

    -
    row

    row

    -
    col

    column

    -
    plot

    plot number, consistent across years

    -
    year

    year. Last two digits of 1895, 1896, 1897

    -
    yield

    yield, bushels / acre

    - -
    - -

    Details

    - -

    Data come from the Illinois Experiment Station.

    -

    The data values are from Smith (1910) and the field map is from Harris (1920). - Each plot was 1/10 acre, but the dimensions are not given. Note that - 1/10 acre is also the area of a square 1 chain (66 feet) on a side.

    -

    The following text is abridged from Smith (1910).

    -

    How much variability may we reasonably expect in land that is - apparently uniform? Some data among the records of the soil plots at - the Illinois Experiment station furnish interesting material for study - in this connection.

    -

    A field that had lain sixteen years in pasture was broken up in 1895 - and laid out into plots to be subsequently used for soil - experiments. The land is slightly rolling but otherwise quite uniform - in appearance. There are in the series to be considered in this - connection 120 one-tenth acre plots. These plots were all planted to - corn for three consecutive years without any soil treatment, so that - the records offer a rather exceptional opportunity for a study of this - kind.

    -

    The yields of all these plots in terms of bushels of shelled corn per - acre for the three years are given in the accompanying table.

    -

    A study of this data reveals some very striking variations. It will be - noticed in the first place that there is a tremendous difference in - production in the different years. The first year, 1895, was an - extremely unfavorable one for corn and the yields are exceptionally - low. The weather records show that the season was not only unusually - dry, but also cool in the early part. The following year we have an - exceptionally favorable corn season, and the yields run unusually - high. The third year was also a good one, and the yields are perhaps - somewhat above the normal for this locality.

    -

    It will be observed that certain plots appear to be very - abnormal. Thus plots 117, 118, 119, and 120 give an abnormally high - yield in the first season and an abnormally low one in the two - following years. This is to be accounted for in the topography of the - land. These plots lie in a low spot which was favorable in the dry - year of 1895, but unfavorable in 1896 and 1897. For this reason these - four plots were rejected from further consideration in this study, as - were also plots 616, 617, 618, 619, and 620. This leaves 111 plots - whose variations are apparently unaccounted for and which furnish the - data from which the following results are taken.

    -

    It is noticeable that the variability as measured by the standard - deviation becomes less in each succeeding year. This suggests the - question as to whether continued cropping might not tend to induce - uniformity. The records of a few of these plots which were continued - in corn for three years longer, however, do not support such a - conclusion.

    -

    It seems reasonable to expect greater variability in seasons very - unfavorable for production, such as that of 1895, because so much may - depend upon certain critical factors of production coming into play - and this suggestion may be the explanation of the high standard - deviation in this first year. Results extending over a longer series - of years would be extremely interesting in this connection.

    -

    If we consider the total range of variation in any single year, we - find differences as follows: Plots lying adjoining have shown the - following maximum variations: 18 bushels in 1895; 11 bushels in 1896; - 8 bushels in 1897.

    -

    The above results give us a conception of the unaccountable plot - variations which we have to deal with in field tests. The possibility - remains that a still closer study might detect some abnormal factors - at play to account for these variations in certain cases, but the - study certainly suggests the importance of conservatism in arriving at - conclusions based upon plot tests.

    -

    The particular value that the writer has derived from this study is - the strengthening of his conviction that the only dependence to be - placed upon variety tests and other field experiments is from records - involving the average of liberal numbers and extending over long - periods of time.

    -

    Source

    - -

    Smith, L.H. 1910. Plot arrangement for variety experiments with corn. - Agronomy Journal, 1, 84--89. Table 1. - https://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84

    -

    Harris, J.A. 1920. Practical universality of field heterogeneity as a factor - influencing plot yields. - Journal of Agricultural Research, 19, 279--314. Page 296-297. - https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(smith.corn.uniformity)
-dat <- smith.corn.uniformity
-
-  dat = transform(dat, year=factor(year))
-  libs(desplot)
-  desplot(dat, yield~col*row|year,
-          layout=c(2,2), aspect=1,
-          main="smith.corn.uniformity: yield across years 1895-1987")
-
    
-  ## # Outliers are obvious
-  ## libs(lattice)
-  ## xyplot(yield~row|factor(col), dat, groups=year,
-  ##        auto.key=list(columns=3), main="smith.corn.uniformity")
-
-  libs(rgl)
-  # A few odd pairs of outliers in column 6
-  # black/gray dots very close to each other
-  plot3d(dat$col, dat$row, dat$yield, col=dat$year,
-         xlab="col",ylab="row",zlab="yield")
-  rgl.close()
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/snedecor.asparagus-1.png b/docs/reference/snedecor.asparagus-1.png deleted file mode 100644 index 709bf48f..00000000 Binary files a/docs/reference/snedecor.asparagus-1.png and /dev/null differ diff --git a/docs/reference/snedecor.asparagus.html b/docs/reference/snedecor.asparagus.html deleted file mode 100644 index 335d5b98..00000000 --- a/docs/reference/snedecor.asparagus.html +++ /dev/null @@ -1,307 +0,0 @@ - - - - - - - - -Asparagus yields for different cutting treatments — snedecor.asparagus • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Asparagus yields for different cutting treatments

    - - -
    - -
    -

    Asparagus yields for different cutting treatments, in 4 years.

    -
    - - - -

    Format

    - -

    A data frame with 64 observations on the following 4 variables.

    -
    block

    block factor, 4 levels

    -
    year

    year, numeric

    -
    trt

    treatment factor of final cutting date

    -
    yield

    yield, ounces

    - -
    - -

    Details

    - -

    Planted in 1927. Cutting began in 1929. Yield is the weight of - asparagus cuttings up to Jun 1 in each plot. Some plots received continued - cuttings until Jun 15, Jul 1, and Jul 15.

    -

    In the past, repeated-measurement experiments like this were sometimes - analyzed as if they were a split-plot experiment. This violates some - indpendence assumptions.

    -

    Source

    - -

    Snedecor and Cochran, 1989. Statistical Methods.

    -

    References

    - -

    Mick O'Neill, 2010. - A Guide To Linear Mixed Models In An Experimental Design Context. - Statistical Advisory & Training Service Pty Ltd.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(snedecor.asparagus)
-  dat <- snedecor.asparagus
-
-  dat <- transform(dat, year=factor(year))
-  dat$trt <- factor(dat$trt,
-                    levels=c("Jun-01", "Jun-15", "Jul-01", "Jul-15"))
-
-  # Continued cutting reduces plant vigor and yield
-  libs(lattice)
-  dotplot(yield ~ trt|year, data=dat,
-          xlab="Cutting treatment", main="snedecor.asparagus")
-
    
-  # Split-plot
-  if(0){
-    libs(lme4)
-    m1 <- lmer(yield ~ trt + year + trt:year +
-                 (1|block) + (1|block:trt), data=dat)
-  }
-
-  # ----------
-
-  libs(asreml,lucid) # asreml4
-
-  # Split-plot with asreml
-  m2 <- asreml(yield ~ trt + year + trt:year, data=dat,
-               random = ~ block + block:trt)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:50 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -245.589       735.637     60 17:08:51    0.0
-#>  2      -242.013       605.606     60 17:08:51    0.0
-#>  3      -239.057       500.049     60 17:08:51    0.0
-#>  4      -237.707       441.384     60 17:08:51    0.0
-#>  5      -237.333       411.824     60 17:08:51    0.0
-#>  6      -237.307       405.253     60 17:08:51    0.0
-#>  7      -237.307       404.663     60 17:08:51    0.0
      # vc(m2)
-  ##    effect component std.error z.ratio bound 
-  ##     block     354.3     405      0.87     P 0.1
-  ## block:trt     462.8     256.9    1.8      P 0  
-  ##   units!R     404.7      82.6    4.9      P 0  
-
-  ## # Antedependence with asreml.  See O'Neill (2010).
-  dat <- dat[order(dat$block, dat$trt), ]
-  m3 <- asreml(yield ~ year * trt, data=dat,
-               random = ~ block,
-               residual = ~ block:trt:ante(year,1),
-               max=50)
-
    #> Model fitted using the sigma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:51 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -239.093           1.0     60 17:08:51    0.0
-#>  2      -237.801           1.0     60 17:08:51    0.0
-#>  3      -233.259           1.0     60 17:08:51    0.0
-#>  4      -223.667           1.0     60 17:08:51    0.0
-#>  5      -220.355           1.0     60 17:08:51    0.0
-#>  6      -220.139           1.0     60 17:08:51    0.0
-#>  7      -220.091           1.0     60 17:08:51    0.0
-#>  8      -220.079           1.0     60 17:08:51    0.0
-#>  9      -220.076           1.0     60 17:08:51    0.0
    #> Warning: Some components changed by more than 1% on the last iteration.
    
-  ## # Extract the covariance matrix for years and convert to correlation
-  ## covmat <- diag(4)
-  ## covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$`block:trt:year`$year$initial
-  ## covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)]
-  ## round(cov2cor(covmat),2) # correlation among the 4 years
-  ## #      [,1] [,2] [,3] [,4]
-  ## # [1,] 1.00 0.45 0.39 0.31
-  ## # [2,] 0.45 1.00 0.86 0.69
-  ## # [3,] 0.39 0.86 1.00 0.80
-  ## # [4,] 0.31 0.69 0.80 1.00
-
-  ## # We can also build the covariance Sigma by hand from the estimated
-  ## # variance components via: Sigma^-1 = U D^-1 U'
-  ## vv <- vc(m3)
-  ## print(vv)
-  ## ##            effect component std.error z.ratio constr
-  ## ##   block!block.var  86.56    156.9        0.55    pos
-  ## ##        R!variance   1              NA      NA    fix
-  ## ##  R!year.1930:1930   0.00233   0.00106    2.2   uncon
-  ## ##  R!year.1931:1930  -0.7169    0.4528    -1.6   uncon
-  ## ##  R!year.1931:1931   0.00116   0.00048    2.4   uncon
-  ## ##  R!year.1932:1931  -1.139     0.1962    -5.8   uncon
-  ## ##  R!year.1932:1932   0.00208   0.00085    2.4   uncon
-  ## ##  R!year.1933:1932  -0.6782    0.1555    -4.4   uncon
-  ## ##  R!year.1933:1933   0.00201   0.00083    2.4   uncon
-  
-  ## U <- diag(4)
-  ## U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2]
-  ## Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2]))
-  ## # solve(U 
-  ## solve(crossprod(t(U), tcrossprod(Dinv, U)) )
-  ## ##          [,1]      [,2]      [,3]      [,4]
-  ## ## [1,] 428.4310  307.1478  349.8152  237.2453
-  ## ## [2,] 307.1478 1083.9717 1234.5516  837.2751
-  ## ## [3,] 349.8152 1234.5516 1886.5150 1279.4378
-  ## ## [4,] 237.2453  837.2751 1279.4378 1364.8446
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/snijders.fusarium-1.png b/docs/reference/snijders.fusarium-1.png deleted file mode 100644 index 004269bf..00000000 Binary files a/docs/reference/snijders.fusarium-1.png and /dev/null differ diff --git a/docs/reference/snijders.fusarium-2.png b/docs/reference/snijders.fusarium-2.png deleted file mode 100644 index c39ceade..00000000 Binary files a/docs/reference/snijders.fusarium-2.png and /dev/null differ diff --git a/docs/reference/snijders.fusarium.html b/docs/reference/snijders.fusarium.html deleted file mode 100644 index d4c97885..00000000 --- a/docs/reference/snijders.fusarium.html +++ /dev/null @@ -1,333 +0,0 @@ - - - - - - - - -Fusarium infection in wheat varieties — snijders.fusarium • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Fusarium infection in wheat varieties

    - - -
    - -
    -

    Infection in wheat by different strains of Fusarium.

    -
    - - - -

    Format

    - -

    A data frame with 204 observations on the following 4 variables.

    -
    gen

    wheat genotype

    -
    strain

    fusarium strain

    -
    year

    year

    -
    y

    percent infected

    - -
    - -

    Details

    - - - -

    The data are the percent of leaf area affected by Fusarium head blight, - averaged over 4-5 reps, for 17 winter wheat genotypes.

    -

    Van Eeuwijk fit a generalized ammi-2 model to this data. It is a - generalized model in the sense that a link function is used, and is a - non-linear AMMI model in that there are main effects for variety and - year-strain, but additional multiplicative effects for the - interactions.

    -

    Note, the value for strain F348 in 1988, gen SVP75059-32 should be - 28.3 (as shown in VanEeuwijk 1995) and not 38.3 (as shown in Snijders - 1991).

    - -

    Used with permission of Fred van Eeuwijk.

    -

    Source

    - -

    Snijders, CHA and Van Eeuwijk, FA. 1991. - Genotype x strain interactions for resistance to Fusarium head blight - caused by Fusarium culmorum in winter wheat. - Theoretical and Applied Genetics, 81, 239--244. Table 1. - https://doi.org/10.1007/BF00215729

    -

    References

    - -

    Fred A van Eeuwijk. 1995. - Multiplicative interaction in generalized linear models. - Biometrics, 51, 1017-1032. - https://doi.org/10.2307/2533001

    - -

    Examples

    - 
    
-library(agridat)
-
-data(snijders.fusarium)
-dat <- snijders.fusarium
-
-aggregate(y ~ strain + year, dat, FUN=mean) # Match means in Snijders table 1
-
    #>    strain year         y
-#> 1    F329 1986  7.617647
-#> 2    F348 1986  8.117647
-#> 3     F39 1986 28.000000
-#> 4    F436 1986 11.500000
-#> 5    F329 1987  2.223529
-#> 6    F348 1987  1.441176
-#> 7     F39 1987 30.794118
-#> 8    F436 1987  8.452941
-#> 9    F329 1988  9.247059
-#> 10   F348 1988  7.611765
-#> 11    F39 1988 14.117647
-#> 12   F436 1988 11.276471
    
-dat <- transform(dat, y=y/100, year=factor(year), yrstr=factor(paste0(year,"-",strain)))
-
-# Strain F329 shows little variation across years. F39 shows a lot.
-libs(lattice)
-dotplot(gen~y|strain, data=dat, group=year,
-        main="snijders.fusarium : infection by strain",
-        xlab="Fraction infected", ylab="variety",
-        auto.key=list(columns=3))
-
    
-# Logit transform
-dat <- transform(dat, logit=log(y/(1-y)))
-m1 <- aov(logit ~ yrstr + gen, data=dat) # Match SS in VanEeuwijk table 4
-anova(m1)  # Match SS in VanEeuwijk table 4
-
    #> Analysis of Variance Table
-#> 
-#> Response: logit
-#>            Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> yrstr      11 224.092 20.3720  39.367 < 2.2e-16 ***
-#> gen        16 171.600 10.7250  20.725 < 2.2e-16 ***
-#> Residuals 176  91.079  0.5175                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    m2 <- aov(logit ~ year*strain + gen + gen:year + gen:strain, data=dat)
-anova(m2) # Match to VanEeuwijk table 5
-
    #> Analysis of Variance Table
-#> 
-#> Response: logit
-#>             Df  Sum Sq Mean Sq  F value    Pr(>F)    
-#> year         2  33.890  16.945  50.9711 8.226e-16 ***
-#> strain       3 130.246  43.415 130.5964 < 2.2e-16 ***
-#> gen         16 171.600  10.725  32.2615 < 2.2e-16 ***
-#> year:strain  6  59.957   9.993  30.0593 < 2.2e-16 ***
-#> year:gen    32  43.853   1.370   4.1223 4.049e-08 ***
-#> strain:gen  48  15.312   0.319   0.9596    0.5541    
-#> Residuals   96  31.914   0.332                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# \dontrun{
-
-# GLM on untransformed data using logit link, variance mu^2(1-mu)^2
-libs(gnm) # for 'wedderburn' family
-
    #> 
-#> Attaching package: 'gnm'
    #> The following object is masked from 'package:asreml':
-#> 
-#>     wheat
    #> The following object is masked from 'package:lattice':
-#> 
-#>     barley
    m2 <- glm(y ~ yrstr + gen, data=dat, family="wedderburn")
-anova(m2) # Main effects match VanEeuwijk table 6
-
    #> Analysis of Deviance Table
-#> 
-#> Model: wedderburn, link: logit
-#> 
-#> Response: y
-#> 
-#> Terms added sequentially (first to last)
-#> 
-#> 
-#>       Df Deviance Resid. Df Resid. Dev
-#> NULL                    203     369.44
-#> yrstr 11   150.85       192     218.60
-#> gen   16   145.27       176      73.33
    
-# Generalized AMMI-2 model. Matches VanEeuwijk table 6
-bilin2 <- gnm(y ~ yrstr + gen + instances(Mult(yrstr, gen), 2),
-              data=dat, family = wedderburn)
-
    #> Initialising
-#> Running start-up iterations..
-#> Running main iterations.......................................................
-#> Done
    # plot(bilin2,1) # Resid vs fitted plot matches VanEeuwijk figure 3c
-## anova(bilin2)
-##                            Df Deviance Resid. Df Resid. Dev
-## NULL                                         203     369.44
-## yrstr                      11  150.847       192     218.60
-## gen                        16  145.266       176      73.33
-## Mult(yrstr, gen, inst = 1) 26   26.128       150      47.20
-## Mult(yrstr, gen, inst = 2) 24   19.485       126      27.72
-
-# Manually extract coordinates for biplot
-cof <- coef(bilin2)
-y1 <- cof[29:40]
-g1 <- cof[41:57]
-y2 <- cof[58:69]
-g2 <- cof[70:86]
-g12 <- cbind(g1,g2)
-rownames(g12) <- substring(rownames(g12), 29)
-y12 <- cbind(y1,y2)
-rownames(y12) <- substring(rownames(y12), 31)
-g12[,1] <- -1 * g12[,1]
-y12[,1] <- -1 * y12[,1]
-# GAMMI biplot.  Inner-products of points projected onto
-# arrows match VanEeuwijk figure 4.  Slight rotation of graph is ignorable.
-biplot(y12, g12, cex=.75, main="snijders.fusarium") # Arrows to genotypes.
-
    
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/stephens.sorghum.uniformity-1.png b/docs/reference/stephens.sorghum.uniformity-1.png deleted file mode 100644 index afac980e..00000000 Binary files a/docs/reference/stephens.sorghum.uniformity-1.png and /dev/null differ diff --git a/docs/reference/stephens.sorghum.uniformity.html b/docs/reference/stephens.sorghum.uniformity.html deleted file mode 100644 index ac54d6fa..00000000 --- a/docs/reference/stephens.sorghum.uniformity.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Uniformity trial of sorghum silage — stephens.sorghum.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Uniformity trial of sorghum silage

    - - -
    - -
    -

    Uniformity trial of sorghum silage at Chillicothe, Texas, 1915.

    -
    - - - -

    Format

    - -

    A data frame with 2000 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column / rod

    -
    yield

    yield, ounces

    - -
    - -

    Details

    - -

    Grown near Chillicothe, TX in 1915. Rows 40 inches apart. - Each row harvested in 1-rod (16.5 ft) lengths. - East side higher yielding than west side. - Yields are weight (ounces) of green forage each rod-row. - Total area harvested: 100*40/12 = 333.33 feet by 20*16.5=330 feet.

    -

    Field width: 20 plots * 16.5 ft (1 rod) = 330 feet.

    -

    Field length: 100 plots * 40 in = 333 feet

    -

    Source

    - -

    Stephens, Joseph C. 1928. - Experimental methods and the probable error in field experiments with sorghum. - Journal of Agricultural Research, 37, 629--646. - https://naldc.nal.usda.gov/catalog/IND43967516

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(stephens.sorghum.uniformity)
-  dat <- stephens.sorghum.uniformity
-
-  dat <- subset(dat, row>2 & row<99) # omit outer two rows
-  # mean(dat$yield) # 180.27
-  # range(dat$yield) # 75,302 matches Stephens
-  
-  # densityplot(~dat$yield) # Stephens figure 3
-  
-  # Aggregate 4 side-by-side rows.
-  d4 <- dat
-  d4$row2 <- ceiling((d4$row-2)/4)
-  d4 <- aggregate(yield ~ row2+col, data=d4, FUN=sum)
-  d4$row2 <- 25-d4$row2 # flip horizontally
-  
-  libs(desplot)
-  grays <- colorRampPalette(c("#d9d9d9","#252525"))
-  desplot(d4, yield ~ row2*col,
-          aspect=333/330, flip=TRUE, # true aspect
-          main="stephens.sorghum.uniformity",
-          col.regions=grays(3),
-          at=c(500,680,780,1000))
-
      # Similar to Stephens Figure 7.  North at top.  East at right.
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
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    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex

    - - -
    - -
    -

    Phenotypic and genotypic data for a barley population of Steptoe x - Morex. There were 150 doubled haploid crosses, evaluated at 223 - markers. Phenotypic data wascollected on 8 traits at 16 environments.

    -
    - - 
    data("steptoe.morex.pheno")
    - - -

    Format

    - - - -

    steptoe.morex.pheno is a data.frame of phenotypic data - with 2432 observations on 10 variables:

    -
    gen

    genotype factor with parents Steptoe and Morex, - and 150 crosses SM1, SM2, ..., SM200. Not all 200 numbers were used.

    -
    env

    environment, 16 levels

    -
    amylase

    alpha amylase (20 Deg Units)

    -
    diapow

    diastatic power (degree units)

    -
    hddate

    heading date (julian days)

    -
    lodging

    lodging (percent)

    -
    malt

    malt extract (percent)

    -
    height

    plant height (centimeters)

    -
    protein

    grain protein (percent)

    -
    yield

    grain yield (Mt/Ha)

    - -
    - -

    steptoe.morex.geno is a cross object from the - qtl package with genotypic data of the 223 - markers for the 150 crosses of Steptoe x Morex.

    -

    Details

    - - - -

    As described by Hayes et al (1993), a population of 150 barley doubled - haploid (DH) lines was developed by the Oregon State University Barley - Breeding Program for the North American Barley Genome Mapping - Project. The parentage of the population is Steptoe / Morex.

    -

    Steptoe is the dominant feed barley in the northwestern U.S.

    -

    Morex is the spring U.S. malting quality standard.

    -

    Seed from a single head of each parent was used to create the F1, from which a - set of 150 lines was developed.

    -

    Phenotypic values for the parents Steptoe and Morex are here: - https://wheat.pw.usda.gov/ggpages/SxM/parental_values.html

    -

    There are 16 locations, The average across locations is in column 17. Not all - traits were collected at every location. At each location, all 150 lines were - included in block 1, a random subset of 50 lines was used in block 2.

    -

    The traits are: - Alpha Amylase (20 Deg Units), - Diastatic Power (Deg Units), - Heading Date (Julian Days), - Lodging (percent), - Malt Extract (percent), - Grain Protein (percent), - Grain Yield (Mt/Ha).

    -

    Phenotypic values of the 150 lines in the F1 population are here: - https://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html

    -

    Each trait is in a different file, in which each block of numbers - represents one location.

    -

    The 223-markers Steptoe/Morex base map is here: - https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map

    -

    The data for these markers on the 150 lines is - https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk

    -

    These were hand-assembled (e.g. marker distances were cumulated to - marker positions) into a .csv file which was then imported into - R using qtl::read.cross. The class was manually changed from - c('bc','cross') to c('dh','cross').

    -

    The marker data is coded as A = Steptoe, B = Morex, - = missing.

    -

    The pedigrees for the 150 lines are found here: - https://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html

    -

    Source

    - -

    The Steptoe x Morex Barley Mapping Population. - Map: Version 2, August 1, 1995 - https://wheat.pw.usda.gov/ggpages/SxM. Accessed Jan 2015.

    -

    Data provided by the United States Department of Agriculture.

    -

    References

    - - - -

    P.M. Hayes, B.H. Liu, S.J. Knapp, F. Chen, B. Jones, T. Blake, - J. Franckowiak, D. Rasmusson, M. Sorrells, S.E. Ullrich, and others. 1993. - Quantitative trait locus effects and environmental interaction in a sample of - North American barley germplasm. - Theoretical and Applied Genetics, 87, 392--401. - https://doi.org/10.1007/BF01184929

    -

    Ignacio Romagosa, Steven E. Ullrich, Feng Han, Patrick M. Hayes. 1996. - Use of the additive main effects and multiplicative interaction model - in QTL mapping for adaptation in barley. - Theor Appl Genet, 93, 30-37. - https://doi.org/10.1007/BF00225723

    -

    Piepho, Hans-Peter. 2000. - A mixed-model approach to mapping quantitative trait loci in barley on - the basis of multiple environment data. - Genetics, 156, 2043-2050.

    -

    M. Malosetti, J. Voltas, I. Romagosa, S.E. Ullrich, F.A. van Eeuwijk. (2004). - Mixed models including environmental covariables for studying QTL by - environment interaction. - Euphytica, 137, 139-145. - https://doi.org/10.1023/B:EUPH.0000040511.4638

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(steptoe.morex.pheno)
-  dat <- steptoe.morex.pheno
-
-  # Visualize GxE of traits
-  libs(lattice)
-  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-  levelplot(amylase~env*gen, data=dat, col.regions=redblue,
-            scales=list(x=list(rot=90)), main="amylase")
-
      ## levelplot(diapow~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="diapow")
-  ## levelplot(hddate~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="hddate")
-  ## levelplot(lodging~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="lodging")
-  ## levelplot(malt~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="malt")
-  ## levelplot(height~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="height")
-  ## levelplot(protein~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="protein")
-  ## levelplot(yield~env*gen, data=dat, col.regions=redblue,
-  ##           scales=list(x=list(rot=90)), main="yield")
-
-
-  # Calculate avg yield for each loc as in Romagosa 1996, table 3
-  # t(t(round(tapply(dat$yield, dat$env, FUN=mean),2)))
-  # SKo92,SKg92 means in table 3 are switched.  Who is right, him or me?
-
-  # Draw marker map
-  libs(qtl)
-  data(steptoe.morex.geno)
-  datg <- steptoe.morex.geno
-  plot.map(datg, main="steptoe.morex.pheno")  # or just use plot()
-
    
-
-  # This is a very rudimentary example.
-    
-  # Fit a simple multi-environment mixed model
-  libs(asreml)
-  m1 <- asreml(yield ~ env, data=dat, random=~gen)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:12 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -826.109      0.664231   2416 17:50:13    0.0
-#>  2      -821.409      0.656597   2416 17:50:13    0.0
-#>  3      -817.325      0.647509   2416 17:50:13    0.0
-#>  4      -815.816      0.641196   2416 17:50:13    0.0
-#>  5      -815.601      0.638204   2416 17:50:13    0.0
-#>  6      -815.600      0.637998   2416 17:50:13    0.0
      
-  libs(wgaim)
-  qtl::plotMissing(datg)
-
      wgaim::linkMap(datg)
-
      # Create an interval object for wgaim
-  dati <- wgaim::cross2int(datg, id="gen")
-  
-  # Whole genome qtl
-  q1 <- wgaim::wgaim(m1, intervalObj=dati, merge.by="gen", na.action=na.method(x="include"))
-
    #> Error in eval(baseModel$call$data): object 'dat' not found
      #wgaim::linkMap(q1, dati) # Visualize
-  wgaim::outStat(q1, dati) # outlier statistic
-
    #> Error in wgaim::outStat(q1, dati): object 'q1' not found
      summary(q1, dati)  # Table of important intervals
-
    #> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'q1' not found
      # Chrom Left Marker dist(cM) Right Marker dist(cM)   Size Pvalue 
-  #     3      ABG399     52.6       BCD828     56.1  0.254  0.000  45.0
-  #     5      MWG912      148      ABG387A    151.2  0.092  0.001   5.9
-  #     6     ABC169B     64.8       CDO497     67.5 -0.089  0.001   5.6
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/stickler.sorghum.uniformity-1.png b/docs/reference/stickler.sorghum.uniformity-1.png deleted file mode 100644 index 1c7dd2dd..00000000 Binary files a/docs/reference/stickler.sorghum.uniformity-1.png and /dev/null differ diff --git a/docs/reference/stickler.sorghum.uniformity-2.png b/docs/reference/stickler.sorghum.uniformity-2.png deleted file mode 100644 index 8a5e7eb7..00000000 Binary files a/docs/reference/stickler.sorghum.uniformity-2.png and /dev/null differ diff --git a/docs/reference/stickler.sorghum.uniformity.html b/docs/reference/stickler.sorghum.uniformity.html deleted file mode 100644 index 3cae6241..00000000 --- a/docs/reference/stickler.sorghum.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trial of sorghum — stickler.sorghum.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Uniformity trial of sorghum

    - - -
    - -
    -

    Uniformity trial of sorghum in at Manhattan, Kansas, 1958-1959.

    -
    - - 
    data("stickler.sorghum.uniformity")
    - - -

    Format

    - -

    A data frame with 1600 observations on the following 4 variables.

    -
    expt

    experiment

    -
    row

    row

    -
    col

    col

    -
    yield

    yield

    - -
    - -

    Details

    - - - -

    Four sorghum experiments at the Agronomy Farm at Manhattan, - Kansas. Experiments E1,E2 grown in 1958. Expts E3,E5 grown in 1959.

    -

    Experiment E1.

    -

    Field width = 20 units * 14 inches = 23.3 ft.

    -

    Field length = 20 units * 10 feet = 200 feet.

    -

    Experiment E2-E3.

    -

    Field width = 20 units * 44 inches = 73 feet

    -

    Field length = 20 units * 5 ft = 100 feet.

    -

    Source

    - -

    F. C. Stickler (1960). - Estimates of Optimum Plot Size from Grain Sorghum Uniformity Trial Data. - Technical bulletin, Kansas Agricultural Experiment Station.

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(stickler.sorghum.uniformity)
-  dat <- stickler.sorghum.uniformity
-  
-  dat1 <- subset(dat, expt=="E1")
-  dat2 <- subset(dat, expt!="E1")
-  
-  libs(desplot)
-  desplot(dat, yield ~ col*row|expt,
-          subset=expt=="E1",
-          #cex=1,text=yield, shorten="none",
-          xlab="row",ylab="range",
-          flip=TRUE, tick=TRUE, aspect=(20*10)/(20*14/12), # true aspect
-          main="stickler.sorghum.uniformity: expt E1")
-
    
-  desplot(dat, yield ~ col*row|expt,
-          subset=expt!="E1",
-          xlab="row",ylab="range",
-          flip=TRUE, tick=TRUE, aspect=(20*5)/(20*44/12), # true aspect
-          main="stickler.sorghum.uniformity: expt E2,E3,E4")
-
    
-  # Stickler, p. 10-11 has
-  #    E1    E2    E3    E4
-  # 34.81 11.53 11.97 14.10 
-  cv <- function(x) 100*sd(x)/mean(x)
-  tapply(dat$yield, dat$expt, cv)
-
    #>       E1       E2       E3       E4 
-#> 35.74653 11.55062 11.97011 14.11389 
      # 35.74653 11.55062 11.97011 14.11389
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/stirret.borers-1.png b/docs/reference/stirret.borers-1.png deleted file mode 100644 index 6f48d9eb..00000000 Binary files a/docs/reference/stirret.borers-1.png and /dev/null differ diff --git a/docs/reference/stirret.borers-2.png b/docs/reference/stirret.borers-2.png deleted file mode 100644 index dc229c0f..00000000 Binary files a/docs/reference/stirret.borers-2.png and /dev/null differ diff --git a/docs/reference/stirret.borers.html b/docs/reference/stirret.borers.html deleted file mode 100644 index da837857..00000000 --- a/docs/reference/stirret.borers.html +++ /dev/null @@ -1,235 +0,0 @@ - - - - - - - - -Corn borer control by application of fungal spores. — stirret.borers • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Corn borer control by application of fungal spores.

    - - -
    - -
    -

    Corn borer control by application of fungal spores.

    -
    - - - -

    Format

    - -

    A data frame with 60 observations on the following 4 variables.

    -
    block

    block, 15 levels

    -
    trt

    treatment, 4 levels

    -
    count1

    count of borers on August 18

    -
    count2

    count of borers on October 19

    - -
    - -

    Details

    - -

    Experiment conducted in 1935, Ottawa. European corn borer infestation - was established by application of egg masses to plants. - Treatments were applied on July 8 and July 19 at two levels, 0 and 40 - grams per acre. The number of borers per plot were counted on - Aug 18 and Oct 19.

    -

    Source

    - -

    Stirrett, George M and Beall, Geoffrey and Timonin, M. (1937). - A field experiment on the control of the European corn borer, Pyrausta - nubilalis Hubn, by Beauveria bassiana Vuill. - Sci. Agric., 17, 587--591. Table 2.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(stirret.borers)
-  dat <- stirret.borers
-
-  libs(lattice)
-  xyplot(count2~count1|trt,dat,
-         main="stirret.borers - by treatment",
-         xlab="Early count of borers", ylab="Late count")
-
      
-  # Even though the data are counts, Normal distribution seems okay
-  # qqmath(~count1|trt, dat, main="stirret.borers")
-  
-  m1 <- lm(count1 ~ -1 + trt + block, dat)
-  anova(m1)
-
    #> Analysis of Variance Table
-#> 
-#> Response: count1
-#>           Df Sum Sq Mean Sq  F value    Pr(>F)    
-#> trt        4 174970   43742 185.2950 < 2.2e-16 ***
-#> block     14  16148    1153   4.8861  3.06e-05 ***
-#> Residuals 42   9915     236                       
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      # predicted means = main effect + average of 15 block effects
-  # note block 1 effect is 0
-  # coef(m1)[1:4] + sum(coef(m1)[-c(1:4)])/15
-  ##  trtBoth trtEarly  trtLate  trtNone 
-  ## 47.86667 62.93333 40.93333 61.13333 
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/streibig.competition-1.png b/docs/reference/streibig.competition-1.png deleted file mode 100644 index 2dc2f9b8..00000000 Binary files a/docs/reference/streibig.competition-1.png and /dev/null differ diff --git a/docs/reference/streibig.competition-2.png b/docs/reference/streibig.competition-2.png deleted file mode 100644 index 9ee70f34..00000000 Binary files a/docs/reference/streibig.competition-2.png and /dev/null differ diff --git a/docs/reference/streibig.competition.html b/docs/reference/streibig.competition.html deleted file mode 100644 index 64def2bd..00000000 --- a/docs/reference/streibig.competition.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - - -Competition experiment between barley and sinapis. — streibig.competition • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Competition experiment between barley and sinapis.

    - - -
    - -
    -

    Competition experiment between barley and sinapis, at different - planting rates.

    -
    - - - -

    Format

    - -

    A data frame with 135 observations on the following 8 variables.

    -
    pot

    pot number

    -
    bseeds

    barley seeds sown

    -
    sseeds

    sinapis seeds sown

    -
    block

    block

    -
    bfwt

    barley fresh weight

    -
    sfwt

    sinapis fresh weight

    -
    bdwt

    barley dry weight

    -
    sdwt

    sinapis dry weight

    - -
    - -

    Details

    - -

    The source data (in McCullagh) also contains a count of plants - harvested (not included here) that sometimes is greater than the - number of seeds planted.

    -

    Used with permission of Jens Streibig.

    -

    Source

    - -

    Peter McCullagh, John A. Nelder. - Generalized Linear Models, page 318-320.

    -

    References

    - -

    Oliver Schabenberger and Francis J Pierce. 2002. - Contemporary Statistical Models for the Plant and Soil Sciences. - CRC Press. Page 370-375.

    - -

    Examples

    - 
    # \dontrun{
-  
-library(agridat)
-
-data(streibig.competition)
-dat <- streibig.competition
-
-# See Schaberger and Pierce, pages 370+
-# Consider only the mono-species barley data (no competition from sinapis)
-d1 <- subset(dat, sseeds<1)
-d1 <- transform(d1, x=bseeds, y=bdwt, block=factor(block))
-
-# Inverse yield looks like it will be a good fit for Gamma's inverse link
-libs(lattice)
-xyplot(1/y~x, data=d1, group=block, auto.key=list(columns=3),
-       xlab="Seeding rate", ylab="Inverse yield of barley dry weight",
-       main="streibig.competition")
-
    
-# linear predictor is quadratic, with separate intercept and slope per block
-m1 <- glm(y ~ block + block:x + x+I(x^2), data=d1,
-          family=Gamma(link="inverse"))
-# Predict and plot
-newdf <- expand.grid(x=seq(0,120,length=50), block=factor(c('B1','B2','B3')) )
-newdf$pred <- predict(m1, new=newdf, type='response')
-plot(y~x, data=d1, col=block, main="streibig.competition - by block",
-     xlab="Barley seeds", ylab="Barley dry weight")
-
    for(bb in 1:3){
-  newbb <- subset(newdf, block==c('B1','B2','B3')[bb])
-  lines(pred~x, data=newbb, col=bb)
-}
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/strickland.apple.uniformity-1.png b/docs/reference/strickland.apple.uniformity-1.png deleted file mode 100644 index 43faf105..00000000 Binary files a/docs/reference/strickland.apple.uniformity-1.png and /dev/null differ diff --git a/docs/reference/strickland.apple.uniformity.html b/docs/reference/strickland.apple.uniformity.html deleted file mode 100644 index 8c589d2a..00000000 --- a/docs/reference/strickland.apple.uniformity.html +++ /dev/null @@ -1,215 +0,0 @@ - - - - - - - - -Uniformity trial in apple — strickland.apple.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial in apple

    - - -
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    Uniformity trial in apple in Australia

    -
    - - 
    data("strickland.apple.uniformity")
    - - -

    Format

    - -

    A data frame with 198 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per tree, pounds

    - -
    - -

    Details

    - -

    Some recently re-worked trees were removed from the data.

    -

    The distance between trees in uncertain, but likely in the range 20-30 - feet.

    -

    Source

    - -

    A. G. Strickland (1935). - Error in horticultural experiments. - Journal of Agriculture, Victoria, 33, 408-416. - https://handle.slv.vic.gov.au/10381/386642

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  data(strickland.apple.uniformity)
-  dat <- strickland.apple.uniformity
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          main="strickland.apple.uniformity",
-          flip=TRUE, aspect=(18/11))
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/strickland.grape.uniformity-1.png b/docs/reference/strickland.grape.uniformity-1.png deleted file mode 100644 index 16798d91..00000000 Binary files a/docs/reference/strickland.grape.uniformity-1.png and /dev/null differ diff --git a/docs/reference/strickland.grape.uniformity.html b/docs/reference/strickland.grape.uniformity.html deleted file mode 100644 index 66ce455a..00000000 --- a/docs/reference/strickland.grape.uniformity.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - -Uniformity trial of grape — strickland.grape.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - -
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    Uniformity trial of grape

    - - -
    - -
    -

    Uniformity trial of grape in Australia

    -
    - - 
    data("strickland.grape.uniformity")
    - - -

    Format

    - -

    A data frame with 155 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per vine, pounds

    - -
    - -

    Details

    - -

    Yields of individual grape vines, planted 8 feet apart in rows 10 feet - apart. - Grown in Rutherglen, North-East Victoria, Australia, 1930.

    -

    Certain sections were omitted because of missing vines.

    -

    Source

    - -

    A. G. Strickland (1932). - A vine uniformity trial. - Journal of Agriculture, Victoria, 30, 584-593. - https://handle.slv.vic.gov.au/10381/386462

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(strickland.grape.uniformity)
-dat <- strickland.grape.uniformity
-
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          main="strickland.grape.uniformity",
-          flip=TRUE, aspect=(31*8)/(5*10) )
-
      
-  # CV 43.4
-  sd(dat$yield, na.rm=TRUE)/mean(dat$yield, na.rm=TRUE)
-
    #> [1] 0.4348955
      
-  # anova like Strickland, appendix 1
-  anova(aov(yield ~ factor(row) + factor(col), data=dat))
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>             Df Sum Sq Mean Sq F value    Pr(>F)    
-#> factor(row) 24 130.71   5.446  1.3796    0.1383    
-#> factor(col)  4 318.93  79.732 20.1975 4.281e-12 ***
-#> Residuals   96 378.97   3.948                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-  # numbers ending in .5 much more common than .0
-  # table(substring(format(na.omit(dat$yield)),4,4))
-  #  0   5 
-  # 25 100 
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/strickland.peach.uniformity-1.png b/docs/reference/strickland.peach.uniformity-1.png deleted file mode 100644 index afddafd1..00000000 Binary files a/docs/reference/strickland.peach.uniformity-1.png and /dev/null differ diff --git a/docs/reference/strickland.peach.uniformity.html b/docs/reference/strickland.peach.uniformity.html deleted file mode 100644 index 3d3bb081..00000000 --- a/docs/reference/strickland.peach.uniformity.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - - -Uniformity trial of peach — strickland.peach.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
    -
    -
    -

    Uniformity trial of peach

    - - -
    - -
    -

    Uniformity trial of peach trees in Australia.

    -
    - - 
    data("strickland.peach.uniformity")
    - - -

    Format

    - -

    A data frame with 144 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, pounds per tree

    - -
    - -

    Details

    - -

    Yields are the weight of peaches per individual tree in pounds.

    -

    Source

    - -

    A. G. Strickland (1935). - Error in horticultural experiments. - Journal of Agriculture, Victoria, 33, 408-416. - https://handle.slv.vic.gov.au/10381/386642

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(strickland.peach.uniformity)
-  dat <- strickland.peach.uniformity
-
-  mean(dat$yield) # 131.3, Strickland has 131.3
-
    #> [1] 131.2917
      sd(dat$yield)/mean(dat$yield) # 31.1, Strickland has 34.4
-
    #> [1] 0.3113755
    
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          main="strickland.peach.uniformity",
-          flip=TRUE, aspect=1)
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/strickland.tomato.uniformity-1.png b/docs/reference/strickland.tomato.uniformity-1.png deleted file mode 100644 index 1f28cd80..00000000 Binary files a/docs/reference/strickland.tomato.uniformity-1.png and /dev/null differ diff --git a/docs/reference/strickland.tomato.uniformity.html b/docs/reference/strickland.tomato.uniformity.html deleted file mode 100644 index 1a88f7d6..00000000 --- a/docs/reference/strickland.tomato.uniformity.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - -Uniformity trial of tomato — strickland.tomato.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    -

    Uniformity trial of tomato

    - - -
    - -
    -

    Uniformity trial of tomato in Australia

    -
    - - 
    data("strickland.tomato.uniformity")
    - - -

    Format

    - -

    A data frame with 180 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield per plot, pounds

    - -
    - -

    Details

    - -

    Tomato plants were placed 2 feet apart in rows 4 feet apart. Each plot - contained 6 plants.

    -

    Field dimensions are not given, but the most likely design is:

    -

    Field length: 6 plots * 6 plants * 2 feet = 72 feet

    -

    Field width: 30 plots * 4 feet = 120 feet

    -

    Source

    - -

    A. G. Strickland (1935). - Error in horticultural experiments. - Journal of Agriculture, Victoria, 33, 408-416. - https://handle.slv.vic.gov.au/10381/386642

    -

    References

    - -

    None

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  data(strickland.tomato.uniformity)
-  dat <- strickland.tomato.uniformity
-
-  mean(dat$yield)
-
    #> [1] 50.47222
      sd(dat$yield)
-
    #> [1] 10.49843
    
-  libs(desplot)
-  desplot(dat, yield ~ col*row,
-          main="strickland.tomato.uniformity",
-          flip=TRUE, aspect=(6*12)/(30*4))
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/stroup.nin-1.png b/docs/reference/stroup.nin-1.png deleted file mode 100644 index d6d3fca3..00000000 Binary files a/docs/reference/stroup.nin-1.png and /dev/null differ diff --git a/docs/reference/stroup.nin-2.png b/docs/reference/stroup.nin-2.png deleted file mode 100644 index f344dde4..00000000 Binary files a/docs/reference/stroup.nin-2.png and /dev/null differ diff --git a/docs/reference/stroup.nin-3.png b/docs/reference/stroup.nin-3.png deleted file mode 100644 index 6b15c9ab..00000000 Binary files a/docs/reference/stroup.nin-3.png and /dev/null differ diff --git a/docs/reference/stroup.nin-4.png b/docs/reference/stroup.nin-4.png deleted file mode 100644 index a0da63b3..00000000 Binary files a/docs/reference/stroup.nin-4.png and /dev/null differ diff --git a/docs/reference/stroup.nin-5.png b/docs/reference/stroup.nin-5.png deleted file mode 100644 index 8921906c..00000000 Binary files a/docs/reference/stroup.nin-5.png and /dev/null differ diff --git a/docs/reference/stroup.nin.html b/docs/reference/stroup.nin.html deleted file mode 100644 index b19cd782..00000000 --- a/docs/reference/stroup.nin.html +++ /dev/null @@ -1,467 +0,0 @@ - - - - - - - - -RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
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    -

    RCB experiment of wheat at the Nebraska Intrastate Nursery

    - - -
    - -
    -

    The yield data from an advanced Nebraska Intrastate Nursery (NIN) breeding - trial conducted at Alliance, Nebraska, in 1988/89.

    -
    - - - -

    Format

    - -

    -
    gen

    genotype, 56 levels

    -
    rep

    replicate, 4 levels

    -
    yield

    yield, bu/ac

    -
    col

    column

    -
    row

    row

    - -
    - -

    Details

    - - - -

    Four replicates of 19 released cultivars, 35 experimental - wheat lines and 2 additional triticale lines were laid out in a 22 row - by 11 column rectangular array of plots. The varieties were allocated - to the plots using a randomised complete block (RCB) design. - Each plot was sown in four rows 4.3 m long and 0.3 m apart. - Plots were trimmed down to 2.4 m in length before harvest. The - orientation of the plots is not clear from the paper, but the data in - Littel et al are given in meters and make the orientation clear.

    -

    Field length: 11 plots * 4.3 m = 47.3 m

    -

    Field width: 22 plots * 1.2 m = 26.4 m

    -

    All plots with missing data are coded as being gen = "Lancer". - (For ASREML, missing plots need to be included for spatial analysis and the - level of 'gen' needs to be one that is already in the data.)

    -

    These data were first analyzed by Stroup et al (1994) and subsequently - by Littell et al (1996, page 321), Pinheiro and Bates (2000, page 260), - and Butler et al (2004).

    -

    This version of the data give the yield in bushels per acre. The yield - values published in Stroup et al (1994) are expressed in kg/ha. For - wheat, 1 bu/ac = 67.25 kg/ha.

    -

    Some of the gen names are different in Stroup et al (1994). - (Sometimes an experimental genotype is given a new name when it is - released for commercial use.) At a minimum, the following differences - in gen names should be noted:

    - - - -
    stroup.ninStroup et al
    NE83498Rawhide
    KS831374Karl
    - - -

    Some published versions of the data use long/lat instead of col/row. - To obtain the correct value of 'long', multiply 'col' by 1.2. - To obtain the correct value of 'lat', multiply 'row' by 4.3.

    -

    Relatively low yields were clustered in the northwest corner, which is - explained by a low rise in this part of the field, causing increased - exposure to winter kill from wind damage and thus depressed yield. - The genotype 'Buckskin' is a known superior variety, but was - disadvantaged by assignment to unfavorable locations within the - blocks.

    -

    Note that the figures in Stroup 2002 claim to be based on this data, - but the number of rows and columns are both off by 1 and the positions - of Buckskin as shown in Stroup 2002 do not appear to be quite right.

    -

    Source

    - -

    Stroup, Walter W., P Stephen Baenziger, Dieter K Mulitze (1994) - Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods. - Crop Science, 86:62--66. - https://doi.org/10.2135/cropsci1994.0011183X003400010011x

    -

    References

    - - - -

    Littell, R.C. and Milliken, G.A. and Stroup, W.W. and Wolfinger, - R.D. 1996. SAS system for mixed models, SAS Institute, Cary, NC.

    -

    Jose Pinheiro and Douglas Bates, 2000, - Mixed Effects Models in S and S-Plus, Springer.

    -

    Butler, D., B R Cullis, A R Gilmour, B J Goegel. (2004) - Spatial Analysis Mixed Models for S language environments

    -

    W. W. Stroup (2002). - Power Analysis Based on Spatial Effects Mixed Models: A Tool for - Comparing Design and Analysis Strategies in the Presence of Spatial - Variability. Journal of Agricultural, Biological, and - Environmental Statistics, 7(4), 491-511. - https://doi.org/10.1198/108571102780

    -

    See also

    - -

    Identical data (except for the missing values) are available - in the nlme package as Wheat2.

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(stroup.nin)
-  dat <- stroup.nin
-
-  # Experiment layout. All "Buckskin" plots are near left side and suffer
-  # from poor fertility in two of the reps.
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          aspect=47.3/26.4, out1="rep", num=gen, cex=0.6, # true aspect
-          main="stroup.nin - yield heatmap (true shape)")
-
    
-  # Dataframe to hold model predictions
-  preds <- data.frame(gen=levels(dat$gen))
-
-
-  # -----
-  # nlme
-  libs(nlme)
-  # Random RCB model
-  lme1 <- lme(yield ~ 0 + gen, random=~1|rep, data=dat, na.action=na.omit)
-  preds$lme1 <- fixef(lme1)
-
-  # Linear (Manhattan distance) correlation model
-  lme2 <- gls(yield ~ 0 + gen, data=dat,
-              correlation = corLin(form = ~ col + row, nugget=TRUE),
-              na.action=na.omit)
-  preds$lme2 <- coef(lme2)
-
-  # Random block and spatial correlation.
-  # Note: corExp and corSpher give nearly identical results
-  lme3 <- lme(yield ~ 0 + gen, data=dat,
-              random = ~ 1 | rep,
-              correlation = corExp(form = ~ col + row),
-              na.action=na.omit)
-  preds$lme3 <- fixef(lme3)
-
-  # AIC(lme1,lme2,lme3) # lme2 is lowest
-  ##      df      AIC
-  ## lme1 58 1333.702
-  ## lme2 59 1189.135
-  ## lme3 59 1216.704
-
-
-  # -----
-  # SpATS
-  libs(SpATS)
-
    #> 
-#> Attaching package: 'SpATS'
    #> The following object is masked from 'package:gstat':
-#> 
-#>     variogram
    
-  dat <- transform(dat, yf = as.factor(row), xf = as.factor(col))
-
-  # what are colcode and rowcode???
-  sp1 <- SpATS(response = "yield",
-               spatial = ~ SAP(col, row, nseg = c(10,20), degree = 3, pord = 2),
-               genotype = "gen",
-               #fixed = ~ colcode + rowcode,
-               random = ~ yf + xf,
-               data = dat,
-               control = list(tolerance = 1e-03))
-
    #> Effective dimensions
-#> -------------------------
-#> It.     Deviance          yf          xff(col,row)|colf(col,row)|row
-#>   1  2608.971235       0.495       1.195      10.143      13.395
-#>   2   738.685475       0.228       1.592       9.461      12.400
-#>   3   738.061019       0.128       2.053       9.192      11.898
-#>   4   737.719349       0.082       2.560       9.110      11.663
-#>   5   737.485972       0.056       3.090       9.110      11.567
-#>   6   737.314054       0.040       3.611       9.144      11.541
-#>   7   737.188760       0.029       4.098       9.189      11.550
-#>   8   737.101522       0.022       4.527       9.234      11.574
-#>   9   737.044016       0.017       4.889       9.274      11.602
-#>  10   737.008035       0.013       5.183       9.307      11.629
-#>  11   736.986502       0.010       5.413       9.334      11.654
-#>  12   736.974058       0.007       5.588       9.355      11.674
-#>  13   736.967044       0.006       5.720       9.372      11.690
-#>  14   736.963152       0.004       5.817       9.385      11.702
-#>  15   736.961005       0.003       5.887       9.395      11.712
-#>  16   736.959818       0.003       5.938       9.402      11.720
-#>  17   736.959154       0.002       5.975       9.408      11.725
-#> Timings:
-#> SpATS 0.36 seconds
-#> All process 0.42 seconds
      #plot(sp1)
-  preds$spats <- predict(sp1, which="gen")$predicted.value
-
-
-  # -----
-  # Template Model Builder
-  # See the ar1xar1 example:
-  # https://github.com/kaskr/adcomp/tree/master/TMB/inst/examples
-  # This example uses dpois() in the cpp file to model a Poisson response
-  # with separable AR1xAR1.  I think this example could be used for the
-  # stroup.nin data, changing dpois() to something Normal.
-
-
-  # -----
-  # asreml4
-  libs(asreml,lucid)
-
-  # RCB analysis
-  as1 <- asreml(yield ~ gen, random = ~ rep, data=dat,
-                na.action=na.method(x="omit"))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:20 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -454.807       50.3285    168 17:50:20    0.0
-#>  2      -454.663       50.1198    168 17:50:20    0.0
-#>  3      -454.532       49.8684    168 17:50:20    0.0
-#>  4      -454.472       49.6374    168 17:50:20    0.0
-#>  5      -454.469       49.5854    168 17:50:20    0.0
      preds$asreml1 <- predict(as1, data=dat, classify="gen")$pvals$predicted.value
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:20 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -454.469       49.5824    168 17:50:20    0.0
-#>  2      -454.469       49.5824    168 17:50:20    0.0
-#>  3      -454.469       49.5824    168 17:50:20    0.0
    
-  # Two-dimensional AR1xAR1 spatial model
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  dat <- dat[order(dat$xf, dat$yf),]
-  as2 <- asreml(yield~gen, data=dat,
-                residual = ~ar1(xf):ar1(yf),
-                na.action=na.method(x="omit"))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:21 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -449.818       49.7753    168 17:50:21    0.0
-#>  2      -424.315       40.2331    168 17:50:21    0.0
-#>  3      -405.419       38.9218    168 17:50:21    0.0
-#>  4      -400.288       43.0115    168 17:50:21    0.0
-#>  5      -399.368       47.2602    168 17:50:21    0.0
-#>  6      -399.326       48.3882    168 17:50:21    0.0
-#>  7      -399.324       48.6371    168 17:50:21    0.0
-#>  8      -399.324       48.6942    168 17:50:21    0.0
      preds$asreml2 <- predict(as2, data=dat, classify="gen")$pvals$predicted.value
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:21 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -399.324       48.7081    168 17:50:21    0.0
-#>  2      -399.324       48.7092    168 17:50:21    0.0
-#>  3      -399.324       48.7119    168 17:50:21    0.0
    
-  lucid::vc(as2)
-
    #>        effect component std.error z.ratio bound %ch
-#>       xf:yf!R   48.69     7.152       6.8     P   0
-#>  xf:yf!xf!cor    0.6554   0.05638    12       U   0
-#>  xf:yf!yf!cor    0.4375   0.0806      5.4     U   0
      ##     effect component std.error z.ratio constr
-  ## R!variance   48.7      7.155       6.8    pos
-  ##   R!xf.cor    0.6555   0.05638    12      unc
-  ##   R!yf.cor    0.4375   0.0806      5.4    unc
-
-  # Compare the estimates from the two asreml models.
-  # We see that Buckskin has correctly been shifted upward by the spatial model
-  plot(preds$as1, preds$as2, xlim=c(13,37), ylim=c(13,37),
-       xlab="RCB", ylab="AR1xAR1", type='n')
-
      title("stroup.nin: Comparison of predicted values")
-
      text(preds$asreml1, preds$asreml2, preds$gen, cex=0.5)
-
      abline(0,1)
-
    
-
-  # -----
-  # sommer
-  # Fixed gen, random row, col, 2D spline
-  libs(sommer)
-
    #> Loading required package: crayon
    #> Registered S3 method overwritten by 'sommer':
-#>   method          from
-#>   print.wald.test aod 
    #> 
-#> Attaching package: 'sommer'
    #> The following object is masked from 'package:aod':
-#> 
-#>     wald.test
    #> The following object is masked from 'package:spaMM':
-#> 
-#>     AR1
      dat <- transform(dat, yf = as.factor(row), xf = as.factor(col))
-  so1 <- mmer(yield ~ 0+gen,
-              random = ~ vs(xf) + vs(yf) + vs(spl2D(row,col)),
-              data=dat)
-
    #> iteration    LogLik     wall    cpu(sec)   restrained
-#>     1      -52.5235   17:50:21      0           0
-#>     2      -25.4345   17:50:21      0           0
-#>     3      -24.9668   17:50:21      0           0
-#>     4      -24.523   17:50:21      0           0
-#>     5      -24.1879   17:50:21      0           0
-#>     6      -23.9594   17:50:21      0           0
-#>     7      -23.8243   17:50:21      0           0
-#>     8      -23.7531   17:50:21      0           0
-#>     9      -23.7166   17:50:21      0           0
-#>     10      -23.6964   17:50:21      0           0
-#>     11      -23.6839   17:50:22      1           0
-#>     12      -23.6751   17:50:22      1           0
-#>     13      -23.6684   17:50:22      1           0
-#>     14      -23.663   17:50:22      1           0
-#>     15      -23.6586   17:50:22      1           0
-#>     16      -23.6548   17:50:22      1           0
-#>     17      -23.6515   17:50:22      1           0
-#>     18      -23.6486   17:50:22      1           0
-#>     19      -23.646   17:50:22      1           0
-#>     20      -23.6436   17:50:22      1           0
      preds$so1 <- coef(so1)[,"Estimate"]
-  # spatPlot
-
-  # -----
-  # compare variety effects from different packages
-  lattice::splom(preds[,-1], main="stroup.nin")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/stroup.splitplot-1.png b/docs/reference/stroup.splitplot-1.png deleted file mode 100644 index 76638f9f..00000000 Binary files a/docs/reference/stroup.splitplot-1.png and /dev/null differ diff --git a/docs/reference/stroup.splitplot.html b/docs/reference/stroup.splitplot.html deleted file mode 100644 index 5f1bd3e2..00000000 --- a/docs/reference/stroup.splitplot.html +++ /dev/null @@ -1,451 +0,0 @@ - - - - - - - - -Split-plot experiment of simulated data — stroup.splitplot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Split-plot experiment of simulated data

    - - -
    - -
    -

    A simulated dataset of a very simple split-plot experiment, used to - illustrate the details of calculating predictable functions (broad - space, narrow space, etc.).

    -

    For example, the density of narrow, intermediate and broad-space - predictable function for factor level A1 is shown below (html help only) - Figure: stroup.splitplot.png

    -
    - - - -

    Format

    - -

    -
    y

    simulated response

    -
    rep

    replicate, 4 levels

    -
    b

    sub-plot, 2 levels

    -
    a

    whole-plot, 3 levels

    - -
    - -

    Used with permission of Walt Stroup.

    -

    Source

    - -

    Walter W. Stroup, 1989. - Predictable functions and prediction space in the mixed model procedure. - Applications of Mixed Models in Agriculture and Related Disciplines.

    -

    References

    - -

    Wolfinger, R.D. and Kass, R.E., 2000. - Nonconjugate Bayesian analysis of variance component models, - Biometrics, 56, 768--774. - https://doi.org/10.1111/j.0006-341X.2000.00768.x

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(stroup.splitplot)
-  dat <- stroup.splitplot
-
-  # ---- lme4 -----
-  # libs(lme4)
-  # m0 <- lmer(y~ -1 + a + b + a:b + (1|rep) + (1|a:rep), data=dat)
-  # No predict function
-  
-  # ----- nlme -----
-  # libs(nlme)
-  # m0 <- lme(y ~ -1 + a + b + a:b, data=dat, random = ~ 1|rep/a)
-  
-  # ----- ASREML model -----
-  libs(asreml)
-  m1 <- asreml(y~ -1 + a + b + a:b, random=~ rep + a:rep, data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:27 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -50.5227       54.2060     18 17:50:27    0.0
-#>  2      -47.0112       23.4186     18 17:50:27    0.0
-#>  3      -44.8607       14.0938     18 17:50:27    0.0
-#>  4      -43.7727       11.1500     18 17:50:27    0.0
-#>  5      -43.3895        9.8532     18 17:50:27    0.0
-#>  6      -43.3415        9.4390     18 17:50:27    0.0
-#>  7      -43.3400        9.3641     18 17:50:27    0.0
      
-  libs(lucid)
-  # vc(m1) # Variance components match Stroup p. 41
-  ##   effect component std.error z.ratio bound 
-  ##      rep    62.4      56.54      1.1     P   0
-  ##    a:rep    15.38     11.79      1.3     P   0
-  ## units(R)     9.361     4.413     2.1     P   0
-  
-  # Narrow space predictions
-  predict(m1, data=dat, classify="a", average=list(rep=NULL))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:27 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -43.3400       9.36112     18 17:50:27    0.0
-#>  2      -43.3400       9.36111     18 17:50:27    0.0
-#>  3      -43.3400       9.36111     18 17:50:27    0.0
    #> $pvals
-#> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: b,rep
-#> 
-#> 
-#>    a predicted.value std.error    status
-#> 1 a1          32.875   1.08173 Estimable
-#> 2 a2          34.125   1.08173 Estimable
-#> 3 a3          25.750   1.08173 Estimable
-#> 
-#> $avsed
-#>  overall 
-#> 1.529797 
-#> 
      #  a Predicted Std Err    Status
-  # a1     32.88   1.082 Estimable
-  # a2     34.12   1.082 Estimable
-  # a3     25.75   1.082 Estimable
-
-  # Intermediate space predictions
-  predict(m1, data=dat, classify="a", ignore=list("a:rep"),
-          average=list(rep=NULL))
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:27 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -43.3400       9.36112     18 17:50:27    0.0
-#>  2      -43.3400       9.36111     18 17:50:27    0.0
-#>  3      -43.3400       9.36111     18 17:50:27    0.0
    #> $pvals
-#> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: b,rep
-#> - a:b is included in this prediction
-#> - b is included in this prediction
-#> - a is included in this prediction
-#> - a:rep is ignored in this prediction
-#> 
-#> 
-#>    a predicted.value std.error    status
-#> 1 a1          32.875  2.239559 Estimable
-#> 2 a2          34.125  2.239559 Estimable
-#> 3 a3          25.750  2.239559 Estimable
-#> 
-#> $avsed
-#>  overall 
-#> 3.167215 
-#> 
      #  a Predicted Std Err    Status
-  # a1     32.88    2.24 Estimable
-  # a2     34.12    2.24 Estimable
-  # a3     25.75    2.24 Estimable
-
-  # Broad space predictions
-  predict(m1, data=dat, classify="a")
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:27 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -43.3400       9.36112     18 17:50:27    0.0
-#>  2      -43.3400       9.36111     18 17:50:27    0.0
-#>  3      -43.3400       9.36111     18 17:50:27    0.0
    #> $pvals
-#> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: b
-#> - The ignored set: rep
-#> 
-#> 
-#>    a predicted.value std.error    status
-#> 1 a1          32.875  4.540329 Estimable
-#> 2 a2          34.125  4.540329 Estimable
-#> 3 a3          25.750  4.540329 Estimable
-#> 
-#> $avsed
-#>  overall 
-#> 3.167215 
-#> 
      #  a Predicted Std Err    Status
-  # a1     32.88    4.54 Estimable
-  # a2     34.12    4.54 Estimable
-  # a3     25.75    4.54 Estimable
-
-
-  # ----- Mcmcglmm model -----
-  # Use the point estimates from REML with a prior distribution
-  libs(lattice,MCMCglmm)
-
    #> Loading required package: coda
    #> 
-#> Attaching package: 'coda'
    #> The following object is masked from 'package:HH':
-#> 
-#>     acfplot
    #> Loading required package: ape
    #> 
-#> Attaching package: 'ape'
    #> The following object is masked from 'package:agricolae':
-#> 
-#>     consensus
    #> The following object is masked from 'package:pls':
-#> 
-#>     mvr
      prior2 = list(
-    G = list(G1=list(V=62.40, nu=1),  G2=list(V=15.38, nu=1)),
-    R = list(V = 9.4, nu=1)
-  )
-  m2 <- MCMCglmm(y~ -1 + a + b + a:b,
-                 random=~ rep + a:rep, data=dat,
-                 pr=TRUE, # save random effects as columns of 'Sol'
-                 nitt=23000, # double the default 13000
-                 prior=prior2, verbose=FALSE)
-  
-  # Now create a matrix of coefficients for the prediction.
-  # Each column is for a different prediction.  For example,
-  # the values in the column called 'a1a2n' are multiplied times
-  # the model coefficients (identified at the right side) to create
-  # the linear contrast for the the narrow-space predictions
-  # (also called adjusted mean) for the a1:a2 interaction.
-  #              a1n   a1i  a1b a1a2n a1a2ib
-  cm <- matrix(c(1,   1,   1,    1,    1,   # a1
-                 0,   0,   0,   -1,   -1,   # a2
-                 0,   0,   0,    0,    0,   # a3
-                 1/2, 1/2, 1/2,    0,    0,   # b2
-                 0,   0,   0,  -1/2,  -1/2, # a2:b2
-                 0,   0,   0,    0,    0,   # a3:b2
-                 1/4, 1/4,   0,    0,    0,   # r1
-                 1/4, 1/4,   0,    0,    0,   # r2
-                 1/4, 1/4,   0,    0,    0,   # r3
-                 1/4, 1/4,   0,    0,    0,   # r4
-                 1/4,   0,   0,  1/4,    0,   # a1r1
-                 0,   0,   0, -1/4,    0,   # a2r1
-                 0,   0,   0,    0,    0,   # a3r1
-               1/4,   0,   0,  1/4,    0,   # a1r2
-                 0,   0,   0, -1/4,    0,   # a2r2
-                 0,   0,   0,    0,    0,   # a3r2
-               1/4,   0,   0,  1/4,    0,   # a1r3
-                 0,   0,   0, -1/4,    0,   # a2r3
-                 0,   0,   0,    0,    0,   # a3r3
-               1/4,   0,   0,  1/4,    0,   # a1r4
-                 0,   0,   0, -1/4,    0,   # a2r4
-                 0,   0,   0,    0,    0),  # a3r4
-               ncol=5, byrow=TRUE)
-  rownames(cm) <-   c("a1", "a2", "a3", "b2", "a2:b2", "a3:b2",
-                      "r1", "r2", "r3", "r4",
-                      "a1r1", "a1r2", "a1r3", "a1r4", "a2r1", "a2r2",
-                      "a2r3", "a2r4", "a3r1", "a3r2",  "a3r3", "a3r4")
-  colnames(cm) <- c("A1n","A1i","A1b", "A1-A2n", "A1-A2ib")
-  print(cm)
-
    #>        A1n  A1i A1b A1-A2n A1-A2ib
-#> a1    1.00 1.00 1.0   1.00     1.0
-#> a2    0.00 0.00 0.0  -1.00    -1.0
-#> a3    0.00 0.00 0.0   0.00     0.0
-#> b2    0.50 0.50 0.5   0.00     0.0
-#> a2:b2 0.00 0.00 0.0  -0.50    -0.5
-#> a3:b2 0.00 0.00 0.0   0.00     0.0
-#> r1    0.25 0.25 0.0   0.00     0.0
-#> r2    0.25 0.25 0.0   0.00     0.0
-#> r3    0.25 0.25 0.0   0.00     0.0
-#> r4    0.25 0.25 0.0   0.00     0.0
-#> a1r1  0.25 0.00 0.0   0.25     0.0
-#> a1r2  0.00 0.00 0.0  -0.25     0.0
-#> a1r3  0.00 0.00 0.0   0.00     0.0
-#> a1r4  0.25 0.00 0.0   0.25     0.0
-#> a2r1  0.00 0.00 0.0  -0.25     0.0
-#> a2r2  0.00 0.00 0.0   0.00     0.0
-#> a2r3  0.25 0.00 0.0   0.25     0.0
-#> a2r4  0.00 0.00 0.0  -0.25     0.0
-#> a3r1  0.00 0.00 0.0   0.00     0.0
-#> a3r2  0.25 0.00 0.0   0.25     0.0
-#> a3r3  0.00 0.00 0.0  -0.25     0.0
-#> a3r4  0.00 0.00 0.0   0.00     0.0
      # post2 <- as.mcmc(m2$Sol 
-  post2 <- as.mcmc(crossprod(t(m2$Sol), cm))
-
-  # Following table has columns for A1 estimate (narrow, intermediate, broad)
-  # A1-A2 estimate (narrow and intermediat/broad).
-  # The REML estimates are from Stroup 1989.
-  est <- rbind("REML est"=c(32.88, 32.88, 32.88, -1.25, -1.25),
-               "REML stderr"=c(1.08, 2.24, 4.54, 1.53, 3.17),
-               "MCMC mode"=posterior.mode(post2),
-               "MCMC stderr"=apply(post2, 2, sd))
-  round(est,2)
-
    #>               A1n   A1i   A1b A1-A2n A1-A2ib
-#> REML est    32.88 32.88 32.88  -1.25   -1.25
-#> REML stderr  1.08  2.24  4.54   1.53    3.17
-#> MCMC mode   32.72 32.87 31.24  -1.58   -1.94
-#> MCMC stderr  1.24  2.59  6.29   1.71    3.71
      #               A1n   A1i   A1b A1-A2n A1-A2ib
-  # REML est    32.88 32.88 32.88  -1.25   -1.25
-  # REML stderr  1.08  2.24  4.54   1.53    3.17
-  # MCMC mode   32.95 32.38 31.96  -1.07   -1.17
-  # MCMC stderr  1.23  2.64  5.93   1.72    3.73
-
-  post22 <- lattice::make.groups(
-    Narrow=post2[,1], Intermediate=post2[,2], Broad=post2[,3])
-  print(densityplot(~data|which, data=post22, groups=which,
-                    cex=.25, lty=1, layout=c(1,3),
-                    xlab="MCMC model value of predictable function for A1"))
-
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/student.barley-1.png b/docs/reference/student.barley-1.png deleted file mode 100644 index 2c8efcf1..00000000 Binary files a/docs/reference/student.barley-1.png and /dev/null differ diff --git a/docs/reference/student.barley.html b/docs/reference/student.barley.html deleted file mode 100644 index a543884a..00000000 --- a/docs/reference/student.barley.html +++ /dev/null @@ -1,315 +0,0 @@ - - - - - - - - -Multi-environment trial of barley — student.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of barley

    - - -
    - -
    -

    Yield for two varieties of barley grown at 51 locations in the years - 1901 to 1906.

    -
    - - - -

    Format

    - -

    A data frame with 102 observations on the following 7 variables.

    -
    year

    year, 1901-1906

    -
    farmer

    farmer name

    -
    place

    place (nearest town)

    -
    district

    district, geographical area

    -
    gen

    genotype, Archer and Goldthorpe

    -
    yield

    yield, 'stones' per acre (1 stone = 14 pounds)

    -
    income

    income per acre in shillings, based on yield and quality

    - -
    - -

    Details

    - -

    Experiments were conducted for six years by the Department of - Agriculture in Ireland. A total of seven varieties were tested, but - only Archer and Goldthorpe were tested in all six years (others were - dropped after being found inferior, or were added later). Plots were - two acres in size. The value of the grain depended on the yield and - quality. Quality varied much from farm to farm, but not so much within - the same farm.

    -

    The phrase "analysis of variance" first appears in the abstract (only) - of a 1918 paper by Fisher. The 1923 paper by Student contained the - first analysis of variance table (but not for this data).

    -

    One stone is 14 pounds. - To convert lb/ac to tonnes/ha, multiply by 0.00112085116

    -

    Note: The analysis of Student cannot be reproduced exactly. For - example, Student states that the maximum income of Goldthorpe is 230 - shillings. A quick glance at Table I of Student shows that the - maximum income for Goldthorpe is 220 shillings (11 pounds, 0 - shillings) in 1901 at Thurles. Also, the results of Kempton could not - be reproduced exactly, perhaps due to rounding or the conversion - factor that was used.

    -

    Source

    - -

    Student. 1923. - On Testing Varieties of Cereals. - Biometrika, 15, 271--293. - https://doi.org/10.1093/biomet/15.3-4.271

    -

    References

    - -

    R A Kempton and P N Fox, 1997. - Statistical Methods for Plant Variety Evaluation.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(student.barley)
-  dat <- student.barley
-
-  libs(lattice)
-  bwplot(yield ~ gen|district, dat, main="student.barley - yield")
-
    
-  dat$year <- factor(dat$year)
-  dat$income <- NULL
-  
-  # convert to tons/ha
-  dat <- transform(dat, yield=yield*14 * 0.00112085116)
-  
-  # Define 'loc' the way that Kempton does
-  dat$loc <- rep("",nrow(dat))
-  dat[is.element(dat$farmer, c("Allardyce","Roche","Quinn")),"loc"] <- "1"
-  dat[is.element(dat$farmer, c("Luttrell","Dooley")), "loc"] <- "2"
-  dat[is.element(dat$year, c("1904","1905","1906")) & dat$farmer=="Kearney","loc"] <- "2"
-  dat[dat$farmer=="Mulhall","loc"] <- "3"
-  
-  dat <- transform(dat, loc=factor(paste(place,loc,sep="")))
-  
-  libs(reshape2)
-  datm <- melt(dat, measure.var='yield')
-
-  # Kempton Table 9.5
-  round(acast(datm, loc+gen~year),2)
-
    #>                          1901 1902 1903 1904 1905 1906
-#> Arnestown_Archer           NA   NA 1.33   NA   NA   NA
-#> Arnestown_Goldthorpe       NA   NA 1.93   NA   NA   NA
-#> Bagnalstown_Archer         NA   NA   NA   NA   NA 3.83
-#> Bagnalstown_Goldthorpe     NA   NA   NA   NA   NA 3.48
-#> Ballinacurra_Archer      2.82 3.11 1.66 2.57 3.14 2.48
-#> Ballinacurra_Goldthorpe  1.76 2.98 1.82 2.98 3.28 2.43
-#> Birr_Archer                NA 3.11 2.46 2.81 3.69 2.75
-#> Birr_Goldthorpe            NA 2.31 2.01 2.98 3.39 2.50
-#> Carlingford1_Archer        NA   NA 2.95   NA   NA   NA
-#> Carlingford1_Goldthorpe    NA   NA 2.31   NA   NA   NA
-#> Carlingford2_Archer        NA   NA   NA 2.01 3.61 2.93
-#> Carlingford2_Goldthorpe    NA   NA   NA 2.37 2.82 3.20
-#> Castlebridge_Archer        NA 2.81 3.12 2.29 2.86 2.92
-#> Castlebridge_Goldthorpe    NA 2.82 2.50 1.57 2.86 2.65
-#> Dunleer_Archer             NA   NA   NA 3.03 3.62 3.61
-#> Dunleer_Goldthorpe         NA   NA   NA 2.87 3.14 3.36
-#> Enniscorthy_Archer         NA 2.84   NA   NA   NA   NA
-#> Enniscorthy_Goldthorpe     NA 2.98   NA   NA   NA   NA
-#> Greenore_Archer            NA   NA 2.81   NA   NA   NA
-#> Greenore_Goldthorpe        NA   NA 1.96   NA   NA   NA
-#> Monasterevan1_Archer       NA   NA   NA 2.62   NA   NA
-#> Monasterevan1_Goldthorpe   NA   NA   NA 2.59   NA   NA
-#> Monasterevan2_Archer       NA   NA   NA   NA 3.64 2.42
-#> Monasterevan2_Goldthorpe   NA   NA   NA   NA 3.22 2.65
-#> Monasterevan3_Archer       NA   NA   NA   NA   NA 3.14
-#> Monasterevan3_Goldthorpe   NA   NA   NA   NA   NA 3.48
-#> Nenagh_Archer            2.76 3.04 2.04 3.31 3.61 2.95
-#> Nenagh_Goldthorpe        2.51 3.36 2.12 2.89 3.92 2.21
-#> NewRoss1_Archer            NA   NA   NA 2.04   NA   NA
-#> NewRoss1_Goldthorpe        NA   NA   NA 1.76   NA   NA
-#> NewRoss2_Archer            NA   NA   NA   NA 3.26 3.56
-#> NewRoss2_Goldthorpe        NA   NA   NA   NA 3.42 3.09
-#> Portarlington_Archer       NA   NA   NA 3.03 3.03   NA
-#> Portarlington_Goldthorpe   NA   NA   NA 2.81 2.64   NA
-#> Thurles_Archer           3.80   NA   NA   NA   NA   NA
-#> Thurles_Goldthorpe       3.48   NA   NA   NA   NA   NA
-#> Tullamore_Archer           NA   NA   NA   NA 3.45 2.23
-#> Tullamore_Goldthorpe       NA   NA   NA   NA 2.67 2.18
-#> Whitegate_Archer         2.56 3.51 2.20 2.68 2.93 2.65
-#> Whitegate_Goldthorpe     1.95 3.26 1.84 2.57 2.84 2.23
    
-  # Kempton Table 9.6
-  d2 <- dcast(datm, year+loc~gen)
-  mean(d2$Archer)
-
    #> [1] 2.895005
      mean(d2$Goldthorpe)
-
    #> [1] 2.685164
      mean(d2$Archer-d2$Goldthorpe)
-
    #> [1] 0.2098409
      sqrt(var(d2$Archer-d2$Goldthorpe)/51)
-
    #> [1] 0.05336873
      cor(d2$Archer,d2$Goldthorpe)
-
    #> [1] 0.7652551
    
-
-if(0){
-  # Kempton Table 9.6b
-  libs(lme4)
-  m2 <- lmer(yield~1 + (1|loc) + (1|year) +
-               (1|loc:year) + (1|gen:loc) + (1|gen:year), data=dat,
-             control=lmerControl(check.nobs.vs.rankZ="ignore"))
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/tai.potato-1.png b/docs/reference/tai.potato-1.png deleted file mode 100644 index 631a2f4b..00000000 Binary files a/docs/reference/tai.potato-1.png and /dev/null differ diff --git a/docs/reference/tai.potato.html b/docs/reference/tai.potato.html deleted file mode 100644 index 108af144..00000000 --- a/docs/reference/tai.potato.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - - - -Multi-environment trial of potato — tai.potato • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of potato

    - - -
    - -
    -

    Multi-environment trial of potato tuber yields

    -
    - - 
    data("tai.potato")
    - - -

    Format

    - -

    A data frame with 48 observations on the following 6 variables.

    -
    yield

    yield, kg/plot

    -
    gen

    genotype code

    -
    variety

    variety name

    -
    env

    environment code

    -
    loc

    location

    -
    year

    year

    - -
    - -

    Details

    - -

    Mean tuber yield of 8 genotypes in 3 locations over two years. - Katahdin and Sebago are check varieties. Each location was planted as - a 4-rep RCB design.

    -

    In Tai's plot of the stability parameters, F5751 and Sebago were in - the average stability area. The highest yielding genotype F6032 had - an unstable performance.

    -

    Source

    - -

    G.C.C. Tai, 1971. - Genotypic stability analysis and its application to potato regional trials. - Crop Sci 11, 184-190. Table 2, p. 187. - https://doi.org/10.2135/cropsci1971.0011183X001100020006x

    -

    References

    - -

    George Fernandez (1991). - Analysis of Genotype x Environment Interaction by Stability Estimates. - Hort Science, 26, 947-950.

    - -

    Examples

    - 
    
-library(agridat)
-data(tai.potato)
-dat <- tai.potato
-
-libs(lattice)
-dotplot(variety ~ yield|env, dat, main="tai.potato")
-
    
-# fixme - need to add tai() example
-
-# note, st4gi::tai assumes there are replications in the data
-# https://github.com/reyzaguirre/st4gi/blob/master/R/tai.R
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/talbot.potato-1.png b/docs/reference/talbot.potato-1.png deleted file mode 100644 index 2c998dd3..00000000 Binary files a/docs/reference/talbot.potato-1.png and /dev/null differ diff --git a/docs/reference/talbot.potato.html b/docs/reference/talbot.potato.html deleted file mode 100644 index 9e4c7ae7..00000000 --- a/docs/reference/talbot.potato.html +++ /dev/null @@ -1,298 +0,0 @@ - - - - - - - - -Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of potato in UK, yields and trait scores at 12 locations

    - - -
    - -
    - -

    Yield and 14 trait scores for each of 9 potato varieties at 12 - locations in UK.

    - -
    - - 
    data("talbot.potato.traits")
-data("talbot.potato.yield")
    - -

    Format

    - - - -

    The talbot.potato.yield dataframe has 126 observations on the following 3 variables.

    -
    gen

    genotype/variety

    -
    trait

    trait

    -
    score

    trait score, 1-9

    -
    - -

    The talbot.potato.yield dataframe has 108 observations on the following 3 variables.

    -
    gen

    genotype/variety

    -
    loc

    location/center

    -
    yield

    yield, t/ha

    -
    - - - -

    Details

    - - - -

    The talbot.potato.yield dataframe contains mean tuber yields - (t/ha) of 9 varieties of potato at 12 centers in the United Kingdom - over five years 1983-1987. The following abbreviations are used for - the centers.

    -

    - - - - - - - - - - - - -
    BUBush
    CACambridge
    CBConon Bridge
    CCCrossacreevy
    CPCockle Park
    CRCraibstone
    GRGreenmount
    HAHarper Adams
    MOMorley
    RORosemaund
    SBSutton Bonnington
    TETerrington
    - - - - -

    Source

    - -

    Mike Talbot and A V Wheelwright, 1989, - The analysis of genotype x analysis interactions by partial - least squares regression. - Biuletyn Oceny Odmian, 21/22, 19--25.

    -

    Used with permission of Mike Talbot.

    - - -

    Examples

    - 
    
-library(agridat)
-
-libs(pls, reshape2)
-
-data(talbot.potato.traits)
-datt <- talbot.potato.traits
-data(talbot.potato.yield)
-daty <- talbot.potato.yield
-
-datt <- acast(datt, gen ~ trait, value.var='score')
-daty <- acast(daty, gen ~ loc, value.var='yield')
-
-# Transform columns to zero mean and unit variance
-datt <- scale(datt)
-daty <- scale(daty)
-
-m1 <- plsr(daty ~ datt, ncomp=3)
-summary(m1)
    #> Data: 	X dimension: 9 14 
-#> 	Y dimension: 9 12
-#> Fit method: kernelpls
-#> Number of components considered: 3
-#> TRAINING: % variance explained
-#>      1 comps  2 comps  3 comps
-#> X   34.75196   51.176    69.43
-#> BU  23.45729   43.184    50.67
-#> CA  17.43483   54.890    58.94
-#> CB  43.53317   62.991    76.86
-#> CC  68.01120   82.451    82.99
-#> CP  48.47678   49.634    89.26
-#> CR  67.06064   71.160    83.44
-#> GR   0.03819   36.128    39.76
-#> HA  61.84794   87.342    87.49
-#> MO  61.46510   65.841    65.84
-#> RO  20.82397   27.029    46.44
-#> SB  16.47977   27.939    29.75
-#> TE   6.32481    9.181    22.36
    
-# Loadings factor 1
-lo <- loadings(m1)[,1,drop=FALSE]
-round(-1*lo[order(-1*lo),1,drop=FALSE],2)
    #>               Comp 1
-#> DryMatter      -0.38
-#> CrispColor     -0.38
-#> FoliageMatur   -0.33
-#> TuberBlight    -0.27
-#> ExtDamage      -0.21
-#> Gangrene       -0.20
-#> FoliageBlight  -0.01
-#> VirusY          0.12
-#> IntBruise       0.13
-#> FoliageCover    0.17
-#> CommonScab      0.25
-#> Dormancy        0.30
-#> LeafRoll        0.36
-#> DroughtResis    0.41
    
-biplot(m1, main="talbot.potato - biplot")
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/theobald.barley-1.png b/docs/reference/theobald.barley-1.png deleted file mode 100644 index 86c1640e..00000000 Binary files a/docs/reference/theobald.barley-1.png and /dev/null differ diff --git a/docs/reference/theobald.barley.html b/docs/reference/theobald.barley.html deleted file mode 100644 index 9ad64ebe..00000000 --- a/docs/reference/theobald.barley.html +++ /dev/null @@ -1,220 +0,0 @@ - - - - - - - - -Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of barley, multiple years & fertilizer levels

    - - -
    - -
    -

    Barley yields at multiple locs, years, fertilizer levels

    -
    - - 
    data("theobald.barley")
    - - -

    Format

    - -

    A data frame with 105 observations on the following 5 variables.

    -
    yield

    yield, tonnes/ha

    -
    gen

    genotype

    -
    loc

    location, 5 levels

    -
    nitro

    nitrogen kg/ha

    -
    year

    year, 2 levels

    - -
    - -

    Details

    - -

    Theobald and Talbot used BUGS to fit a fully Bayesian model - for yield response curves.

    -

    Locations of the experiment were in north-east Scotland.

    -

    Assumed nitrogen cost 400 pounds per tonne. - Grain prices used were 100, 110, and 107.50 pounds per tonne for - Georgie, Midas and Sundance.

    -

    Source

    - -

    Chris M. Theobald and Mike Talbot, (2002). - The Bayesian choice of crop variety and fertilizer dose. - Appl Statistics, 51, 23-36. - https://doi.org/10.1111/1467-9876.04863

    -

    Data provided by Chris Theobald and Mike Talbot.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(theobald.barley)
-dat <- theobald.barley
-dat <- transform(dat,  env=paste(loc,year,sep="-"))
-dat <- transform(dat, income=100*yield - 400*nitro/1000)
-
-libs(lattice)
-xyplot(income~nitro|env, dat, groups=gen, type='b',
-       auto.key=list(columns=3), main="theobald.barley")
-
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/theobald.covariate-1.png b/docs/reference/theobald.covariate-1.png deleted file mode 100644 index 1fd010a4..00000000 Binary files a/docs/reference/theobald.covariate-1.png and /dev/null differ diff --git a/docs/reference/theobald.covariate.html b/docs/reference/theobald.covariate.html deleted file mode 100644 index bb37058c..00000000 --- a/docs/reference/theobald.covariate.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn silage, Year * Loc * Variety with covariate

    - - -
    - -
    -

    Corn silage yields for maize in 5 years at 7 districts for 10 hybrids.

    -
    - - - -

    Format

    - -

    A data frame with 256 observations on the following 5 variables.

    -
    year

    year, 1990-1994

    -
    env

    environment/district, 1-7

    -
    gen

    genotype, 1-10

    -
    yield

    dry-matter silage yield for corn

    -
    chu

    corn heat units, thousand degrees Celsius

    - -
    - -

    Used with permission of Chris Theobald.

    -

    Details

    - -

    The trials were carried out in seven districts in the maritime - provinces of Eastern Canada. - Different fields were used in successive years. - The covariate CHU (Corn Heat Units) is the accumulated average daily - temperatures (thousands of degrees Celsius) during the growing season - at each location.

    -

    Source

    - -

    Chris M. Theobald and Mike Talbot and Fabian Nabugoomu, 2002. - A Bayesian Approach to Regional and Local-Area Prediction From Crop - Variety Trials. - Journ Agric Biol Env Sciences, 7, 403--419. - https://doi.org/10.1198/108571102230

    - -

    Examples

    - 
    
-library(agridat)
-
-data(theobald.covariate)
-dat <- theobald.covariate
-libs(lattice)
-xyplot(yield ~ chu|gen, dat, type=c('p','smooth'),
-       xlab =  "chu = corn heat units",
-       main="theobald.covariate - yield vs heat")
-
    
-# \dontrun{
-  # REML estimates (Means) in table 3 of Theobald 2002
-  libs(lme4)
-  dat <- transform(dat, year=factor(year))
-  m0 <- lmer(yield ~ -1 + gen + (1|year/env) + (1|gen:year), data=dat)
-  round(fixef(m0),2)
-
    #> genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 
-#>   6.50   6.63   5.71   6.81   6.37   6.28   6.54   6.66   6.18   6.80 
    # }
-
-# \dontrun{
-
-# Use JAGS to fit Theobald (2002) model 3.2 with 'Expert' prior
-
-libs(reshape2)
-ymat <- acast(dat, year+env~gen, value.var='yield')
-chu <- acast(dat, year+env~., mean, value.var='chu', na.rm=TRUE)
-chu <- as.vector(chu - mean(chu))  # Center the covariate
-dat$yr <- as.numeric(dat$year)
-yridx <- as.vector(acast(dat, year+env~., mean, value.var='yr', na.rm=TRUE))
-dat$loc <- as.numeric(dat$env)
-locidx <- acast(dat, year+env~., mean, value.var='loc', na.rm=TRUE)
-locidx <- as.vector(locidx)
-
-jdat <- list(nVar = 10, nYear = 5, nLoc = 7, nYL = 29, yield = ymat,
-            chu = chu, year = yridx, loc = locidx)
-
-libs(rjags)
-
    #> Linked to JAGS 4.3.0
    #> Loaded modules: basemod,bugs
    m1 <- jags.model(file=system.file(package="agridat", "files/theobald.covariate.jag"),
-  data=jdat, n.chains=2)
-
    #> Compiling model graph
-#>    Resolving undeclared variables
-#>    Allocating nodes
-#> Graph information:
-#>    Observed stochastic nodes: 256
-#>    Unobserved stochastic nodes: 153
-#>    Total graph size: 1013
-#> 
-#> Initializing model
-#> 
    
-# Table 3, Variety deviations from means (Expert prior)
-c1 <- coda.samples(m1, variable.names=(c('alpha')),
-                   n.iter=10000, thin=10)
-s1 <- summary(c1)
-effs <- s1$statistics[,'Mean']
-rev(sort(round(effs - mean(effs), 2))) # Perfect match (different order?)
-
    #>  alpha[4] alpha[10]  alpha[8]  alpha[2]  alpha[7]  alpha[1]  alpha[5]  alpha[6] 
-#>      0.34      0.32      0.18      0.17      0.09      0.05     -0.07     -0.15 
-#>  alpha[9]  alpha[3] 
-#>     -0.25     -0.68 
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/thompson.cornsoy-1.png b/docs/reference/thompson.cornsoy-1.png deleted file mode 100644 index 71036d8f..00000000 Binary files a/docs/reference/thompson.cornsoy-1.png and /dev/null differ diff --git a/docs/reference/thompson.cornsoy-2.png b/docs/reference/thompson.cornsoy-2.png deleted file mode 100644 index 9e198e48..00000000 Binary files a/docs/reference/thompson.cornsoy-2.png and /dev/null differ diff --git a/docs/reference/thompson.cornsoy-3.png b/docs/reference/thompson.cornsoy-3.png deleted file mode 100644 index 908e3e93..00000000 Binary files a/docs/reference/thompson.cornsoy-3.png and /dev/null differ diff --git a/docs/reference/thompson.cornsoy.html b/docs/reference/thompson.cornsoy.html deleted file mode 100644 index ef632d69..00000000 --- a/docs/reference/thompson.cornsoy.html +++ /dev/null @@ -1,298 +0,0 @@ - - - - - - - - -Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation

    - - -
    - -
    -

    Average yield of corn and soybeans in five U.S. states - (IA, IL, IN, MO, OH) during the years 1930-1962. - Pre-season precipitation and average temperature and precipitation - during each month of the growing season is included.

    -
    - - - -

    Format

    - -

    -
    state

    state

    -
    year

    year, 1930-1962

    -
    rain0

    pre-season precipitation in inches

    -
    temp5

    may temperature, Fahrenheit

    -
    rain6

    june rain, inches

    -
    temp6

    june temp

    -
    rain7

    july rain

    -
    temp7

    july temp

    -
    rain8

    august rain

    -
    temp8

    august temp

    -
    corn

    corn yield, bu/acre

    -
    soy

    soybean yield, bu/acre

    - -
    - -

    Details

    - -

    Note: The Iowa corn data has sometimes been identified (in other - sources) as the "Iowa wheat" data, but this is incorrect.

    -

    The 'year' variable affects yield through (1) improvements in - plant genetics (2) changes in management techniques such as - fertilizer, chemicals, tillage, planting date, and - (3) climate, pest infestations, etc.

    -

    Double-cross corn hybrids were introduced in the 1920s. Single-cross - hybrids became common around 1960.

    -

    During World War II, nitrogen was used in the production of TNT for - bombs. After the war, these factories switched to producing ammonia - for fertilizer. Nitrogen fertilizer use greatly increased after WWII - and is a major reason for yield gains of corn. Soybeans gain little - benefit from nitrogen fertilizer. The other major reason for - increasing yields in both crops is due to improved plant genetics.

    -

    Crops are often planted in May, and harvest begins in September.

    -

    Yields in 1936 were very low due to July being one of the hottest and - driest on record.

    -

    Some relevant maps of yield, heat, and precipitation can be found in - Atlas of crop yield and summer weather patterns, 1931-1975, - https://www.isws.illinois.edu/pubdoc/C/ISWSC-150.pdf

    -

    The following notes pertain to the Iowa data.

    -

    The 1947 June precipitation of 10.33 inches was the wettest June on - record (a new Iowa June record of 10.34 inches was set in 2010). - As quoted in Monthly Weather Review (Dec 1957, p. 396) - "The dependence of Iowa agriculture upon the vagaries of the weather - was closely demonstrated during the 1947 season. A cool wet spring - delayed crop planting activity and plant growth; - then, in addition, a hard freeze on May 29th ... further set back the - corn. The heavy rains and subsequent floods during June caused - appreciable crop acreage to be abandoned ... followed by a hot dry - weather regime that persisted from mid-July through the first week - of September."

    -

    In 1949 soybean yields were average while corn yields were low. - From the same source above, - "The year 1949 saw the greatest infestation of corn borer in the - history of corn in Iowa".

    -

    1955 yields were reduced due to dry weather in late July and August.

    -

    Source

    - -

    Thompson, L.M., 1963. Weather and technology in the production - of corn and soybeans. CAED Report 17. The Center for Agriculture - and Economic Development, Iowa State University, Ames, Iowa.

    -

    References

    - -

    Draper, N. R. and Smith, H. (1981). Applied Regression Analysis, - second ed., Wiley, New York.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(thompson.cornsoy)
-dat <- thompson.cornsoy
-
-# The droughts of 1934/36 were severe in IA/MO. Less so in OH.
-libs(lattice)
-xyplot(corn+soy~year|state, dat,
-       type=c('p','l','r'), auto.key=list(columns=2),
-       main="thompson.cornsoy",
-       layout=c(5,1),ylab='yield')
-
    
-# In 1954, only Missouri suffered very hot, dry weather
-## xyplot(corn~year, dat,
-##        groups=state, type=c('p','l'),
-##        main="thompson.cornsoy",
-##        auto.key=list(columns=5), ylab='corn yield')
-
-# Rain and temperature have negative correlation in each month.
-# July is a critical month: temp and yield are negatively correlated,
-# while rain and yield are positively correlated.
-# splom(~dat[-1,-1], col=dat$state, cex=.5, main="thompson.cornsoy")
-
-# \dontrun{
-  # Plots similar to those in Venables' Exegeses paper.
-
-  dat.ia <- subset(dat, state=="Iowa")
-
-  libs(splines)
-  m2 <- aov(corn ~ ns(rain0, 3) + ns(rain7, 3) +
-              ns(temp8, 3) + ns(year,3), dat.ia)
-  op <- par(mfrow=c(2,2))
-  termplot(m2, se=TRUE, rug=TRUE, partial=TRUE)
-
      par(op)
-
-  # do NOT use gam package
-  libs(mgcv)
-  m1 <- gam(corn ~ s(year, k=5) + s(rain0, k=5) +
-              s(rain7, k=5) + s(temp8, k=5), data=dat.ia)
-  op <- par(mfrow=c(2,2))
-  plot.gam(m1, residuals=TRUE, se=TRUE, cex=2)
-
      par(op)
-# }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/turner.herbicide-1.png b/docs/reference/turner.herbicide-1.png deleted file mode 100644 index 30b847d8..00000000 Binary files a/docs/reference/turner.herbicide-1.png and /dev/null differ diff --git a/docs/reference/turner.herbicide.html b/docs/reference/turner.herbicide.html deleted file mode 100644 index d3e9796e..00000000 --- a/docs/reference/turner.herbicide.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Herbicide control of larkspur — turner.herbicide • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Herbicide control of larkspur

    - - -
    - -
    -

    Herbicide control of larkspur

    -
    - - 
    data("turner.herbicide")
    - - -

    Format

    - -

    A data frame with 12 observations on the following 4 variables.

    -
    rep

    rep factor

    -
    rate

    rate of herbicide

    -
    live

    number of live plants before application

    -
    dead

    number of plants killed by herbicide

    - -
    - -

    Details

    - -

    Effectiveness of the herbicide Picloram on larkspur plants at 4 doses - (0, 1.1, 2.2, 4.5) in 3 reps. Experiment was done in 1986 at Manti, - Utah.

    -

    Source

    - -

    David L. Turner and Michael H. Ralphs and John O. Evans (1992). - Logistic Analysis for Monitoring and Assessing Herbicide Efficacy. - Weed Technology, 6, 424-430. - https://www.jstor.org/stable/3987312

    -

    References

    - -

    Christopher Bilder, Thomas Loughin. - Analysis of Categorical Data with R.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(turner.herbicide)
-  dat <- turner.herbicide
-  
-  dat <- transform(dat, prop=dead/live)
-  # xyplot(prop~rate,dat, pch=20, main="turner.herbicide", ylab="Proportion killed")
-
-  m1 <- glm(prop~rate, data=dat, weights=live, family=binomial)
-  coef(m1) # -3.46, 2.6567  Same as Turner eqn 3
-
    #> (Intercept)        rate 
-#>   -3.459958    2.656715 
      
-  # Make conf int on link scale and back-transform
-  p1 <- expand.grid(rate=seq(0,to=5,length=50))
-  p1 <- cbind(p1, predict(m1, newdata=p1, type='link', se.fit=TRUE))
-  p1 <- transform(p1, lo = plogis(fit - 2*se.fit),
-                  fit = plogis(fit),
-                  up = plogis(fit + 2*se.fit))
-  
-  # Figure 2 of Turner
-  libs(latticeExtra)
-  foo1 <- xyplot(prop~rate,dat, cex=1.5,
-                 main="turner.herbicide (model with 2*S.E.)",
-                 xlab="Herbicide rate", ylab="Proportion killed")
-  foo2 <- xyplot(fit~rate, p1, type='l')
-  foo3 <- xyplot(lo+up~rate, p1, type='l', lty=1, col='gray')
-  print(foo1 + foo2 + foo3)
-
    
-
-  # What dose gives a LD90 percent kill rate?
-  # libs(MASS)
-  # dose.p(m1, p=.9)
-  ##             Dose       SE
-  ## p = 0.9: 2.12939 0.128418
-
-  # Alternative method
-  # libs(car) # logit(.9) = 2.197225
-  # deltaMethod(m1, g="(log(.9/(1-.9))-b0)/(b1)", parameterNames=c('b0','b1'))
-  ##                      Estimate       SE
-  ## (2.197225 - b0)/(b1)  2.12939 0.128418
-  
-  # What is a 95 percent conf interval for LD90?  Bilder & Loughin page 138
-  root <- function(x, prob=.9, alpha=0.05){
-    co <- coef(m1)    # b0,b1
-    covs <- vcov(m1)  # b00,b11,b01
-    # .95 = b0 + b1*x
-    # (b0+b1*x) + Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) > .95
-    # (b0+b1*x) - Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) < .95
-    f <- abs(co[1] + co[2]*x - log(prob/(1-prob))) /
-      sqrt(covs[1,1] + x^2 * covs[2,2] + 2*x*covs[1,2])
-    return( f - qnorm(1-alpha/2))
-  }
-  lower <- uniroot(f=root, c(0,2.13))
-  upper <- uniroot(f=root, c(2.12, 5))
-  c(lower$root, upper$root)
-
    #> [1] 1.917257 2.446099
      # 1.92 2.45
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/urquhart.feedlot-1.png b/docs/reference/urquhart.feedlot-1.png deleted file mode 100644 index 3129f278..00000000 Binary files a/docs/reference/urquhart.feedlot-1.png and /dev/null differ diff --git a/docs/reference/urquhart.feedlot.html b/docs/reference/urquhart.feedlot.html deleted file mode 100644 index 56c81066..00000000 --- a/docs/reference/urquhart.feedlot.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - - - -Weight gain calves in a feedlot — urquhart.feedlot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Weight gain calves in a feedlot

    - - -
    - -
    -

    Weight gain calves in a feedlot, given three different diets.

    -
    - - 
    data("urquhart.feedlot")
    - - -

    Format

    - -

    A data frame with 67 observations on the following 5 variables.

    -
    animal

    animal ID

    -
    herd

    herd ID

    -
    diet

    diet: Low, Medium, High

    -
    weight1

    initial weight

    -
    weight2

    slaughter weight

    - -
    - -

    Details

    - - - -

    Calves born in 1975 in 11 different herds entered a feedlot as - yearlings. Each animal was fed one of three diets with low, medium, or - high energy. The original sources explored the use of some contrasts - for comparing breeds.

    -

    - - - - - - - - - - - - -
    HerdBreed
    9New Mexico Herefords
    16New Mexico Herefords
    3Utah State University Herefords
    32Angus
    24Angus x Hereford (cross)
    31Charolais x Hereford
    19Charolais x Hereford
    36Charolais x Hereford
    34Brangus
    35Brangus
    33Southern Select
    - - - -

    Source

    - -

    N. Scott Urquhart (1982). - Adjustment in Covariance when One Factor Affects the Covariate - Biometrics, 38, 651-660. Table 4, p. 659. - https://doi.org/10.2307/2530046

    -

    References

    - -

    N. Scott Urquhart and David L. Weeks (1978). - Linear Models in Messy Data: Some Problems and Alternatives - Biometrics, 34, 696-705. - https://doi.org/10.2307/2530391

    -

    Also available in the 'emmeans' package as the 'feedlot' data.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(urquhart.feedlot)
-  dat <- urquhart.feedlot
-
-  libs(reshape2)
-  d2 <- melt(dat, id.vars=c('animal','herd','diet'))
-
-  libs(latticeExtra)
-  useOuterStrips(xyplot(value ~ variable|diet*herd, data=d2, group=animal,
-                        type='l',
-                        xlab="Initial & slaughter timepoint for each diet",
-                        ylab="Weight for each herd",
-                        main="urquhart.feedlot - weight gain by animal"))
-
    
-
-  # simple fixed-effects model 
-  dat <- transform(dat, animal = factor(animal), herd=factor(herd))
-  m1 <- lm(weight2 ~ weight1 + herd*diet, data = dat)
-  coef(m1) # weight1 = 1.1373 match Urquhart table 5 common slope
-
    #>       (Intercept)           weight1             herd9            herd16 
-#>        280.222922          1.137315        -71.611764        -51.679186 
-#>            herd19            herd24            herd31            herd32 
-#>        -81.089896        -80.768678        -36.269082        -65.477829 
-#>            herd33            herd34            herd35            herd36 
-#>       -125.209353         -3.970439        -29.895929        -15.328810 
-#>           dietLow        dietMedium     herd9:dietLow    herd16:dietLow 
-#>        -33.622123        -12.783862        -16.960282          9.502665 
-#>    herd19:dietLow    herd24:dietLow    herd31:dietLow    herd32:dietLow 
-#>        121.696783         20.625565         50.806235         66.358372 
-#>    herd33:dietLow    herd34:dietLow    herd35:dietLow    herd36:dietLow 
-#>        222.442937        -27.760884         25.084868         -1.751030 
-#>  herd9:dietMedium herd16:dietMedium herd19:dietMedium herd24:dietMedium 
-#>                NA                NA         49.530167                NA 
-#> herd31:dietMedium herd32:dietMedium herd33:dietMedium herd34:dietMedium 
-#>        -29.462442        -67.216138         25.450529        -20.649019 
-#> herd35:dietMedium herd36:dietMedium 
-#>          2.447965       -116.014781 
      
-  # random-effects model might be better, for example
-  # libs(lme4)
-  # m1 <- lmer(weight2 ~ -1 + diet + weight1 + (1|herd), data=dat)
-  # summary(m1) # weight1 = 1.2269
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/usgs.herbicides-1.png b/docs/reference/usgs.herbicides-1.png deleted file mode 100644 index b51f91b9..00000000 Binary files a/docs/reference/usgs.herbicides-1.png and /dev/null differ diff --git a/docs/reference/usgs.herbicides-2.png b/docs/reference/usgs.herbicides-2.png deleted file mode 100644 index c161f8a7..00000000 Binary files a/docs/reference/usgs.herbicides-2.png and /dev/null differ diff --git a/docs/reference/usgs.herbicides-3.png b/docs/reference/usgs.herbicides-3.png deleted file mode 100644 index 565c29a0..00000000 Binary files a/docs/reference/usgs.herbicides-3.png and /dev/null differ diff --git a/docs/reference/usgs.herbicides.html b/docs/reference/usgs.herbicides.html deleted file mode 100644 index f4b1c0f1..00000000 --- a/docs/reference/usgs.herbicides.html +++ /dev/null @@ -1,279 +0,0 @@ - - - - - - - - -Concentrations of herbicides in streams in the United States — usgs.herbicides • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Concentrations of herbicides in streams in the United States

    - - -
    - -
    -

    Concentrations of selected herbicides and degradation products - determined by laboratory method analysis code GCS for water samples - collected from 51 streams in nine Midwestern States,2002

    -
    - - 
    data("usgs.herbicides")
    - - -

    Format

    - -

    A data frame with 184 observations on the following 19 variables.

    -
    mapnum

    map number

    -
    usgsid

    USGS ID

    -
    long

    longitude

    -
    lat

    latitude

    -
    site

    site name

    -
    city

    city

    -
    sampletype

    sample type code

    -
    date

    date sample was collected

    -
    hour

    hour sample was collected

    -
    acetochlor

    concentration as character

    -
    alachlor

    concentration as character

    -
    ametryn

    concentration as character

    -
    atrazine

    concentration as character

    -
    CIAT

    concentration as character

    -
    CEAT

    concentration as character

    -
    cyanazine

    concentration as character

    -
    CAM

    concentration as character

    -
    dimethenamid

    concentration as character

    -
    flufenacet

    concentration as character

    - -
    - -

    Details

    - - - -

    Concentrations of selected herbicides and degradation products determined by laboratory method analysis code GCS for water samples collected from 51 streams in nine Midwestern States, 2002.

    -

    All concentrations are micrograms/liter, "<" means "less than". The data are in character format to allow for "<".

    -

    The original report contains data for more herbicides. This data is for illustrative purposes.

    -

    Sample types: CR = concurrent replicate sample, FB = field blank, LD = laboratory duplicate, S1 = sample from pre-emergence runoff, S2 = sample from post-emergence runoff, S3 = sample from harvest-season runoff.

    -

    Source

    - -

    Scribner, E.A., Battaglin, W.A., Dietze, J.E., and Thurman, E.M., - "Reconnaissance Data for Glyphosate, Other Selected Herbicides, their Degradation Products, and Antibiotics in 51 streams in Nine Midwestern States, 2002". - U.S. Geological Survey Open File Report 03-217. - Herbicide data from table 5, page 30-37. Site coordinates page 7-8. - https://ks.water.usgs.gov/pubs/reports/ofr.03-217.html

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(usgs.herbicides)
-  dat <- usgs.herbicides
-  
-
-  libs(NADA)
-
    #> 
-#> Attaching package: 'NADA'
    #> The following object is masked from 'package:stats':
-#> 
-#>     cor
      # create censored data for one trait
-  dat$y <- as.numeric(dat$atrazine)
-  dat$ycen <- is.na(dat$y)
-  dat$y[is.na(dat$y)] <- .05
-  
-  # percent censored
-  with(dat, censummary(y, censored=ycen))
-
    #> all:
-#>       n   n.cen pct.cen     min     max 
-#>     184       0       0       1     101 
-#> 
-#> limits:
-#>   limit n uncen pexceed
-#> 1     0 0   184       1
-#> 
      # median/mean
-  with(dat, cenmle(y, ycen, dist="lognormal"))
-
    #>         n     n.cen    median      mean        sd 
-#> 184.00000   0.00000  30.63365  60.32547 102.33953 
      # boxplot
-  with(dat, cenboxplot(obs=y, cen=ycen, log=FALSE))
-
    #> Warning: no non-missing arguments to max; returning -Inf
      # with(dat, boxplot(y))
-  pp <- with(dat, ros(obs=y, censored=ycen, forwardT="log")) # default lognormal
-  plot(pp)
-
    
-
-  plotfun <- function(vv){
-    dat$y <- as.numeric(dat[[vv]])
-    dat$ycen <- is.na(dat$y)
-    dat$y[is.na(dat$y)] <- .01
-    # qqnorm(log(dat$y), main=vv) # ordinary qq plot shows censored values
-    pp <- with(dat, ros(obs=y, censored=ycen, forwardT="log"))
-    plot(pp, main=vv) # omits censored values
-  }
-  op <- par(mfrow=c(3,3))
-  vnames <- c("acetochlor", "alachlor", "ametryn", "atrazine","CIAT", "CEAT", "cyanazine", #"CAM",
-              "dimethenamid", "flufenacet")
-  for(vv in vnames) plotfun(vv)
-
      par(op)
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vaneeuwijk.drymatter-1.png b/docs/reference/vaneeuwijk.drymatter-1.png deleted file mode 100644 index 9e424da0..00000000 Binary files a/docs/reference/vaneeuwijk.drymatter-1.png and /dev/null differ diff --git a/docs/reference/vaneeuwijk.drymatter.html b/docs/reference/vaneeuwijk.drymatter.html deleted file mode 100644 index 75c0b116..00000000 --- a/docs/reference/vaneeuwijk.drymatter.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - -Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of maize, dry matter content

    - - -
    - -
    -

    Multi-environment trial of maize, dry matter content

    -
    - - 
    data("vaneeuwijk.drymatter")
    - - -

    Format

    - -

    A data frame with 168 observations on the following 5 variables.

    -
    year

    year

    -
    site

    site, 4 levels

    -
    variety

    variety, 6 levels

    -
    y

    dry matter percent

    - -
    - -

    Details

    - - - -

    Percent dry matter is given.

    -

    Site codes are soil type classifications: - SS=Southern Sand, CS=Central Sand, NS=Northern Sand, RC=River Clay.

    -

    These data are a balanced subset of the data analyzed in - van Eeuwijk, Keizer, and Bakker (1995b) and - Kroonenberg, Basford, and Ebskamp (1995).

    - -

    Used with permission of Fred van Eeuwijk.

    -

    Source

    - -

    van Eeuwijk, Fred A. and Pieter M. Kroonenberg (1998). - Multiplicative Models for Interaction in Three-Way ANOVA, with Applications to Plant Breeding - Biometrics, 54, 1315-1333. - https://doi.org/10.2307/2533660

    -

    References

    - - - -

    Kroonenberg, P.M., Basford, K.E. & Ebskamp, A.G.M. (1995). - Three-way cluster and component analysis of maize variety trials. - Euphytica, 84(1):31-42. - https://doi.org/10.1007/BF01677554

    -

    van Eeuwijk, F.A., Keizer, L.C.P. & Bakker, J.J. Van Eeuwijk. (1995b). - Linear and bilinear models for the analysis of multi-environment trials: - II. An application to data from the Dutch Maize Variety Trials - Euphytica, 84(1):9-22. - https://doi.org/10.1007/BF01677552

    -

    Hardeo Sahai, Mario M. Ojeda. - Analysis of Variance for Random Models, Volume 1. Page 261.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(vaneeuwijk.drymatter)
-  dat <- vaneeuwijk.drymatter
-  dat <- transform(dat, year=factor(year))
-  dat <- transform(dat, env=factor(paste(year,site)))
-
-  libs(HH)
-  HH::interaction2wt(y ~ year+site+variety,dat,rot=c(90,0),
-                     x.between=0, y.between=0,
-                     main="vaneeuwijk.drymatter")
-
    
-  
-  # anova model
-  m1 <- aov(y ~ variety+env+variety:env, data=dat)
-  anova(m1) # Similar to VanEeuwijk table 2
-
    #> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
    #> Analysis of Variance Table
-#> 
-#> Response: y
-#>              Df  Sum Sq Mean Sq F value Pr(>F)
-#> variety       5   80.18  16.036               
-#> env          27 3008.21 111.415               
-#> variety:env 135  161.87   1.199               
-#> Residuals     0    0.00                       
      m2 <- aov(y ~ year*site*variety, data=dat)
-  anova(m2) # matches Sahai table 5.5
-
    #> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
    #> Analysis of Variance Table
-#> 
-#> Response: y
-#>                   Df  Sum Sq Mean Sq F value Pr(>F)
-#> year               6 1194.65  199.11               
-#> site               3  962.19  320.73               
-#> variety            5   80.18   16.04               
-#> year:site         18  851.37   47.30               
-#> year:variety      30   64.90    2.16               
-#> site:variety      15   17.01    1.13               
-#> year:site:variety 90   79.97    0.89               
-#> Residuals          0    0.00                       
      
-  # variance components model
-  libs(lme4)
-  libs(lucid)
-  m3 <- lmer(y ~ (1|year) + (1|site) + (1|variety) +
-               (1|year:site) + (1|year:variety) + (1|site:variety),
-             data=dat)
-  vc(m3) # matches Sahai page 266
-
    #>           grp        var1 var2    vcov  sdcor
-#>  year:variety (Intercept) <NA> 0.3187  0.5645
-#>     year:site (Intercept) <NA> 7.734   2.781 
-#>  site:variety (Intercept) <NA> 0.03503 0.1872
-#>          year (Intercept) <NA> 6.273   2.505 
-#>       variety (Intercept) <NA> 0.4867  0.6976
-#>          site (Intercept) <NA> 6.504   2.55  
-#>      Residual        <NA> <NA> 0.8886  0.9426
      ##          grp        var1 var2    vcov  sdcor
-  ## year:variety (Intercept) <NA> 0.3187  0.5645
-  ##    year:site (Intercept) <NA> 7.735   2.781 
-  ## site:variety (Intercept) <NA> 0.03502 0.1871
-  ##         year (Intercept) <NA> 6.272   2.504 
-  ##      variety (Intercept) <NA> 0.4867  0.6976
-  ##         site (Intercept) <NA> 6.504   2.55  
-  ##     Residual        <NA> <NA> 0.8885  0.9426
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vaneeuwijk.fusarium-1.png b/docs/reference/vaneeuwijk.fusarium-1.png deleted file mode 100644 index 25ea2f45..00000000 Binary files a/docs/reference/vaneeuwijk.fusarium-1.png and /dev/null differ diff --git a/docs/reference/vaneeuwijk.fusarium.html b/docs/reference/vaneeuwijk.fusarium.html deleted file mode 100644 index 2dc51ccb..00000000 --- a/docs/reference/vaneeuwijk.fusarium.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - -Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Infection of wheat varieties by Fusarium strains from 1990 to 1993

    - - -
    - -
    -

    Infection of wheat varieties by Fusarium strains from 1990 to 1993

    -
    - - 
    data("vaneeuwijk.fusarium")
    - - -

    Format

    - -

    A data frame with 560 observations on the following 4 variables.

    -
    year

    year, 1990-1993

    -
    strain

    strain of fusarium

    -
    gen

    genotype/variety

    -
    y

    - -
    - -

    Details

    - -

    Data come from Hungary. There were 20 wheat varieties infected with 7 - strains of Fusarium in the years 1990-1993. The measured value is a - rating of the severity of disease due to Fusarium head blight, - expressed as a number 1-100.

    -

    Three-way interactions for varieties 21 and 23 were the only ones in - 1992 suffering from strain infections. This was due to incorrect - storage of the innoculum (strain) which rendered it incapable of - infecting most other varieties.

    -

    The data is a subset of the data analyzed by VanEeuwijk et al. 1995.

    -

    Used with permission of Fred van Eeuwijk.

    -

    Source

    - -

    van Eeuwijk, Fred A. and Pieter M. Kroonenberg (1998). - Multiplicative Models for Interaction in Three-Way ANOVA, with Applications to Plant Breeding - Biometrics, 54, 1315-1333. - https://doi.org/10.2307/2533660

    -

    References

    - -

    F. A. van Eeuwijk, A. Mesterhazy, Ch. I. Kling, P. Ruckenbauer, - L. Saur, H. Burstmayr, M. Lemmens, L. C. P. Keizer, N. Maurin, - C. H. A. Snijders. (1995). - Assessing non-specificity of resistance in wheat to head blight caused - by inoculation with European strains of Fusarium culmorum, - F. graminearum and F. nivale using a multiplicative model for - interaction. - Theor Appl Genet. 90(2), 221-8. - https://doi.org/10.1007/BF00222205

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(vaneeuwijk.fusarium)
-  dat <- vaneeuwijk.fusarium
-  dat <- transform(dat, year=factor(year))
-  dat <- transform(dat, logity=log((y/100)/(1-y/100)))
-
-  libs(HH)
-  position(dat$year) <- c(3,9,14,19)
-  position(dat$strain) <- c(2,5,8,11,14,17,20)
-  HH::interaction2wt(logity ~ gen+year+strain,dat,rot=c(90,0),
-                     x.between=0, y.between=0,
-                     main="vaneeuwijk.fusarium")
-
    
-
-  # anova on logit scale. Near match to VanEeuwijk table 6
-  m1 <- aov(logity ~ gen*strain*year, data=dat)
-  anova(m1)
-
    #> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
    #> Analysis of Variance Table
-#> 
-#> Response: logity
-#>                  Df Sum Sq Mean Sq F value Pr(>F)
-#> gen              19 157.55   8.292               
-#> strain            6  91.54  15.256               
-#> year              3 321.99 107.331               
-#> gen:strain      114  34.03   0.299               
-#> gen:year         57 140.94   2.473               
-#> strain:year      18 236.95  13.164               
-#> gen:strain:year 342  93.15   0.272               
-#> Residuals         0   0.00                       
      ## Response: logity
-  ##                  Df Sum Sq Mean Sq F value Pr(>F)
-  ## gen              19 157.55   8.292               
-  ## strain            6  91.54  15.256               
-  ## year              3 321.99 107.331               
-  ## gen:strain      114  34.03   0.299               
-  ## gen:year         57 140.94   2.473               
-  ## strain:year      18 236.95  13.164               
-  ## gen:strain:year 342  93.15   0.272
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vaneeuwijk.nematodes-1.png b/docs/reference/vaneeuwijk.nematodes-1.png deleted file mode 100644 index bacac3e7..00000000 Binary files a/docs/reference/vaneeuwijk.nematodes-1.png and /dev/null differ diff --git a/docs/reference/vaneeuwijk.nematodes-2.png b/docs/reference/vaneeuwijk.nematodes-2.png deleted file mode 100644 index 22ac768b..00000000 Binary files a/docs/reference/vaneeuwijk.nematodes-2.png and /dev/null differ diff --git a/docs/reference/vaneeuwijk.nematodes-3.png b/docs/reference/vaneeuwijk.nematodes-3.png deleted file mode 100644 index 832597eb..00000000 Binary files a/docs/reference/vaneeuwijk.nematodes-3.png and /dev/null differ diff --git a/docs/reference/vaneeuwijk.nematodes.html b/docs/reference/vaneeuwijk.nematodes.html deleted file mode 100644 index b4421d33..00000000 --- a/docs/reference/vaneeuwijk.nematodes.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - -Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations.

    - - -
    - -
    -

    The number of cysts on 11 potato genotypes for 5 potato cyst nematode - populations.

    -
    - - 
    data("vaneeuwijk.nematodes")
    - - -

    Format

    - -

    A data frame with 55 observations on the following 3 variables.

    -
    gen

    potato genotype

    -
    pop

    nematode population

    -
    y

    number of cysts

    - -
    - -

    Details

    - -

    The number of cysts on 11 potato genotypes for 5 potato cyst nematode - populations belonging to the species Globodera pallida. This - is part of a larger table in . The numbers are the means over four or - five replicates.

    -

    Van Eeuwijk used this data to illustrate fitting a generalized linear - model.

    -

    Source

    - -

    Fred A. van Eeuwijk, (1995). - Multiplicative Interaction in Generalized Linear Models. - Biometrics, 51, 1017-1032. - https://doi.org/10.2307/2533001

    -

    References

    - -

    Arntzen, F.K. & van Eeuwijk (1992). - Variation in resistance level of potato genotypes and virulence level of - potato cyst nematode populations. - Euphytica, 62, 135-143. - https://doi.org/10.1007/BF00037939

    - -

    Examples

    - 
    
-library(agridat)
-data(vaneeuwijk.nematodes)
-dat <- vaneeuwijk.nematodes
-
-# show non-normality
-op <- par(mfrow=c(2,1), mar=c(5,4,3,2))
-boxplot(y ~ pop, data=dat, las=2,
-        ylab="number of cysts")
-title("vaneeuwijk.nematodes - cysts per nematode pop")
-boxplot(y ~ gen, data=dat, las=2)
-
    title("vaneeuwijk.nematodes - cysts per potato")
-
    par(op)
-
-# \dontrun{
-  # normal distribution
-  lm1 <- lm(y ~ gen + pop, data=dat)
-
-  # poisson distribution
-  glm1 <- glm(y ~ gen+pop,data=dat,family=quasipoisson(link=log))
-  anova(glm1)
-
    #> Analysis of Deviance Table
-#> 
-#> Model: quasipoisson, link: log
-#> 
-#> Response: y
-#> 
-#> Terms added sequentially (first to last)
-#> 
-#> 
-#>      Df Deviance Resid. Df Resid. Dev
-#> NULL                    54     8947.4
-#> gen  10   7111.4        44     1836.0
-#> pop   4    690.6        40     1145.4
    
-  libs(gnm)
-
-  # main-effects non-interaction model
-  gnm0 <- gnm(y ~ pop + gen, data=dat,
-              family=quasipoisson(link=log))
-  # one interaction
-  gnm1 <- gnm(y ~ pop + gen + Mult(pop,gen,inst=1), data=dat,
-              family=quasipoisson(link=log))
-
    #> Initialising
-#> Running start-up iterations..
-#> Running main iterations.........................................................
-#> ..........................
-#> Done
      # two interactions
-  gnm2 <- gnm(y ~ pop + gen + Mult(pop,gen,inst=1) + Mult(pop,gen,inst=2),
-              data=dat,
-              family=quasipoisson(link=log))
-
    #> Initialising
-#> Running start-up iterations..
-#> Running main iterations.............
-#> Done
    
-  # anova(gnm0, gnm1, gnm2, test="F")
-  # only 2, not 3 axes needed
-
-  # match vaneeuwijk table 2
-  # anova(gnm2)
-  ##                          Df Deviance Resid. Df Resid. Dev
-  ## NULL                                        54     8947.4
-  ## pop                       4    690.6        50     8256.8
-  ## gen                      10   7111.4        40     1145.4
-  ## Mult(pop, gen, inst = 1) 13    716.0        27      429.4
-  ## Mult(pop, gen, inst = 2) 11    351.1        16       78.3
-
-  # compare residual qq plots from models
-  op <- par(mfrow=c(2,2))
-  plot(lm1, which=2, main="LM")
-  plot(glm1, which=2, main="GLM")
-  plot(gnm0, which=2, main="GNM, no interaction")
-  plot(gnm2, which=2, main="GNM, 2 interactions")
-
      par(op)
-
-  # extract interaction-term coefficients, make a biplot
-  pops <- pickCoef(gnm2, "[.]pop")
-  gens <- pickCoef(gnm2, "[.]gen")
-  coefs <- coef(gnm2)
-  A <- matrix(coefs[pops], nc = 2)
-  B <- matrix(coefs[gens], nc = 2)
-  A2=scale(A)
-  B2=scale(B)
-  rownames(A2) <- levels(dat$pop)
-  rownames(B2) <- levels(dat$gen)
-  # near-match with vaneeuwijk figure 1
-  biplot(A2,B2, expand=2.5,xlim=c(-2,2),ylim=c(-2,2),
-         main="vaneeuwijk.nematodes - GAMMI biplot")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vargas.txe-1.png b/docs/reference/vargas.txe-1.png deleted file mode 100644 index 102b2e42..00000000 Binary files a/docs/reference/vargas.txe-1.png and /dev/null differ diff --git a/docs/reference/vargas.txe-2.png b/docs/reference/vargas.txe-2.png deleted file mode 100644 index 5eab3fcc..00000000 Binary files a/docs/reference/vargas.txe-2.png and /dev/null differ diff --git a/docs/reference/vargas.txe-3.png b/docs/reference/vargas.txe-3.png deleted file mode 100644 index b9dc05a5..00000000 Binary files a/docs/reference/vargas.txe-3.png and /dev/null differ diff --git a/docs/reference/vargas.txe.html b/docs/reference/vargas.txe.html deleted file mode 100644 index cd1e47f9..00000000 --- a/docs/reference/vargas.txe.html +++ /dev/null @@ -1,288 +0,0 @@ - - - - - - - - -Treatment x environment interaction in agronomy trials — vargas.txe • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Treatment x environment interaction in agronomy trials

    - - -
    - -
    - -

    Treatment x environment interaction in agronomy trials

    - -
    - - 
    data("vargas.txe.covs")
-data("vargas.txe.yield")
    - -

    Format

    - - - -

    The 'vargas.txe.covs' data has 10 years of measurements on 28 - environmental covariates:

    -
    year

    year

    -
    MTD

    mean maximum temperature in December

    -
    MTJ

    mean maximum temperature in January

    -
    MTF

    mean maximum temperature in February

    -
    MTM

    mean maximum temperature in March

    -
    MTA

    mean maximum temperature in April

    -
    mTD

    mean minimum temperature in December

    -
    mTJ

    mean minimum temperature in January

    -
    mTF

    mean minimum temperature in February

    -
    mTM

    mean minimum temperature in March

    -
    mTA

    mean minimum temperature in April

    -
    mTUD

    mean minimum temperature in December

    -
    mTUJ

    mean minimum temperature in January

    -
    mTUF

    mean minimum temperature in February

    -
    mTUM

    mean minimum temperature in March

    -
    mTUA

    mean minimum temperature in April

    -
    PRD

    total monthly precipitation in December

    -
    PRJ

    total monthly precipitation in Jan

    -
    PRF

    total monthly precipitation in Feb

    -
    PRM

    total monthly precipitation in Mar

    -
    SHD

    sun hours per day in Dec

    -
    SHJ

    sun hours per day in Jan

    -
    SHF

    sun hours per day in Feb

    -
    EVD

    total monthly evaporation in Dec

    -
    EVJ

    total monthly evaporation in Jan

    -
    EVF

    total monthly evaporation in Feb

    -
    EVM

    total monthly evaporation in Mar

    -
    EVA

    total monthly evaporation in Apr

    -
    - -

    The 'vargas.txe.yield' dataframe contains 240 observations on - three variables

    -
    year

    Year

    -
    trt

    Treatment. See details section

    -
    yield

    Grain yield, kg/ha

    -
    - - - -

    Details

    - - - -

    The treatment names indicate:

    - - - - - - - - - -
    Tdeep knife
    tno deep knife
    Ssesbania
    ssoybean
    Mchicken manure
    mno chicken manure
    0no nitrogen
    n100 kg/ha nitrogen
    N200 kg/ha nitrogen
    - - - -

    Source

    - - - -

    Vargas, Mateo and Crossa, Jose and van Eeuwijk, Fred and Sayre, - Kenneth D. and Reynolds, Matthew P. (2001). - Interpreting Treatment x Environment Interaction in Agronomy Trials. - Agron. J., 93, 949-960. Table A1, A3. - https://doi.org/10.2134/agronj2001.934949x

    -

    Used with permission of Jose Crossa.

    - - -

    Examples

    - 
    
-  library(agridat)
-  data(vargas.txe.covs)
-  data(vargas.txe.yield)
-
-  libs(reshape2)
-  libs(lattice)
-  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-  Z <- vargas.txe.yield
-  Z <- acast(Z, year ~ trt, value.var='yield')
-  levelplot(Z, col.regions=redblue,
-            main="vargas.txe.yield", xlab="year", ylab="treatment",
-            scales=list(x=list(rot=90)))
    
-  # Double-centered like AMMI
-  Z <- sweep(Z, 1, rowMeans(Z))
-  Z <- sweep(Z, 2, colMeans(Z))
-  # Vargas figure 1
-  biplot(prcomp(Z, scale.=FALSE), main="vargas.txe.yield")
    
-  # Now, PLS relating the two matrices
-  U <- vargas.txe.covs
-  U <- scale(U) # Standardized covariates
-  libs(pls)
-  m1 <- plsr(Z~U)
-  # Vargas Fig 2, flipped vertical/horizontal
-  biplot(m1, which="x", var.axes=TRUE)
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.3.0.9100.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vargas.wheat1-1.png b/docs/reference/vargas.wheat1-1.png deleted file mode 100644 index 93131ea0..00000000 Binary files a/docs/reference/vargas.wheat1-1.png and /dev/null differ diff --git a/docs/reference/vargas.wheat1.html b/docs/reference/vargas.wheat1.html deleted file mode 100644 index f1914971..00000000 --- a/docs/reference/vargas.wheat1.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - - - - -Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Wheat yields in 7 years with genetic and environment covariates

    - - -
    - -
    -

    Yield of Durum wheat, 7 genotypes, 6 years, with 16 genotypic variates - and 16 environment variates.

    -
    - - 
    data("vargas.wheat1.covs")
-data("vargas.wheat1.traits")
    - - -

    Format

    - - - -

    The vargas.wheat1.covs dataframe has 6 observations on the following 17 variables.

    -
    year

    year, 1990-1995

    -
    MTD

    Mean daily max temperature December, deg C

    -
    MTJ

    January

    -
    MTF

    February

    -
    MTM

    March

    -
    mTD

    Mean daily minimum temperature December, deg C

    -
    mTJ

    January

    -
    mTF

    February

    -
    mTM

    March

    -
    PRD

    Monthly precipitation in December, mm

    -
    PRJ

    January

    -
    PRF

    February

    -
    PRM

    March

    -
    SHD

    a numeric vector

    -
    SHJ

    January

    -
    SHF

    February

    -
    SHM

    March

    - -
    - -

    The vargas.wheat1.traits dataframe has 126 observations on the following 19 variables.

    -
    year

    year, 1990-1995

    -
    rep

    replicate, 3 levels

    -
    gen

    genotype, 7 levels

    -
    yield

    yield, kg/ha

    -
    ANT

    anthesis, days after emergence

    -
    MAT

    maturity, days after emergence

    -
    GFI

    grainfill, MAT-ANT

    -
    PLH

    plant height, cm

    -
    BIO

    biomass above ground, kg/ha

    -
    HID

    harvest index

    -
    STW

    straw yield, kg/ha

    -
    NSM

    spikes / m^2

    -
    NGM

    grains / m^2

    -
    NGS

    grains per spike

    -
    TKW

    thousand kernel weight, g

    -
    WTI

    weight per tiller, g

    -
    SGW

    spike grain weight, g

    -
    VGR

    vegetative growth rate, kg/ha/day, STW/ANT

    -
    KGR

    kernel growth rate, mg/kernel/day

    - - -
    - - -

    Details

    - - - -

    Conducted in Ciudad Obregon, Mexico.

    -

    Source

    - -

    Mateo Vargas and Jose Crossa and Ken Sayre and Matthew Renolds and - Martha E Ramirez and Mike Talbot, 1998. - Interpreting Genotype x Environment Interaction in Wheat by - Partial Least Squares Regression, Crop Science, 38, 679--689. - https://doi.org/10.2135/cropsci1998.0011183X003800030010x

    -

    Data provided by Jose Crossa.

    - -

    Examples

    - 
    
-library(agridat)
-
-# \dontrun{
-
-  data(vargas.wheat1.covs)
-  data(vargas.wheat1.traits)
-
-  libs(pls)
-  libs(reshape2)
-  
-  # Yield as a function of non-yield traits
-  Y0 <- vargas.wheat1.traits[,c('gen','rep','year','yield')]
-  Y0 <- acast(Y0, gen ~ year, value.var='yield', fun=mean)
-  Y0 <- sweep(Y0, 1, rowMeans(Y0))
-  Y0 <- sweep(Y0, 2, colMeans(Y0)) # GxE residuals
-  Y1 <- scale(Y0) # scaled columns
-  X1 <- vargas.wheat1.traits[, -4] # omit yield
-  X1 <- aggregate(cbind(ANT,MAT,GFI,PLH,BIO,HID,STW,NSM,NGM,
-                        NGS,TKW,WTI,SGW,VGR,KGR) ~ gen, data=X1, FUN=mean)
-  rownames(X1) <- X1$gen
-  X1$gen <- NULL
-  X1 <- scale(X1) # scaled columns
-  m1 <- plsr(Y1~X1)
-  loadings(m1)[,1,drop=FALSE] # X loadings in Table 1 of Vargas
-
    #>          Comp 1
-#> ANT -0.21589859
-#> MAT -0.15861226
-#> GFI  0.14008021
-#> PLH  0.31638335
-#> BIO  0.29630833
-#> HID  0.34930528
-#> STW -0.03876374
-#> NSM -0.03472883
-#> NGM  0.34359779
-#> NGS  0.35502558
-#> TKW -0.19102141
-#> WTI  0.20568622
-#> SGW  0.34543954
-#> VGR  0.21702042
-#> KGR -0.32935119
    
-  biplot(m1, cex=.5, which="x", var.axes=TRUE,
-         main="vargas.wheat1 - gen ~ trait") # Vargas figure 2a
-
    
-  # Yield as a function of environment covariates
-  Y2 <- t(Y0)
-  X2 <- vargas.wheat1.covs
-  rownames(X2) <- X2$year
-  X2$year <- NULL
-  Y2 <- scale(Y2)
-  X2 <- scale(X2)
-  
-  m2 <- plsr(Y2~X2)
-  loadings(m2)[,1,drop=FALSE] # X loadings in Table 2 of Vargas
-
    #>          Comp 1
-#> MTD -0.29946437
-#> MTJ  0.14665034
-#> MTF  0.07040468
-#> MTM  0.40236460
-#> mTD  0.32859831
-#> mTJ  0.18260903
-#> mTF  0.25749215
-#> mTM  0.14059032
-#> PRD -0.08387786
-#> PRJ -0.12277326
-#> PRF -0.03457021
-#> PRM -0.21542249
-#> SHD -0.38252353
-#> SHJ -0.05597998
-#> SHF -0.43981570
-#> SHM  0.37609945
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vargas.wheat2-1.png b/docs/reference/vargas.wheat2-1.png deleted file mode 100644 index 8930e7d8..00000000 Binary files a/docs/reference/vargas.wheat2-1.png and /dev/null differ diff --git a/docs/reference/vargas.wheat2-2.png b/docs/reference/vargas.wheat2-2.png deleted file mode 100644 index 97859860..00000000 Binary files a/docs/reference/vargas.wheat2-2.png and /dev/null differ diff --git a/docs/reference/vargas.wheat2-3.png b/docs/reference/vargas.wheat2-3.png deleted file mode 100644 index 0c339479..00000000 Binary files a/docs/reference/vargas.wheat2-3.png and /dev/null differ diff --git a/docs/reference/vargas.wheat2.html b/docs/reference/vargas.wheat2.html deleted file mode 100644 index 479879e2..00000000 --- a/docs/reference/vargas.wheat2.html +++ /dev/null @@ -1,280 +0,0 @@ - - - - - - - - -Multi-environment trial of wheat with environmental covariates — vargas.wheat2 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of wheat with environmental covariates

    - - -
    - -
    -

    The yield of 8 wheat genotypes was measured in 21 low-humidity - environments. Each environment had 13 covariates recorded.

    -
    - - 
    data("vargas.wheat2.covs")
-data("vargas.wheat2.yield")
    - - -

    Format

    - - - -

    The 'vargas.wheat2.covs' data frame has 21 observations on the following 14 variables.

    -
    env

    environment

    -
    CYC

    length of growth cycle in days

    -
    mTC

    mean daily minimum temperature in degrees Celsius

    -
    MTC

    mean daily maximum temperature

    -
    SHC

    sun hours per day

    -
    mTV

    mean daily minimum temp during vegetative stage

    -
    MTV

    mean daily maximum temp during vegetative stage

    -
    SHV

    sun hours per day during vegetative stage

    -
    mTS

    mean daily minimum temp during spike growth stage

    -
    MTS

    mean daily maximum temp during spike growth stage

    -
    SHS

    sun hours per day during spike growth stage

    -
    mTG

    mean daily minimum temp during grainfill stage

    -
    MTG

    mean daily maximum temp during grainfill stage

    -
    SHG

    sun hours per day during grainfill stage

    - -
    - -

    The 'vargas.wheat2.yield' data frame has 168 observations on the following 3 variables.

    -
    env

    environment

    -
    gen

    genotype

    -
    yield

    yield (kg/ha)

    - -
    - - -

    Details

    - - - -

    Grain yields (kg/ha) for 8 wheat genotypes at 21 low-humidity - environments grown during 1990-1994. The data is environment-centered - and genotype-centered. The rows and columns of the GxE matrix have - mean zero. The locations of the experiments were:

    - - - - - - - -
    OBDCiudad Obregon, Mexico, planted in December
    SUDWad Medani, Sudan
    TLDTlaltizapan, Mexico, planted in December
    TLFTlaltizapan, Mexico, planted in February
    INDDharwar, India
    SYRAleppo, Syria
    NIGKadawa, Nigeria
    - - - -

    Source

    - -

    Mateo Vargas and Jose Crossa and Ken Sayre and Matthew Renolds and - Martha E Ramirez and Mike Talbot, 1998. - Interpreting Genotype x Environment Interaction in Wheat by - Partial Least Squares Regression, Crop Science, 38, 679--689. - https://doi.org/10.2135/cropsci1998.0011183X003800030010x

    -

    Data provided by Jose Crossa.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  libs(pls,reshape2)
-  
-  data(vargas.wheat2.covs)
-  datc <- vargas.wheat2.covs
-
-  data(vargas.wheat2.yield)
-  daty <- vargas.wheat2.yield
-
-  # Cast to matrix
-  daty <- acast(daty, env ~ gen, value.var='yield')
-  rownames(datc) <- datc$env
-  datc$env <- NULL
-  
-  # The pls package centers, but does not (by default) use scaled covariates
-  # Vargas says you should
-  # daty <- scale(daty)
-  datc <- scale(datc)
-
-  m2 <- plsr(daty ~ datc)
-
-  # Plot predicted vs observed for each genotype using all components
-  plot(m2)
-
    
-  # Loadings
-  # plot(m2, "loadings", xaxt='n')
-  # axis(1, at=1:ncol(datc), labels=colnames(datc), las=2)
-
-  # Biplots
-  biplot(m2, cex=.5, which="y", var.axes=TRUE,
-         main="vargas.wheat2 - daty ~ datc") # Vargas figure 2a
-
      biplot(m2, cex=.5, which="x", var.axes=TRUE) # Vectors form figure 2 b
-
      # biplot(m2, cex=.5, which="scores", var.axes=TRUE)
-  # biplot(m2, cex=.5, which="loadings", var.axes=TRUE)
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/verbyla.lupin-1.png b/docs/reference/verbyla.lupin-1.png deleted file mode 100644 index f5bc34c7..00000000 Binary files a/docs/reference/verbyla.lupin-1.png and /dev/null differ diff --git a/docs/reference/verbyla.lupin-2.png b/docs/reference/verbyla.lupin-2.png deleted file mode 100644 index 8b57dd03..00000000 Binary files a/docs/reference/verbyla.lupin-2.png and /dev/null differ diff --git a/docs/reference/verbyla.lupin-3.png b/docs/reference/verbyla.lupin-3.png deleted file mode 100644 index 837c0517..00000000 Binary files a/docs/reference/verbyla.lupin-3.png and /dev/null differ diff --git a/docs/reference/verbyla.lupin-4.png b/docs/reference/verbyla.lupin-4.png deleted file mode 100644 index 49ba5d09..00000000 Binary files a/docs/reference/verbyla.lupin-4.png and /dev/null differ diff --git a/docs/reference/verbyla.lupin.html b/docs/reference/verbyla.lupin.html deleted file mode 100644 index 25187300..00000000 --- a/docs/reference/verbyla.lupin.html +++ /dev/null @@ -1,499 +0,0 @@ - - - - - - - - -Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of lupin, multiple varieties and densities

    - - -
    - -
    -

    Yield of 9 varieties of lupin at different planting densities across - 2 years and multiple locations.

    -
    - - - -

    Format

    - -

    -
    gen

    genotype, 9 varieties

    -
    site

    site, 11 levels

    -
    rep

    rep, 2-3 levels

    -
    rate

    seeding rate in plants/m^2

    -
    row

    row

    -
    col

    column

    -
    serp

    factor of 4 levels for serpentine seeding effect

    -
    linrow

    centered row position as a numeric variate (row-8.5)/10

    -
    lincol

    centered column position as a numeric variate (col-3.5)

    -
    linrate

    linear effect of seedrate, scaled (seedrate-41.92958)/10

    -
    yield

    yield in tons/hectare

    -
    year

    year, 1991-1992

    -
    loc

    location

    - -
    - -

    Details

    - -

    Nine varieties of lupin were tested for yield response to plant density - at 11 sites. The target density in 1991 was 10, 20, ..., 60 plants per - m^2, and in 1992 was 20, 30, ..., 70 plants per m^2.

    -

    Plot dimensions are not given.

    -

    The variety Myallie was grown only in 1992.

    -

    Each site had 2 reps in 1991 and 3 reps in - 1992. Each rep was laid out as a factorial RCB design; one randomization was - used for all sites in 1991 and one (different) randomization was used - for all sites in 1992. (This was confirmed with the principal - investigator.)

    -

    In 1991 at the Mt. Barker location, the data for columns - 5 and 6 was discarded due to problems with weeds.

    -

    Variety 'Myallie' was called '84L:439' in Verbyla 1997.

    -

    The year of release for the varieties is

    -

    - - - - - - - - - -
    Unicrop1973
    Illyarrie1979
    Yandee1980
    Danja1986
    Gungurru1988
    Yorrel1989
    Warrah1989
    Merrit1991
    Myallie1995
    - - -

    Data retrieved Oct 2010 from https://www.blackwellpublishers.co.uk/rss. - (No longer available).

    -

    Used with permission of Blackwell Publishing.

    -

    Source

    - -

    Arunas P. Verbyla and Brian R. Cullis and Michael G. Kenward - and Sue J. Welham, (1999). - The analysis of designed experiments and longitudinal data by using - smoothing splines. - Appl. Statist., 48, 269--311. - https://doi.org/10.1111/1467-9876.00154

    -

    Arunas P. Verbyla and Brian R. Cullis and Michael G. Kenward - and Sue J. Welham, (1997). - The analysis of designed experiments and longitudinal data by using - smoothing splines. - University of Adelaide, Department of Statistics, Research Report - 97/4. - https://https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.808

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(verbyla.lupin)
-  dat <- verbyla.lupin
-
-  # The same RCB randomization was used at all sites in each year
-  libs(desplot)
-  desplot(dat, gen~col+row|site,
-          out1=rep, num=rate,
-          # aspect unknown
-          main="verbyla.lupin - experiment design")
-
    
-
-  # Figure 3 of Verbyla
-  libs(lattice)
-  foo <- xyplot(yield ~ rate|loc*gen, data=dat, subset=year==92,
-                type=c('p','smooth'), cex=.5,
-                main="verbyla.lupin: 1992 yield response curves",
-                xlab="Seed rate (plants/m^2)",
-                ylab="Yield (tons/ha)",
-                strip=strip.custom(par.strip.text=list(cex=.7)))
-
-  libs(latticeExtra) # for useOuterStrips
-  useOuterStrips(foo,
-                 strip=strip.custom(par.strip.text=list(cex=.7)),
-                 strip.left=strip.custom(par.strip.text=list(cex=.7)))
-
    
-
-  # ----------
-
-  libs(asreml,lucid)
-  # asreml 4
-    
-  # We try to reproduce the analysis of Verbyla 1999.
-  # May not be exactly the same, but is pretty close.
-  
-  # Check nlevels for size of random-coefficient structures
-  # length(with(dat, table(gen)))      # 9 varieties for RC1
-  # length(with(dat, table(gen,site))) # 99 site:gen combinations for RC2
-  
-  # Make row and col into factors
-  dat <- transform(dat, colf=factor(col), rowf=factor(row))
-  # sort for asreml
-  dat <- dat[order(dat$site, dat$rowf, dat$colf),]
-    
-  # Make site names more useful for plots
-  # dat <- transform(dat, site=factor(paste0(year,".",substring(loc,1,4))))
-    
-  # Initial model from top of Verbyla table 9.
-  m0 <- asreml(yield ~ 1
-               + site
-               + linrate
-               + site:linrate,
-               data = dat,
-               random = ~ spl(rate)
-               + dev(rate)
-               + site:spl(rate)
-               + site:dev(rate)
-               + str(~gen+gen:linrate, ~us(2):id(9)) # RC1
-               + gen:spl(rate)
-               + gen:dev(rate)
-               + str(~site:gen+site:gen:linrate, ~us(2):id(99)) # RC2
-               + site:gen:spl(rate)
-               + site:gen:dev(rate),
-               residual = ~ dsum( ~ ar1(rowf):ar1(colf)|site) # Spatial AR1 x AR1
-               )
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:41 2020
-#> Spline: design points closer than 0.006 have been merged.
    #> Warning: US updates modified 2 times in iteration 1 to remain positive definite.
    #>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1035.181           1.0   1370 17:50:41    0.5 (16 restrained)
-#>  2      1265.567           1.0   1370 17:50:42    0.4 (15 restrained)
    #> Warning: US updates modified 2 times in iteration 3 to remain positive definite.
    #>  3      1433.305           1.0   1370 17:50:42    0.3 (13 restrained)
    #> Warning: US updates modified 1 times in iteration 4 to remain positive definite.
    #>  4      1491.898           1.0   1370 17:50:42    0.3 (10 restrained)
-#>  5      1530.115           1.0   1370 17:50:43    0.3 (5 restrained)
-#>  6      1535.456           1.0   1370 17:50:43    0.3 (5 restrained)
-#>  7      1537.134           1.0   1370 17:50:43    0.3 (3 restrained)
-#>  8      1537.495           1.0   1370 17:50:44    0.3 (1 restrained)
-#>  9      1537.559           1.0   1370 17:50:44    0.3
-#> 10      1537.563           1.0   1370 17:50:44    0.3
    #> Warning: US variance structures were modified in 5 instances to make them positive definite
    #> Warning: Some components changed by more than 1% on the last iteration.
      m0 <- update(m0)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:44 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1537.564           1.0   1370 17:50:45    0.4
-#>  2      1537.564           1.0   1370 17:50:45    0.3
    #> Warning: Some components changed by more than 1% on the last iteration.
      m0 <- update(m0)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:45 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1537.564           1.0   1370 17:50:46    0.4
-#>  2      1537.564           1.0   1370 17:50:46    0.3
    #> Warning: Some components changed by more than 1% on the last iteration.
      m0 <- update(m0)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:46 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1537.564           1.0   1370 17:50:47    0.4
-#>  2      1537.564           1.0   1370 17:50:47    0.3
    #> Warning: Some components changed by more than 1% on the last iteration.
      m0 <- update(m0)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:47 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1537.564           1.0   1370 17:50:47    0.4
-#>  2      1537.564           1.0   1370 17:50:48    0.3
    #> Warning: Some components changed by more than 1% on the last iteration.
      m0 <- update(m0)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:48 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1537.564           1.0   1370 17:50:48    0.4
-#>  2      1537.564           1.0   1370 17:50:49    0.3
    
-  # Variograms match Verbyla 1999 figure 7 (scale slightly different)
-  plot(varioGram(m0), xlim=c(1:19), zlim=c(0,2),
-       main="verbyla.lupin - variogram by site")
-
      
-    # Sequence of models in Verbyla 1999 table 10
-  m1 <- update(m0, fixed= ~ .
-               + at(site, c(2,5,6,8,9,10)):lincol
-               + at(site, c(3,5,7,8)):linrow
-               + at(site, c(2,3,5,7,8,9,11)):serp
-               , random = ~ .
-               + at(site, c(3,6,7,9)):rowf
-               + at(site, c(1,2,3,9,10)):colf
-               + at(site, c(5,7,8,10)):units)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:49 2020
-#> Spline: design points closer than 0.006 have been merged.
    #> Warning: US updates modified 1 times in iteration 1 to remain positive definite.
    #>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1300.766           1.0   1339 17:50:50    0.8 (15 restrained)
-#>  2      1373.661           1.0   1339 17:50:51    0.6 (11 restrained)
    #> Warning: US updates modified 1 times in iteration 3 to remain positive definite.
    #>  3      1521.164           1.0   1339 17:50:51    0.6 (12 restrained)
    #> Warning: US updates modified 1 times in iteration 4 to remain positive definite.
    #>  4      1550.613           1.0   1339 17:50:52    0.7 (7 restrained)
-#>  5      1571.582           1.0   1339 17:50:53    0.7 (3 restrained)
-#>  6      1578.652           1.0   1339 17:50:53    0.6 (2 restrained)
-#>  7      1582.262           1.0   1339 17:50:54    0.6 (1 restrained)
-#>  8      1582.866           1.0   1339 17:50:54    0.6
-#>  9      1582.912           1.0   1339 17:50:55    0.6
-#> 10      1582.921           1.0   1339 17:50:56    0.6
-#> 11      1582.925           1.0   1339 17:50:56    0.6
-#> 12      1582.928           1.0   1339 17:50:57    0.6
-#> 13      1582.930           1.0   1339 17:50:57    0.6
    #> Warning: US variance structures were modified in 3 instances to make them positive definite
    #> Warning: Log-likelihood not converged
    #> Warning: Some components changed by more than 1% on the last iteration.
      m1 <- update(m1)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:50:58 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1582.932           1.0   1339 17:50:58    0.8
-#>  2      1582.932           1.0   1339 17:50:59    0.6
-#>  3      1582.933           1.0   1339 17:51:00    0.7
-#>  4      1582.933           1.0   1339 17:51:00    0.7
-#>  5      1582.934           1.0   1339 17:51:01    0.7
-#>  6      1582.934           1.0   1339 17:51:02    0.7
    #> Warning: Some components changed by more than 1% on the last iteration.
      
-  m2 <- update(m1,
-               random = ~ .
-               - site:gen:spl(rate) - site:gen:dev(rate))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:02 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1582.935           1.0   1339 17:51:02    0.3 (1 restrained)
-#>  2      1582.935           1.0   1339 17:51:03    0.2 (1 restrained)
-#>  3      1582.935           1.0   1339 17:51:03    0.3
      
-  m3 <- update(m2,
-               random = ~ .
-               - site:dev(rate) - gen:dev(rate))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:03 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1582.936           1.0   1339 17:51:03    0.2 (1 restrained)
-#>  2      1582.936           1.0   1339 17:51:03    0.1 (1 restrained)
-#>  3      1582.936           1.0   1339 17:51:03    0.1
      
-  m4 <- update(m3,
-               random = ~ .
-               - dev(rate))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:04 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1582.936           1.0   1339 17:51:04    0.1 (1 restrained)
-#>  2      1582.936           1.0   1339 17:51:04    0.1 (1 restrained)
-#>  3      1582.936           1.0   1339 17:51:04    0.1
      
-  m5 <- update(m4,
-               random = ~ .
-               - at(site, c(5,7,8,10)):units + at(site, c(5,7,8)):units)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:04 2020
-#> Spline: design points closer than 0.006 have been merged.
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      1582.936           1.0   1339 17:51:04    0.1
-#>  2      1582.936           1.0   1339 17:51:04    0.1
      
-  # Variance components are a pretty good match to Verbyla 1997, table 15
-  libs(lucid)
-  vc(m5)
-
    #>                               effect  component  std.error z.ratio bound %ch
-#>                            spl(rate)  0.001288  0.001019     1.3       P 0  
-#>                       spl(rate):site  0.0001556 0.0001227    1.3       P 0  
-#>            gen+gen:linrate!us(2)_1:1  0.005445  0.003733     1.5       P 0  
-#>            gen+gen:linrate!us(2)_2:1  0.001004  0.0007598    1.3       P 0  
-#>            gen+gen:linrate!us(2)_2:2  0.0003661 0.0002168    1.7       P 0  
-#>                        spl(rate):gen  0.0002057 0.0001326    1.6       P 0  
-#>  site:gen+site:gen:linrate!us(2)_1:1  0.01881   0.003402     5.5       P 0  
-#>  site:gen+site:gen:linrate!us(2)_2:1  0.00175   0.0004706    3.7       P 0  
-#>  site:gen+site:gen:linrate!us(2)_2:2  0.000167  0.00008968   1.9       P 0  
-#>                   at(site, S03):rowf  0.002594  0.006235     0.42      P 0  
-#>                   at(site, S06):rowf  0.001127  0.00226      0.5       P 0  
-#>                   at(site, S07):rowf  0.00019   0.002012     0.094     P 0.1
-#>                   at(site, S09):rowf  0.004526  0.002323     1.9       P 0  
-#>                   at(site, S01):colf  0.01041   0.00854      1.2       P 0  
-#>                   at(site, S02):colf  0.02498   0.02076      1.2       P 0  
-#>                   at(site, S03):colf  0.08167   0.07359      1.1       P 0  
-#>                   at(site, S09):colf  0.004083  0.003688     1.1       P 0  
-#>                   at(site, S10):colf  0.0004588 0.001058     0.43      P 0.2
-#>                  at(site, S05):units  0.004378  0.007786     0.56      P 0  
-#>                  at(site, S07):units  0.01323   0.002712     4.9       P 0  
-#>                  at(site, S08):units  0.01302   0.004202     3.1       P 0  
-#>                           site_S01!R  0.03273   0.005654     5.8       P 0  
-#>                    site_S01!rowf!cor  0.1852    0.1417       1.3       U 0  
-#>                    site_S01!colf!cor  0.1407    0.121        1.2       U 0  
-#>                           site_S02!R  0.03999   0.00753      5.3       P 0  
-#>                    site_S02!rowf!cor  0.3103    0.135        2.3       U 0  
-#>                    site_S02!colf!cor  0.1673    0.1369       1.2       U 0  
-#>                           site_S03!R  0.0505    0.0157       3.2       P 0  
-#>                    site_S03!rowf!cor  0.4707    0.179        2.6       U 0  
-#>                    site_S03!colf!cor -0.01851   0.2423      -0.076     U 0  
-#>                           site_S04!R  0.03301   0.005216     6.3       P 0  
-#>                    site_S04!rowf!cor -0.04041   0.1159      -0.35      U 0  
-#>                    site_S04!colf!cor  0.1109    0.1261       0.88      U 0  
-#>                           site_S05!R  0.01772   0.008592     2.1       P 0  
-#>                    site_S05!rowf!cor  0.5623    0.2644       2.1       U 0  
-#>                    site_S05!colf!cor  0.08337   0.2043       0.41      U 0  
-#>                           site_S06!R  0.02409   0.003996     6         P 0  
-#>                    site_S06!rowf!cor  0.3216    0.08428      3.8       U 0  
-#>                    site_S06!colf!cor  0.2214    0.1186       1.9       U 0  
-#>                           site_S07!R  0.03658   0.02023      1.8       P 0  
-#>                    site_S07!rowf!cor  0.9019    0.06323     14         U 0  
-#>                    site_S07!colf!cor  0.5703    0.2383       2.4       U 0  
-#>                           site_S08!R  0.01813   0.006293     2.9       P 0  
-#>                    site_S08!rowf!cor  0.7429    0.1436       5.2       U 0  
-#>                    site_S08!colf!cor  0.1768    0.2027       0.87      U 0  
-#>                           site_S09!R  0.01725   0.002548     6.8       P 0  
-#>                    site_S09!rowf!cor  0.2509    0.09548      2.6       U 0  
-#>                    site_S09!colf!cor -0.03375   0.1213      -0.28      U 0  
-#>                           site_S10!R  0.01035   0.001703     6.1       P 0  
-#>                    site_S10!rowf!cor  0.5222    0.07746      6.7       U 0.1
-#>                    site_S10!colf!cor  0.1709    0.09266      1.8       U 0  
-#>                           site_S11!R  0.05398   0.006905     7.8       P 0  
-#>                    site_S11!rowf!cor  0.1939    0.09649      2         U 0  
-#>                    site_S11!colf!cor  0.1034    0.1002       1         U 0  
      .001004/sqrt(.005446*.0003662) # .711 correlation for RC1
-
    #> [1] 0.7109445
      .00175/sqrt(.01881*.000167) # .987 correlation for RC2
-
    #> [1] 0.9873825
      
-  # Matches Verbyla 1999 figure 5
-  plot(varioGram(m5),
-       main="verbyla.lupin - final model variograms",
-       xlim=c(1:19), zlim=c(0,1.5))
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vold.longterm-1.png b/docs/reference/vold.longterm-1.png deleted file mode 100644 index 6f21e2ad..00000000 Binary files a/docs/reference/vold.longterm-1.png and /dev/null differ diff --git a/docs/reference/vold.longterm-2.png b/docs/reference/vold.longterm-2.png deleted file mode 100644 index f55d74f6..00000000 Binary files a/docs/reference/vold.longterm-2.png and /dev/null differ diff --git a/docs/reference/vold.longterm-3.png b/docs/reference/vold.longterm-3.png deleted file mode 100644 index 5f5d7b86..00000000 Binary files a/docs/reference/vold.longterm-3.png and /dev/null differ diff --git a/docs/reference/vold.longterm-4.png b/docs/reference/vold.longterm-4.png deleted file mode 100644 index c6f8b684..00000000 Binary files a/docs/reference/vold.longterm-4.png and /dev/null differ diff --git a/docs/reference/vold.longterm.html b/docs/reference/vold.longterm.html deleted file mode 100644 index 6bbeed7d..00000000 --- a/docs/reference/vold.longterm.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Long-term barley yields at different fertilizer levels — vold.longterm • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Long-term barley yields at different fertilizer levels

    - - -
    - -
    -

    Long-term barley yields at different fertilizer levels

    -
    - - 
    data("vold.longterm")
    - - -

    Format

    - -

    A data frame with 76 observations on the following 3 variables.

    -
    year

    year

    -
    nitro

    nitrogen fertilizer, grams/m^2

    -
    yield

    yield, grams/m^2

    - -
    - -

    Details

    - -

    Trials conducted at Osaker, Norway. - Nitrogen fertilizer amounts were increased by twenty percent in 1978.

    -

    Vold (1998) fit a Michaelis-Menten type equation with a different - maximum in each year and a decreasing covariate for non-fertilizer - nitrogen.

    -

    Miguez used a non-linear mixed effects model with asymptotic curve.

    -

    Source

    - -

    Arild Vold (1998). - A generalization of ordinary yield response functions. - Ecological modelling, 108, 227-236. - https://doi.org/10.1016/S0304-3800(98)00031-3

    -

    References

    - -

    Fernando E. Miguez (2008). - Using Non-Linear Mixed Models for Agricultural Data.

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(vold.longterm)
-  dat <- vold.longterm
-  
-  libs(lattice)
-  foo1 <- xyplot(yield ~ nitro | factor(year), data = dat,
-                 as.table=TRUE, type = "o",
-                 main=list("vold.longterm", cex=1.5),
-                 xlab = list("N fertilizer",cex=1.5,font=4),
-                 ylab = list("Yield", cex=1.5))
-  
-
-  # Long term trend shows decreasing yields
-  xyplot(yield ~ year , data = dat, group=nitro, type='o',
-         main="vold.longterm - yield level by nitrogen",
-         auto.key=list(columns=4))
-
    
-
-  if(0){
-  # Global model
-  m1.nls <- nls(yield ~ SSasymp(nitro, max, int, lograte), data=dat)
-  summary(m1.nls)
-  libs(MASS) # for 'confint'
-  confint(m1.nls)
-  
-  # Raw data plus global model.  Year variation not modeled.
-  pdat <- data.frame(nitro=seq(0,14,0.5))
-  pdat$pred <- predict(m1.nls, newdata=pdat)
-    libs(latticeExtra) # for layers
-    foo1 + xyplot(pred ~ nitro , data = pdat,
-                  as.table=TRUE, type='l', col='red', lwd=2)
-  }
-
-  # Separate fit for each year.  Overfitting with 3x19=57 params.
-  libs(nlme)
-  m2.lis <- nlsList(yield ~ SSasymp(nitro,max,int,lograte) | year, data=dat)
-  plot(intervals(m2.lis),layout = c(3,1)) # lograte might be same for each year
-
    
-
-  # Fixed overall asymptotic model, plus random deviations for each year
-  # Simpler code, but less clear about what model is fit: m3.lme <- nlme(m2.lis)
-  libs(nlme)
-  m3.lme <- nlme(yield ~ SSasymp(nitro, max, int, lograte), data=dat,
-                 groups = ~ year,
-                 fixed = list(max~1, int~1, lograte~1),
-                 random= max + int + lograte ~ 1,
-                 start= c(max=300, int=100, rate=-2))
-  
-  ## # Fixed effects are similar for the nls/lme models
-  ## coef(m1.nls)
-  ## fixef(m3.lme)
-  ## # Random effects are normally distributed
-  ## qqnorm(m3.lme, ~ ranef(.),col="black")
-  ## # Note the trend in intercept effects over time
-  ## plot(ranef(m3.lme),layout=c(3,1))
-  
-  ## # Correlation between int,lograte int,max may not be needed
-  ## intervals(m3.lme,which="var-cov")
-  ## pairs(m3.lme,pch=19,col="black")
-  
-  ## # Model with int uncorrelated with max,lograte.  AIC is worse.
-  ## # fit4.lm3 <- update(m3.lme, random=pdBlocked(list(max+lograte~1,int ~ 1)))
-  ## # intervals(fit4.lm3, which="var-cov")
-  ## # anova(m3.lme, fit4.lm3)
-  
-  # Plot the random-effect model.  Excellent fit with few parameters.
-  pdat2 <- expand.grid(year=1970:1988, nitro=seq(0,15,length=50))
-  pdat2$pred <- predict(m3.lme, new=pdat2)
-  pdat2$predf <- predict(m3.lme, new=pdat2, level=0)
-  foo1 <- update(foo1, type='p',
-                 key=simpleKey(c("Observed","Fixed","Random"),
-                               col=c("blue","red","darkgreen"),
-                               points=FALSE, columns=3))
-  libs(latticeExtra)
-  foo2 <- xyplot(pred~nitro|year, data=pdat2, type='l', col="darkgreen", lwd=2)
-  foo3 <- xyplot(predf~nitro|year, data=pdat2, type='l', col="red",lwd=1)
-  foo1 + foo2 + foo3
-
    
-  
-  ## # Income is maximized at about 15
-  ## pdat2 <- transform(pdat2, income = predf*2 - 7*nitro)
-  ## with(pdat2, xyplot(income~nitro))
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/vsn.lupin3-1.png b/docs/reference/vsn.lupin3-1.png deleted file mode 100644 index 55ff88c4..00000000 Binary files a/docs/reference/vsn.lupin3-1.png and /dev/null differ diff --git a/docs/reference/vsn.lupin3-2.png b/docs/reference/vsn.lupin3-2.png deleted file mode 100644 index 05b76b5b..00000000 Binary files a/docs/reference/vsn.lupin3-2.png and /dev/null differ diff --git a/docs/reference/vsn.lupin3.html b/docs/reference/vsn.lupin3.html deleted file mode 100644 index f5c25ed4..00000000 --- a/docs/reference/vsn.lupin3.html +++ /dev/null @@ -1,385 +0,0 @@ - - - - - - - - -Multi-environment trial of lupin, early generation trial — vsn.lupin3 • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of lupin, early generation trial

    - - -
    - -
    -

    Early generation lupin trial with 3 sites, 330 test lines, 6 check lines.

    -
    - - - -

    Format

    - -

    A data frame with 1236 observations on the following 5 variables.

    -
    site

    site, levels S1 S2 S3

    -
    col

    column

    -
    row

    row

    -
    gen

    genotype

    -
    yield

    yield

    - -
    - -

    Details

    - -

    An early-stage multi-environment trial, with 6 check lines and 300 - test lines. The 6 check lines were replicated in each environment.

    -

    Used with permission of Arthur Gilmour, Brian Cullis, Robin Thompson.

    -

    Source

    - -

    Multi-Environment Trials - Lupins. - https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xlupin.htm

    - -

    Examples

    - 
    # \dontrun{
-  
-  library(agridat)
-  data(vsn.lupin3)
-  dat <- vsn.lupin3
-  
-  # Split gen into check/test, make factors
-  dat <- within(dat, {
-    check <- ifelse(gen>336, 0, gen)
-    check <- ifelse(check<7, check, 7)
-    check <- factor(check)
-    test <- factor(ifelse(gen>6 & gen<337, gen, 0))
-    gen=factor(gen)
-  })
-
-  libs(desplot)
-  desplot(dat, yield~ col*row|site,
-          # midpoint="midrange",
-          # aspect unknown
-          main="vsn.lupin3 - yield")
-
      # Site 1 & 2 used same randomization
-  desplot(dat, check~ col*row|site,
-          main="vsn.lupin3: check plot placement") 
-
    
-  # asreml 4
-  libs(asreml,lucid)
-     
-  # Single-site analyses suggested random row term for site 3,
-  # random column terms for all sites,
-  # AR1 was unnecessary for the col dimension of site 3
-  dat <- transform(dat, colf=factor(col), rowf=factor(row))
-  dat <- dat[order(dat$site, dat$colf, dat$rowf),] # Sort for asreml
-  m1 <- asreml(yield ~ site + check:site, data=dat,
-               random = ~ at(site):colf + at(site,3):rowf + test,
-               residual = ~ dsum( ~ ar1(colf):ar1(rowf) +
-                                    id(colf):ar1(rowf) | site,
-                                 levels=list(1:2, 3)
-                                 ) )
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:09 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -615.620           1.0   1213 17:51:09    0.0 (3 restrained)
-#>  2      -424.081           1.0   1213 17:51:09    0.0
-#>  3      -355.609           1.0   1213 17:51:09    0.0
-#>  4      -325.217           1.0   1213 17:51:09    0.0
-#>  5      -315.664           1.0   1213 17:51:10    0.0
-#>  6      -314.402           1.0   1213 17:51:10    0.0
-#>  7      -314.276           1.0   1213 17:51:10    0.0
-#>  8      -314.263           1.0   1213 17:51:10    0.0
-#>  9      -314.262           1.0   1213 17:51:10    0.0
      m1$loglik
-
    #> [1] -314.2617
      ## [1] -314.2616
-  
-  vc(m1)
-
    #>             effect component std.error z.ratio bound %ch
-#>  at(site, S1):colf   0.6234   0.4287       1.5     P 0.3
-#>  at(site, S2):colf   0.1592   0.1141       1.4     P 0.2
-#>  at(site, S3):colf   0.04832  0.02618      1.8     P 0  
-#>  at(site, S3):rowf   0.0235   0.008483     2.8     P 0  
-#>               test   0.1031   0.01468      7       P 0  
-#>          site_S1!R   2.77     0.313        8.8     P 0.1
-#>   site_S1!colf!cor   0.196    0.05375      3.6     U 0.1
-#>   site_S1!rowf!cor   0.6501   0.03873     17       U 0.1
-#>          site_S2!R   0.9924   0.1078       9.2     P 0  
-#>   site_S2!colf!cor   0.2868   0.05246      5.5     U 0  
-#>   site_S2!rowf!cor   0.5743   0.04209     14       U 0  
-#>          site_S3!R   0.1204   0.01875      6.4     P 0  
-#>   site_S3!rowf!cor   0.6394   0.06324     10       U 0  
      ##                      effect component std.error z.ratio constr
-  ##  at(site, S1):colf!colf.var   0.6228   0.4284       1.5    pos
-  ##  at(site, S2):colf!colf.var   0.159    0.1139       1.4    pos
-  ##  at(site, S3):colf!colf.var   0.04832  0.02618      1.8    pos
-  ##  at(site, S3):rowf!rowf.var   0.0235   0.008483     2.8    pos
-  ##               test!test.var   0.1031   0.01468      7      pos
-  ##            site_S1!variance   2.771    0.314        8.8    pos
-  ##            site_S1!colf.cor   0.1959   0.05375      3.6  uncon
-  ##            site_S1!rowf.cor   0.6503   0.03873     17    uncon
-  ##            site_S2!variance   0.9926   0.1079       9.2    pos
-  ##            site_S2!colf.cor   0.2868   0.05246      5.5  uncon
-  ##            site_S2!rowf.cor   0.5744   0.0421      14    uncon
-  ##            site_S3!variance   0.1205   0.01875      6.4    pos
-  ##            site_S3!rowf.cor   0.6394   0.06323     10    uncon
-  
-  # Add site:test
-  m2 <- update(m1, random=~. + site:test)
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:10 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -733.725           1.0   1213 17:51:10    0.1 (7 restrained)
-#>  2      -389.989           1.0   1213 17:51:10    0.1
-#>  3      -342.919           1.0   1213 17:51:10    0.1
-#>  4      -319.635           1.0   1213 17:51:11    0.1
-#>  5      -311.958           1.0   1213 17:51:11    0.1
-#>  6      -310.972           1.0   1213 17:51:11    0.1
-#>  7      -310.889           1.0   1213 17:51:11    0.1
-#>  8      -310.881           1.0   1213 17:51:11    0.1
-#>  9      -310.880           1.0   1213 17:51:11    0.1
      m2$loglik
-
    #> [1] -310.8795
      ## [1] -310.8794
-  
-  # CORUH structure on the site component of site:test
-  m3 <- asreml(yield ~ site + check:site, data=dat,
-               random = ~ at(site):colf + at(site,3):rowf + corh(site):test,
-               residual = ~ dsum( ~ ar1(colf):ar1(rowf) +
-                                    id(colf):ar1(rowf) | site,
-                                 levels=list(1:2, 3)  ))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:11 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -532.783           1.0   1213 17:51:11    0.1 (2 restrained)
-#>  2      -411.201           1.0   1213 17:51:11    0.0
-#>  3      -335.554           1.0   1213 17:51:11    0.0
-#>  4      -303.565           1.0   1213 17:51:11    0.0
-#>  5      -292.148           1.0   1213 17:51:12    0.0
-#>  6      -289.310           1.0   1213 17:51:12    0.1 (1 restrained)
-#>  7      -288.550           1.0   1213 17:51:12    0.1 (1 restrained)
-#>  8      -288.487           1.0   1213 17:51:12    0.0 (1 restrained)
-#>  9      -288.484           1.0   1213 17:51:12    0.0 (1 restrained)
-#> 10      -288.484           1.0   1213 17:51:12    0.0 (1 restrained)
    #> Warning: Some components changed by more than 1% on the last iteration.
      m3$loglik
-
    #> [1] -288.4837
      ## [1] -288.4837
-  
-  # Unstructured genetic variance matrix
-  m4 <- asreml(yield ~ site + check:site, data=dat,
-               random = ~ at(site):colf + at(site,3):rowf + us(site):test,
-               residual = ~ dsum( ~ ar1(colf):ar1(rowf) +
-                                    id(colf):ar1(rowf) | site,
-                                 levels=list(1:2, 3)  ))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:12 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -531.256           1.0   1213 17:51:12    0.1 (2 restrained)
    #> Warning: US updates modified 1 times in iteration 2 to remain positive definite.
    #>  2      -411.627           1.0   1213 17:51:12    0.0 (6 restrained)
    #> Warning: US updates modified 1 times in iteration 3 to remain positive definite.
    #>  3      -333.071           1.0   1213 17:51:12    0.1 (6 restrained)
    #> Warning: US updates modified 1 times in iteration 4 to remain positive definite.
    #>  4      -302.555           1.0   1213 17:51:12    0.1 (6 restrained)
    #> Warning: US updates modified 1 times in iteration 5 to remain positive definite.
    #>  5      -291.384           1.0   1213 17:51:12    0.1 (6 restrained)
-#>  6      -288.359           1.0   1213 17:51:12    0.1 (1 restrained)
-#>  7      -286.890           1.0   1213 17:51:12    0.0 (1 restrained)
-#>  8      -286.828           1.0   1213 17:51:12    0.1 (1 restrained)
-#>  9      -286.825           1.0   1213 17:51:12    0.1 (1 restrained)
-#> 10      -286.824           1.0   1213 17:51:12    0.1 (1 restrained)
    #> Warning: US variance structures were modified in 4 instances to make them positive definite
    #> Warning: Some components changed by more than 1% on the last iteration.
      m4$loglik
-
    #> [1] -286.8242
      ## [1] -286.8239
-  
-  # Note that a 3x3 unstructured matrix can be written LL'+Psi with 1 factor L
-  # Explicitly fit the factor analytic model
-  m5 <- asreml(yield ~ site + check:site, data=dat,
-               random = ~ at(site):colf + at(site,3):rowf
-               + fa(site,1, init=c(.7,.1,.1,.5,.3,.2)):test,
-               residual = ~ dsum( ~ ar1(colf):ar1(rowf) +
-                                    id(colf):ar1(rowf) | site,
-                                 levels=list(1:2, 3)  ))
-
    #> Multi-section model using the sigma parameterization.
-#> ASReml 4.1.0 Fri Dec 11 17:51:12 2020
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -530.602           1.0   1213 17:51:13    0.1 (4 restrained)
-#>  2      -380.673           1.0   1213 17:51:13    0.1 (2 restrained)
-#>  3      -310.637           1.0   1213 17:51:13    0.1 (1 restrained)
-#>  4      -294.195           1.0   1213 17:51:13    0.1 (1 restrained)
-#>  5      -288.494           1.0   1213 17:51:13    0.1 (2 restrained)
-#>  6      -287.011           1.0   1213 17:51:13    0.1 (1 restrained)
-#>  7      -286.855           1.0   1213 17:51:13    0.1 (1 restrained)
-#>  8      -286.849           1.0   1213 17:51:13    0.1 (1 restrained)
-#>  9      -286.848           1.0   1213 17:51:13    0.1 (1 restrained)
    #> Warning: Some components changed by more than 1% on the last iteration.
      m5$loglik # Same as m4
-
    #> [1] -286.8484
      ## [1] -286.8484
-  
-  # Model 4, Unstructured (symmetric) genetic variance matrix
-  un <- diag(3)
-  un[upper.tri(un,TRUE)] <- m4$vparameters[5:10]
-  round(un+t(un)-diag(diag(un)),3)
-
    #>       [,1]  [,2]  [,3]
-#> [1,] 0.992 0.158 0.132
-#> [2,] 0.158 0.072 0.078
-#> [3,] 0.132 0.078 0.122
      ##       [,1]  [,2]  [,3]
-  ## [1,] 0.992 0.158 0.132
-  ## [2,] 0.158 0.073 0.078
-  ## [3,] 0.132 0.078 0.122
-  
-  # Model 5, FA matrix = LL'+Psi.  Not quite the same as unstructured,
-  # since the FA model fixes site 2 variance at 0.
-  psi <- diag(m5$vparameters[5:7])
-  lam <- matrix(m5$vparameters[8:10], ncol=1)
-  round(tcrossprod(lam,lam)+psi,3)
-
    #>       [,1]  [,2]  [,3]
-#> [1,] 0.991 0.156 0.133
-#> [2,] 0.156 0.092 0.078
-#> [3,] 0.133 0.078 0.122
      ##       [,1]  [,2]  [,3]
-  ## [1,] 0.991 0.156 0.133
-  ## [2,] 0.156 0.092 0.078
-  ## [3,] 0.133 0.078 0.122
-
-  
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wallace.iowaland-1.png b/docs/reference/wallace.iowaland-1.png deleted file mode 100644 index 8e95d1f6..00000000 Binary files a/docs/reference/wallace.iowaland-1.png and /dev/null differ diff --git a/docs/reference/wallace.iowaland-2.png b/docs/reference/wallace.iowaland-2.png deleted file mode 100644 index c89cfead..00000000 Binary files a/docs/reference/wallace.iowaland-2.png and /dev/null differ diff --git a/docs/reference/wallace.iowaland.html b/docs/reference/wallace.iowaland.html deleted file mode 100644 index 33b25b1f..00000000 --- a/docs/reference/wallace.iowaland.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - -Iowa farmland values by county in 1925 — wallace.iowaland • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Iowa farmland values by county in 1925

    - - -
    - -
    -

    Iowa farmland values by county in 1925

    -
    - - 
    data("wallace.iowaland")
    - - -

    Format

    - -

    A data frame with 99 observations on the following 10 variables.

    -
    county

    county factor, 99 levels

    -
    fips

    FIPS code (state+county)

    -
    lat

    latitude

    -
    long

    longitude

    -
    yield

    average corn yield per acre (bu)

    -
    corn

    percent of land in corn

    -
    grain

    percent of land in small grains

    -
    untillable

    percent of land untillable

    -
    fedval

    land value (excluding buildings) per acre, 1925 federal census

    -
    stval

    land value (excluding buildings) per acre, 1925 state census

    - -
    - -

    Details

    - -

    None.

    -

    Source

    - -

    H.A. Wallace (1926). Comparative Farm-Land Values in Iowa. - The Journal of Land & Public Utility Economics, 2, 385-392. - Page 387-388. - https://doi.org/10.2307/3138610

    -

    References

    - -

    Larry Winner. Spatial Data Analysis. - https://www.stat.ufl.edu/~winner/data/iowaland.txt

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(wallace.iowaland)
-dat <- wallace.iowaland
-
-# Interesting trends involving latitude
-libs(lattice)
-splom(~dat[,-c(1:2)], type=c('p','smooth'), lwd=2, main="wallace.iowaland")
-
    
-# Means. Similar to Wallace table 1
-apply(dat[, c('yield','corn','grain','untillable','fedval')], 2, mean)
-
    #>      yield       corn      grain untillable     fedval 
-#>   39.11111   32.47475   21.55556   18.84848  118.67677 
    
-# Correlations.  Similar to Wallace table 2
-round(cor(dat[, c('yield','corn','grain','untillable','fedval')]),2)
-
    #>            yield  corn grain untillable fedval
-#> yield       1.00  0.30  0.27      -0.16   0.61
-#> corn        0.30  1.00  0.61      -0.82   0.81
-#> grain       0.27  0.61  1.00      -0.59   0.62
-#> untillable -0.16 -0.82 -0.59       1.00  -0.69
-#> fedval      0.61  0.81  0.62      -0.69   1.00
    
-m1 <- lm(fedval ~ yield + corn + grain + untillable, dat)
-summary(m1) # estimates similar to Wallace, top of p. 389
-
    #> 
-#> Call:
-#> lm(formula = fedval ~ yield + corn + grain + untillable, data = dat)
-#> 
-#> Residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -36.392  -7.797  -0.110   6.068  31.778 
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) -64.7071    17.3420  -3.731 0.000326 ***
-#> yield         3.1488     0.3717   8.472 3.24e-13 ***
-#> corn          1.8175     0.3090   5.881 6.20e-08 ***
-#> grain         0.5394     0.2566   2.102 0.038229 *  
-#> untillable   -0.5527     0.2697  -2.049 0.043270 *  
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> Residual standard error: 11.61 on 94 degrees of freedom
-#> Multiple R-squared:  0.819,	Adjusted R-squared:  0.8113 
-#> F-statistic: 106.3 on 4 and 94 DF,  p-value: < 2.2e-16
-#> 
    
-# Choropleth map
-libs(maps)
-data(county.fips)
-dat <- transform(dat, polnm = paste0('iowa,',county)) # polnm example: iowa,adair
-
-libs("latticeExtra") # for mapplot
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-mapplot(polnm~fedval , data=dat, colramp=redblue,
-        main="wallace.iowaland - Federal land values",
-        xlab="Land value, dollars per acre",
-        scales=list(draw=FALSE),
-        map=map('county', 'iowa', plot=FALSE,
-          fill=TRUE, projection="mercator"))
-
    #> Warning: 6 unmatched regions: iowa,blackhawk, iowa,buenavista, iowa,cerrogordo, iowa,d....
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/walsh.cottonprice-1.png b/docs/reference/walsh.cottonprice-1.png deleted file mode 100644 index 67d9f285..00000000 Binary files a/docs/reference/walsh.cottonprice-1.png and /dev/null differ diff --git a/docs/reference/walsh.cottonprice.html b/docs/reference/walsh.cottonprice.html deleted file mode 100644 index d6970a0d..00000000 --- a/docs/reference/walsh.cottonprice.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - - - -Acres and price of cotton 1910-1943 — walsh.cottonprice • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Acres and price of cotton 1910-1943

    - - -
    - -
    -

    Acres and price of cotton 1910-1943

    -
    - - - -

    Format

    - -

    A data frame with 34 observations on the following 9 variables.

    -
    year

    year, numeric 1910-1943

    -
    acres

    acres of cototn (1000s)

    -
    cotton

    price per pound (cents) in previous year

    -
    cottonseed

    price per ton (dollars) in previous year

    -
    combined

    cotton price/pound + 1.857 x cottonseed price/pound (cents)

    -
    index

    price index, 1911-1914=100

    -
    adjcotton

    adjusted cotton price per pound (cents) in previous year

    -
    adjcottonseed

    adjusted cottonseed price per ton (dollars) in previous year

    -
    adjcombined

    adjusted combined price/pound (cents)

    - -
    - -

    Details

    - -

    The 'index' is a price index for all farm commodities.

    -

    Source

    - -

    R.M. Walsh (1944). - Response to Price in Production of Cotton and Cottonseed, - Journal of Farm Economics, 26, 359-372. - https://doi.org/10.2307/1232237

    - -

    Examples

    - 
    
-library(agridat)
-
-data(walsh.cottonprice)
-dat <- walsh.cottonprice
-
-dat <- transform(dat, acres=acres/1000) # convert to million acres
-
-percentchg <- function(x){ # percent change from previous to current
-  ix <- 2:(nrow(dat))
-  c(NA, (x[ix]-x[ix-1])/x[ix-1])
-}
-
-# Compare percent change in acres with percent change in previous price
-# using constant dollars
-dat <- transform(dat, chga = percentchg(acres), chgp = percentchg(adjcombined))
-
-with(dat, cor(chga, chgp, use='pair')) # .501 correlation
-
    #> [1] 0.5008616
    libs(lattice)
-xyplot(chga~chgp, dat, type=c('p','r'),
-       main="walsh.cottonprice",
-       xlab="Percent change in previous price", ylab="Percent change in acres")
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wassom.brome.uniformity-1.png b/docs/reference/wassom.brome.uniformity-1.png deleted file mode 100644 index b323f3a1..00000000 Binary files a/docs/reference/wassom.brome.uniformity-1.png and /dev/null differ diff --git a/docs/reference/wassom.brome.uniformity.html b/docs/reference/wassom.brome.uniformity.html deleted file mode 100644 index 789e9420..00000000 --- a/docs/reference/wassom.brome.uniformity.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - - -Uniformity trials of bromegrass — wassom.brome.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trials of bromegrass

    - - -
    - -
    -

    Uniformity trials of bromegrass at Ames, Iowa, 1950-1951.

    -
    - - 
    data("wassom.brome.uniformity")
    - - -

    Format

    - -

    A data frame with 1296 observations on the following 3 variables.

    -
    expt

    experiment

    -
    row

    row

    -
    col

    column

    -
    yield

    forage yield, pounds

    - -
    - -

    Details

    - - - -

    Experiments were conducted at Ames, Iowa. The response variable is - forage yield in pounds of green weight.

    -

    Optimum plot size was estimated to be about 3.5 x 7.5 feet.

    -

    Wassom and Kalton used two different methods to estimate optimum plot - size. - 1. Relative efficiency of different plot sizes. - 2. Regression of the log variance of yield vs log plot size.

    -

    There are three Experiments:

    -

    Experiment E1 was broadcast seeded, harvested in 1950.

    -

    Experiment E2 was row planted, harvested in 1950.

    -

    Experiment E3 was broadcast seeded, harvested in 1951. This field contained - a mixture of alfalfa and brome in about equal proportions.

    -

    Each plot was 3.5 ft x 4 ft, but the orientation of the plot is not - clear.

    -

    Field width: 36 plots

    -

    Field length: 36 plots

    -

    Source

    - -

    Wassom and R.R. Kalton. (1953). - Estimations of Optimum Plot Size Using Data from Bromegrass Uniformity - Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396. - https://lib.dr.iastate.edu/ag_researchbulletins/32/

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-  
-  data(wassom.brome.uniformity)
-  dat <- wassom.brome.uniformity
-
-  libs(desplot)
-  desplot(dat, yield~col*row|expt,
-          flip=TRUE, aspect=1, # approximate aspect
-          main="wassom.brome.uniformity")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/waynick.soil-1.png b/docs/reference/waynick.soil-1.png deleted file mode 100644 index 0cd3c4cb..00000000 Binary files a/docs/reference/waynick.soil-1.png and /dev/null differ diff --git a/docs/reference/waynick.soil-2.png b/docs/reference/waynick.soil-2.png deleted file mode 100644 index 450451c4..00000000 Binary files a/docs/reference/waynick.soil-2.png and /dev/null differ diff --git a/docs/reference/waynick.soil-3.png b/docs/reference/waynick.soil-3.png deleted file mode 100644 index 31c1165f..00000000 Binary files a/docs/reference/waynick.soil-3.png and /dev/null differ diff --git a/docs/reference/waynick.soil.html b/docs/reference/waynick.soil.html deleted file mode 100644 index 4c078545..00000000 --- a/docs/reference/waynick.soil.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - - - -Soil nitrogen and carbon in two fields — waynick.soil • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Soil nitrogen and carbon in two fields

    - - -
    - -
    -

    Soil nitrogen and carbon in two fields

    -
    - - - -

    Format

    - -

    A data frame with 200 observations on the following 6 variables.

    -
    field

    field name, 2 levels

    -
    sample

    sample number

    -
    x

    x ordinate

    -
    y

    y ordinate

    -
    nitro

    nitrogen content, percent

    -
    carbon

    carbon content, percent

    - -
    - -

    Details

    - -

    Two fields were studied, one at University Farm in Davis, the other - near Oakley. The Davis field is silty clay loam, the Oakley field is - blow sand.

    -

    Source

    - -

    Waynick, Dean, and Sharp, Leslie. (1918). - Variability in soils and its significance to past and future soil investigations, I-II. - University of California press. - https://archive.org/details/variabilityinsoi45wayn

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(waynick.soil)
-dat <- waynick.soil
-
-# Strong relationship between N,C
-libs(lattice)
-xyplot(nitro~carbon|field, data=dat, main="waynick.soil")
-
    
-# Spatial plot
-libs(sp, gstat)
-d1 <- subset(dat, field=="Davis")
-d2 <- subset(dat, field=="Oakley")
-coordinates(d1) <- data.frame(x=d1$x, y=d1$y)
-coordinates(d2) <- data.frame(x=d2$x, y=d2$y)
-spplot(d1, zcol = "nitro", cuts=8, cex = 1.6,
-       main = "waynick.soil - Davis field - nitrogen",
-       col.regions =  bpy.colors(8), key.space = "right")
-
    
-# Variogram
-v1 <- gstat::variogram(nitro~1, data=d1)
-plot(v1, main="waynick.soil - Davis field - nitrogen") # Maybe hasn't reached sill
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wedderburn.barley-1.png b/docs/reference/wedderburn.barley-1.png deleted file mode 100644 index 2fb6ed75..00000000 Binary files a/docs/reference/wedderburn.barley-1.png and /dev/null differ diff --git a/docs/reference/wedderburn.barley-2.png b/docs/reference/wedderburn.barley-2.png deleted file mode 100644 index 4e2b2ed1..00000000 Binary files a/docs/reference/wedderburn.barley-2.png and /dev/null differ diff --git a/docs/reference/wedderburn.barley-3.png b/docs/reference/wedderburn.barley-3.png deleted file mode 100644 index dc8d03ba..00000000 Binary files a/docs/reference/wedderburn.barley-3.png and /dev/null differ diff --git a/docs/reference/wedderburn.barley-4.png b/docs/reference/wedderburn.barley-4.png deleted file mode 100644 index 208b60a7..00000000 Binary files a/docs/reference/wedderburn.barley-4.png and /dev/null differ diff --git a/docs/reference/wedderburn.barley-5.png b/docs/reference/wedderburn.barley-5.png deleted file mode 100644 index e823bc2a..00000000 Binary files a/docs/reference/wedderburn.barley-5.png and /dev/null differ diff --git a/docs/reference/wedderburn.barley.html b/docs/reference/wedderburn.barley.html deleted file mode 100644 index 48d275fd..00000000 --- a/docs/reference/wedderburn.barley.html +++ /dev/null @@ -1,336 +0,0 @@ - - - - - - - - -Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    Multi-environment trial of barley, percent of leaves affected by leaf blotch

    - - -
    - -
    -

    Percent of leaf area affected by leaf blotch on 10 varieties of barley - at 9 sites.

    -
    - - - -

    Format

    - -

    A data frame with 90 observations on the following 3 variables.

    -
    y

    Percent of leaf area affected, 0-100.

    -
    site

    Site factor, 9 levels

    -
    gen

    Variety factor, 10 levels

    - -
    - -

    Details

    - -

    Incidence of Rhynchosporium secalis (leaf blotch) on the leaves of - 10 varieties of barley grown at 9 sites in 1965.

    -

    Source

    - -

    Wedderburn, R W M (1974). - Quasilikelihood functions, generalized linear models and the - Gauss-Newton method. - Biometrika, 61, 439--47. - https://doi.org/10.2307/2334725

    -

    Wedderburn credits the original data to an unpublished thesis by - J. F. Jenkyn.

    -

    References

    - -

    McCullagh, P and Nelder, J A (1989). - Generalized Linear Models (2nd ed).

    -

    R. B. Millar. - Maximum Likelihood Estimation and Inference: With Examples in R, - SAS and ADMB. Chapter 8.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(wedderburn.barley)
-dat <- wedderburn.barley
-dat$y <- dat$y/100
-
-libs(lattice)
-dotplot(gen~y|site, dat, main="wedderburn.barley")
-
    
-# Use the variance function mu(1-mu).  McCullagh page 330
-# Note, 'binomial' gives same results as 'quasibinomial', but also a warning
-m1 <- glm(y ~ gen + site, data=dat, family="quasibinomial")
-summary(m1)
-
    #> 
-#> Call:
-#> glm(formula = y ~ gen + site, family = "quasibinomial", data = dat)
-#> 
-#> Deviance Residuals: 
-#>      Min        1Q    Median        3Q       Max  
-#> -0.64435  -0.13537  -0.02028   0.09500   0.81003  
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept)  -8.0546     1.4219  -5.665 2.84e-07 ***
-#> genG02        0.1501     0.7237   0.207 0.836289    
-#> genG03        0.6895     0.6724   1.025 0.308587    
-#> genG04        1.0482     0.6494   1.614 0.110910    
-#> genG05        1.6147     0.6257   2.581 0.011895 *  
-#> genG06        2.3712     0.6090   3.893 0.000219 ***
-#> genG07        2.5705     0.6065   4.238 6.58e-05 ***
-#> genG08        3.3420     0.6015   5.556 4.39e-07 ***
-#> genG09        3.5000     0.6013   5.820 1.51e-07 ***
-#> genG10        4.2530     0.6042   7.039 9.38e-10 ***
-#> siteS2        1.6391     1.4433   1.136 0.259870    
-#> siteS3        3.3265     1.3492   2.466 0.016066 *  
-#> siteS4        3.5822     1.3444   2.664 0.009510 ** 
-#> siteS5        3.5831     1.3444   2.665 0.009493 ** 
-#> siteS6        3.8933     1.3402   2.905 0.004875 ** 
-#> siteS7        4.7300     1.3348   3.544 0.000697 ***
-#> siteS8        5.5227     1.3346   4.138 9.38e-05 ***
-#> siteS9        6.7946     1.3407   5.068 3.00e-06 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for quasibinomial family taken to be 0.08877777)
-#> 
-#>     Null deviance: 40.803  on 89  degrees of freedom
-#> Residual deviance:  6.126  on 72  degrees of freedom
-#> AIC: NA
-#> 
-#> Number of Fisher Scoring iterations: 8
-#> 
    
-# Same shape (different scale) as McCullagh fig 9.1a
-plot(m1, which=1, main="wedderburn.barley")
-
    
-# Compare data and model
-dat$pbin <- predict(m1, type="response")
-dotplot(gen~pbin+y|site, dat, main="wedderburn.barley: observed/predicted")
-
    
-
-# Wedderburn suggested variance function: mu^2 * (1-mu)^2
-# Millar shows how to do this explicitly.
-wedder <- list(varfun=function(mu) (mu*(1-mu))^2,
-             validmu=function(mu) all(mu>0) && all(mu<1),
-             dev.resids=function(y,mu,wt) wt * ((y-mu)^2)/(mu*(1-mu))^2,
-             initialize=expression({
-               n <- rep.int(1, nobs)
-               mustart <- pmax(0.001, pmin(0.99,y)) }),
-             name="(mu(1-mu))^2")
-m2 <- glm(y ~ gen + site, data=dat, family=quasi(link="logit", variance=wedder))
-#plot(m2)
-
-# \dontrun{
-# Alternatively, the 'gnm' package has the 'wedderburn' family.
-libs(gnm)
-m3 <- glm(y ~ gen + site, data=dat, family="wedderburn")
-summary(m3)
-
    #> 
-#> Call:
-#> glm(formula = y ~ gen + site, family = "wedderburn", data = dat)
-#> 
-#> Deviance Residuals: 
-#>     Min       1Q   Median       3Q      Max  
-#> -3.1805  -0.6293   0.0000   0.3888   1.9710  
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) -7.92236    0.44464 -17.817  < 2e-16 ***
-#> genG02      -0.46730    0.46869  -0.997  0.32209    
-#> genG03       0.07886    0.46869   0.168  0.86686    
-#> genG04       0.95409    0.46869   2.036  0.04547 *  
-#> genG05       1.35262    0.46869   2.886  0.00515 ** 
-#> genG06       1.32863    0.46869   2.835  0.00595 ** 
-#> genG07       2.34006    0.46869   4.993 4.01e-06 ***
-#> genG08       3.26257    0.46869   6.961 1.30e-09 ***
-#> genG09       3.13550    0.46869   6.690 4.10e-09 ***
-#> genG10       3.88728    0.46869   8.294 4.33e-12 ***
-#> siteS2       1.38311    0.44464   3.111  0.00268 ** 
-#> siteS3       3.86002    0.44464   8.681 8.20e-13 ***
-#> siteS4       3.55700    0.44464   8.000 1.54e-11 ***
-#> siteS5       4.10781    0.44464   9.238 7.53e-14 ***
-#> siteS6       4.30529    0.44464   9.683 1.13e-14 ***
-#> siteS7       4.91808    0.44464  11.061  < 2e-16 ***
-#> siteS8       5.69483    0.44464  12.808  < 2e-16 ***
-#> siteS9       7.06760    0.44464  15.895  < 2e-16 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for wedderburn family taken to be 0.9885279)
-#> 
-#>     Null deviance: 370.526  on 89  degrees of freedom
-#> Residual deviance:  66.269  on 72  degrees of freedom
-#> AIC: NA
-#> 
-#> Number of Fisher Scoring iterations: 13
-#> 
    # Similar to McCullagh fig 9.2
-plot(m3, which=1)
-
    
-# Compare data and model
-dat$pwed <- predict(m3, type="response")
-dotplot(gen~pwed+y|site, dat)
-
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/weiss.incblock-1.png b/docs/reference/weiss.incblock-1.png deleted file mode 100644 index 724d19c7..00000000 Binary files a/docs/reference/weiss.incblock-1.png and /dev/null differ diff --git a/docs/reference/weiss.incblock.html b/docs/reference/weiss.incblock.html deleted file mode 100644 index 58281552..00000000 --- a/docs/reference/weiss.incblock.html +++ /dev/null @@ -1,289 +0,0 @@ - - - - - - - - -Soybean balanced incomplete block experiment — weiss.incblock • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Soybean balanced incomplete block experiment

    - - -
    - -
    -

    Soybean balanced incomplete block experiment

    -
    - - 
    data("weiss.incblock")
    - - -

    Format

    - -

    A data frame with 186 observations on the following 5 variables.

    -
    block

    block factor

    -
    gen

    genotype (variety) factor

    -
    yield

    yield (bu/ac)

    -
    row

    row

    -
    col

    column

    - -
    - -

    Details

    - -

    Grown at Ames, Iowa in 1937. Each plot was 6 feet by 16 feet (2 rows, - 3 feet apart). Including space between plots, the entire experiment - was 252 ft x 96 feet (7 block * 6 plots * 6 feet = 252, 16*5 plots - plus 4 gaps of 4 feet). Weiss shows a figure of the field (that was - later doubled in dize via using two rows per plot).

    -

    Note that only 30 varieties were tested. Varieties 7 and 14 are the - same variety (Mukden). Although total yields of these varieties were - not equal, the correction for blocks adjusted their means to identical - values. Such accuracy is not, however, claimed to be a constant - characteristic of the design.

    -

    Field width: 96 feet

    -

    Field length: 252 feet

    -

    Source

    - -

    Weiss, Martin G. and Cox, Gertrude M. (1939). - Balanced Incomplete Block and Lattice Square Designs for Testing Yield - Differences Among Large Numbers of Soybean Varieties. - Agricultural Research Bulletins, Nos. 251-259. - https://lib.dr.iastate.edu/ag_researchbulletins/24/

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(weiss.incblock)
-  dat <- weiss.incblock
-
-  # True aspect as shown in Weiss and Cox
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          text=gen, shorten='none', cex=.6, out1=block,
-          aspect=252/96, # true aspect
-          main="weiss.incblock")
-
      
-
-  # asreml
-  # Standard inc block analysis used by Weiss and Cox
-  libs(asreml) # asreml 4
-  m1 <- asreml(yield ~ gen + block , data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:51 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -192.991       3.58529    125 17:08:51    0.0
      predict(m1, data=dat, classify="gen")$pvals
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:51 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -192.991       3.58529    125 17:08:52    0.0
-#>  2      -192.991       3.58529    125 17:08:52    0.0
    #> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: block
-#> 
-#> 
-#>    gen predicted.value std.error    status
-#> 1  G01        24.58925 0.8311545 Estimable
-#> 2  G02        26.92473 0.8311545 Estimable
-#> 3  G03        32.61828 0.8311545 Estimable
-#> 4  G04        26.96667 0.8311545 Estimable
-#> 5  G05        26.01828 0.8311545 Estimable
-#> 6  G06        31.98925 0.8311545 Estimable
-#> 7  G07        24.18925 0.8311545 Estimable
-#> 8  G08        27.60860 0.8311545 Estimable
-#> 9  G09        29.27634 0.8311545 Estimable
-#> 10 G10        24.44409 0.8311545 Estimable
-#> 11 G11        27.27957 0.8311545 Estimable
-#> 12 G12        29.24731 0.8311545 Estimable
-#> 13 G13        29.74086 0.8311545 Estimable
-#> 14 G14        24.17957 0.8311545 Estimable
-#> 15 G15        26.29570 0.8311545 Estimable
-#> 16 G16        25.77312 0.8311545 Estimable
-#> 17 G17        19.88280 0.8311545 Estimable
-#> 18 G18        25.80215 0.8311545 Estimable
-#> 19 G19        29.00215 0.8311545 Estimable
-#> 20 G20        33.17312 0.8311545 Estimable
-#> 21 G21        31.14086 0.8311545 Estimable
-#> 22 G22        25.26667 0.8311545 Estimable
-#> 23 G23        29.81505 0.8311545 Estimable
-#> 24 G24        33.70538 0.8311545 Estimable
-#> 25 G25        26.99247 0.8311545 Estimable
-#> 26 G26        27.25699 0.8311545 Estimable
-#> 27 G27        23.78925 0.8311545 Estimable
-#> 28 G28        26.53441 0.8311545 Estimable
-#> 29 G29        24.76667 0.8311545 Estimable
-#> 30 G30        35.99892 0.8311545 Estimable
-#> 31 G31        26.99892 0.8311545 Estimable
      
-  ## gen pred.value std.error est.stat
-  ## G01     24.59    0.8312  Estimable
-  ## G02     26.92    0.8312  Estimable
-  ## G03     32.62    0.8312  Estimable
-  ## G04     26.97    0.8312  Estimable
-  ## G05     26.02    0.8312  Estimable
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/weiss.lattice-1.png b/docs/reference/weiss.lattice-1.png deleted file mode 100644 index 1bc9fa41..00000000 Binary files a/docs/reference/weiss.lattice-1.png and /dev/null differ diff --git a/docs/reference/weiss.lattice.html b/docs/reference/weiss.lattice.html deleted file mode 100644 index 0b2d2581..00000000 --- a/docs/reference/weiss.lattice.html +++ /dev/null @@ -1,338 +0,0 @@ - - - - - - - - -Lattice experiment in soybeans. — weiss.lattice • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Lattice experiment in soybeans.

    - - -
    - -
    -

    Lattice experiment in soybeans.

    -
    - - 
    data("weiss.lattice")
    - - -

    Format

    - -

    A data frame with 196 observations on the following 5 variables.

    -
    yield

    yield (bu/ac)

    -
    gen

    genotype factor, 49 levels

    -
    rep

    rep factor, 4 levels

    -
    col

    column

    -
    row

    row

    - -
    - -

    Details

    - -

    Yield test of 49 soybean varieties, grown at Ames, IA, in 1938. -Plot dimensions were 3x16 feeet. -The varieties are compared to variety 26 (Mukden).

    -

    It is not clear how the reps were positioned in the field. On the one -hand, the middle three columns of each rep/square are higher yielding, -giving the appearance of the reps being stacked on top of each other. -On the other hand, the analysis by Weiss uses 24 degrees of freedom -4*(7-1) to fit a separate effect for each column in each -rep (instead of across reps).

    -

    Source

    - -

    Weiss, Martin G. and Cox, Gertrude M. (1939). - Balanced Incomplete Block and Lattice Square Designs for Testing Yield - Differences Among Large Numbers of Soybean Varieties. Table 5. - Agricultural Research Bulletins, Nos. 251-259. - https://lib.dr.iastate.edu/ag_researchbulletins/24/

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(weiss.lattice)
-  dat <- weiss.lattice
-
-  libs(desplot)
-  desplot(dat, yield~col*row|rep,
-          text=gen, shorten="none", cex=.8, aspect=3/16, # true aspect
-          main="weiss.lattice (layout uncertain)", xlab="Soybean yields")
-
    
-
-  dat <- transform(dat, xf=factor(col), yf=factor(row))
-  m1 <- lm(terms(yield ~ rep + rep:xf + rep:yf + gen, keep.order=TRUE), data=dat)
-  anova(m1) # Matches Weiss table 7
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
-#> rep        3   91.57  30.525  4.7414 0.0039709 ** 
-#> rep:xf    24 2913.43 121.393 18.8557 < 2.2e-16 ***
-#> rep:yf    24  390.21  16.259  2.5254 0.0007734 ***
-#> gen       48 1029.87  21.456  3.3327 2.652e-07 ***
-#> Residuals 96  618.05   6.438                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      ## Response: yield
-  ##           Df  Sum Sq Mean Sq F value    Pr(>F)
-  ## rep        3   91.57  30.525  4.7414 0.0039709 **
-  ## rep:xf    24 2913.43 121.393 18.8557 < 2.2e-16 ***
-  ## rep:yf    24  390.21  16.259  2.5254 0.0007734 ***
-  ## gen       48 1029.87  21.456  3.3327 2.652e-07 ***
-  ## Residuals 96  618.05   6.438
-  
-  # ----------
-
-  libs(asreml) # asreml4
-  m2 <- asreml(yield ~ rep + rep:xf + rep:yf + gen, data=dat)
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:52 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -208.509       6.43798     96 17:08:52    0.0
      # Weiss table 6 means
-  wald(m2)
-
    #> Wald tests for fixed effects.
-#> Response: yield
-#> Terms added sequentially; adjusted for those above.
-#> 
-#>               Df Sum of Sq Wald statistic Pr(Chisq)    
-#> (Intercept)    1    127424        19792.5    <2e-16 ***
-#> rep            3        92            4.7    0.1917    
-#> gen           48      1863            6.0    1.0000    
-#> rep:xf        24      2200           14.2    0.9410    
-#> rep:yf        24       270            1.7    1.0000    
-#> residual (MS)            6                             
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
      predict(m2, data=dat, classify="gen")$pvals
-
    #> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Mon Jan 11 17:08:53 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -208.509       6.43798     96 17:08:53    0.0
-#>  2      -208.509       6.43798     96 17:08:53    0.0
    #> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: rep,xf,yf
-#> 
-#> 
-#>    gen predicted.value std.error    status
-#> 1  G01        27.74167  1.461179 Estimable
-#> 2  G02        24.94643  1.461179 Estimable
-#> 3  G03        24.38452  1.461179 Estimable
-#> 4  G04        28.04643  1.461179 Estimable
-#> 5  G05        19.60357  1.461179 Estimable
-#> 6  G06        23.79405  1.461179 Estimable
-#> 7  G07        19.61310  1.461179 Estimable
-#> 8  G08        29.41786  1.461179 Estimable
-#> 9  G09        20.36548  1.461179 Estimable
-#> 10 G10        24.86071  1.461179 Estimable
-#> 11 G11        24.52738  1.461179 Estimable
-#> 12 G12        26.74167  1.461179 Estimable
-#> 13 G13        21.78929  1.461179 Estimable
-#> 14 G14        27.52738  1.461179 Estimable
-#> 15 G15        23.33690  1.461179 Estimable
-#> 16 G16        27.65595  1.461179 Estimable
-#> 17 G17        26.48929  1.461179 Estimable
-#> 18 G18        26.36548  1.461179 Estimable
-#> 19 G19        24.28452  1.461179 Estimable
-#> 20 G20        32.14643  1.461179 Estimable
-#> 21 G21        25.98452  1.461179 Estimable
-#> 22 G22        23.79881  1.461179 Estimable
-#> 23 G23        22.50357  1.461179 Estimable
-#> 24 G24        27.06548  1.461179 Estimable
-#> 25 G25        26.88452  1.461179 Estimable
-#> 26 G26        25.83214  1.461179 Estimable
-#> 27 G27        23.75119  1.461179 Estimable
-#> 28 G28        24.66071  1.461179 Estimable
-#> 29 G29        23.98452  1.461179 Estimable
-#> 30 G30        23.57976  1.461179 Estimable
-#> 31 G31        26.83214  1.461179 Estimable
-#> 32 G32        20.94167  1.461179 Estimable
-#> 33 G33        27.12738  1.461179 Estimable
-#> 34 G34        26.63214  1.461179 Estimable
-#> 35 G35        28.13214  1.461179 Estimable
-#> 36 G36        30.24167  1.461179 Estimable
-#> 37 G37        24.03690  1.461179 Estimable
-#> 38 G38        25.68452  1.461179 Estimable
-#> 39 G39        24.04167  1.461179 Estimable
-#> 40 G40        25.61310  1.461179 Estimable
-#> 41 G41        25.92738  1.461179 Estimable
-#> 42 G42        25.75119  1.461179 Estimable
-#> 43 G43        24.44167  1.461179 Estimable
-#> 44 G44        28.20833  1.461179 Estimable
-#> 45 G45        26.31310  1.461179 Estimable
-#> 46 G46        31.75595  1.461179 Estimable
-#> 47 G47        25.82738  1.461179 Estimable
-#> 48 G48        23.82262  1.461179 Estimable
-#> 49 G49        26.36071  1.461179 Estimable
      
-  ##  gen pred.value std.error  est.stat
-  ##  G01      27.74     1.461 Estimable
-  ##  G02      24.95     1.461 Estimable
-  ##  G03      24.38     1.461 Estimable
-  ##  G04      28.05     1.461 Estimable
-  ##  G05      19.6      1.461 Estimable
-  ##  G06      23.79     1.461 Estimable
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/welch.bermudagrass-1.png b/docs/reference/welch.bermudagrass-1.png deleted file mode 100644 index bd29b3f7..00000000 Binary files a/docs/reference/welch.bermudagrass-1.png and /dev/null differ diff --git a/docs/reference/welch.bermudagrass-2.png b/docs/reference/welch.bermudagrass-2.png deleted file mode 100644 index bc2f0309..00000000 Binary files a/docs/reference/welch.bermudagrass-2.png and /dev/null differ diff --git a/docs/reference/welch.bermudagrass.html b/docs/reference/welch.bermudagrass.html deleted file mode 100644 index e981cc37..00000000 --- a/docs/reference/welch.bermudagrass.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - - -Factorial experiment of bermuda grass, N, P, K fertilizers — welch.bermudagrass • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Factorial experiment of bermuda grass, N, P, K fertilizers

    - - -
    - -
    -

    Factorial experiment of bermuda grass, N, P, K fertilizers

    -
    - - - -

    Format

    - -

    A data frame with 64 observations on the following 4 variables.

    -
    n

    nitrogen fertilizer, pounds/acre

    -
    p

    phosphorus, lb/ac

    -
    k

    potassium, lb/ac

    -
    yield

    yield of grass, tons/ac

    - -
    - -

    Details

    - -

    The experiment was conducted 1955, 1956, and 1957. There were 3 - blocks. The harvests were oven-dried. Each value is the mean for 3 - years and 3 replications. In most cases, the yield increased with - additions of the fertilizer nutrients.

    -

    Source

    - -

    Welch, Louis Frederick and Adams, William Eugenius and Carmon, - JL. 1963. Yield response surfaces, isoquants, and economic fertilizer - optima for Coastal Bermudagrass. Agronomy Journal, 55, - 63-67. Table 1. - https://doi.org/10.2134/agronj1963.00021962005500010023x

    -

    References

    - -

    Jim Albert. Bayesian Computation with R. Page 256.

    -

    Peter Congdon. Bayesian Statistical Modeling. Page 124-125.

    -

    P. McCullagh, John A. Nelder. Generalized Linear Models, Second - Edition. Page 382.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(welch.bermudagrass)
-dat <- welch.bermudagrass
-# Welch uses 100-pound units of n,p,k.
-dat <- transform(dat, n=n/100, p=p/100, k=k/100)
-
-libs(latticeExtra)
-useOuterStrips(xyplot(yield~n|factor(p)*factor(k), data=dat, type='b',
-                      main="welch.bermudagrass: yield for each P*K",
-                      xlab="Nitro for each Phosphorous level",
-                      ylab="Yield for each Potassim level"))
-
    
-
-# Fit a quadratic model
-m1 <- lm(yield ~ n + p + k + I(n^2) + I(p^2) + I(k^2) + n:p + n:k + p:k + n:p:k, data=dat)
-signif(coef(m1),4) # These match the 3-yr coefficients of Welch, Table 2
-
    #> (Intercept)           n           p           k      I(n^2)      I(p^2) 
-#>     1.94300     2.00700     1.47100     0.61880    -0.33150    -1.29500 
-#>      I(k^2)         n:p         n:k         p:k       n:p:k 
-#>    -0.37430     0.20780     0.18740     0.23480     0.02789 
    ## (Intercept)           n           p           k      I(n^2)      I(p^2)
-##     1.94300     2.00700     1.47100     0.61880    -0.33150    -1.29500
-##      I(k^2)         n:p         n:k         p:k       n:p:k
-##    -0.37430     0.20780     0.18740     0.23480     0.02789
-
-# Welch Fig 4.  Modeled response curves
-d1 <- expand.grid(n=seq(0, 4, length=50), p=0, k=0)
-d1$pred <- predict(m1, d1)
-d2 <- expand.grid(n=0, p=0, k=seq(0, 1.68, length=50))
-d2$pred <- predict(m1, d2)
-d3 <- expand.grid(n=0, p=seq(0, .88, length=50), k=0)
-d3$pred <- predict(m1, d3)
-
-op <- par(mfrow=c(1,3), mar=c(5,3,4,1))
-plot(pred~n, data=d1, type='l', ylim=c(0,6), xlab="N 100 lb/ac", ylab="")
-plot(pred~k, data=d2, type='l', ylim=c(0,6), xlab="K 100 lb/ac", ylab="")
-title("welch.bermudagrass - Predicted yield vs fertilizer", outer=TRUE, line= -3)
-plot(pred~p, data=d3, type='l', ylim=c(0,6), xlab="P 100 lb/ac",
-ylab="")
-
    par(op)
-
-# Brute-force grid-search optimization of fertilizer quantities, using
-# $25/ton for grass, $.12/lb for N, $.18/lb for P, $.07/lb for K
-# Similar to Example 5 in Table 4 of Welch
-d4 <- expand.grid(n=seq(3,4,length=20), p=seq(.5, 1.5, length=20),
-                  k=seq(.8, 1.8, length=20))
-d4$pred <- predict(m1, newdata=d4)
-d4 <- transform(d4, income = 25*pred - .12*n*100 + -.18*p*100 -.07*k*100)
-d4[which.max(d4$income),] # Optimum at 300 lb N, 71 lb P, 148 lb K
-
    #>      n         p        k     pred   income
-#> 5281 3 0.7105263 1.484211 7.079801 117.8161
    
-
-# ----- JAGS -----
-if(0){
-  # Congdon (2007) p. 124, provides a Bayesian model based on a GLM
-  # by McCullagh & Nelder.  We use JAGS and simplify the code.
-  # y ~ gamma with shape = nu, scale = nu * eps_i
-  # 1/eps = b0 + b1/(N+a1) + b2/(P+a2) + b3/(K+a3)
-  # N,P,K are added fertilizer amounts, a1,a2,a3 are background
-  # nutrient levels and b1,b2,b3 are growth parameters.
-
-  libs(rjags)
-
-  mod.bug =
-  "model {
-  for(i in 1:nobs) {
-    yield[i] ~ dgamma(nu, mu[i])
-    mu[i] <- nu * eta[i]
-    eta[i] <- b0 + b1 / (N[i]+a1) + b2 / (P[i]+a2) + b3 / (K[i]+a3)
-    yhat[i] <- 1 / eta[i]
-  }
-
-  # Hyperparameters
-  nu ~ dgamma(0.01, 0.01)
-  a1 ~ dnorm(40, 0.01) # Informative priors
-  a2 ~ dnorm(22, 0.01)
-  a3 ~ dnorm(32, 0.01)
-  b0 ~ dnorm(0, 0.0001)
-  b1 ~ dnorm(0, 0.0001) I(0,) # Keep b1 non-negative
-  b2 ~ dnorm(0, 0.0001) I(0,)
-  b3 ~ dnorm(0, 0.0001) I(0,)
-}"
-
-  jdat <- with(welch.bermudagrass,
-               list(yield=yield, N=n, P=p, K=k, nobs=64))
-  jinit = list(a1=40, a2=22, a3=32, b0=.1, b1=10, b2=1, b3=1)
-
-  oo <- textConnection(mod.bug)
-  j1 <- jags.model(oo, data=jdat, inits=jinit, n.chains=3)
-  close(oo)
-  
-  c1 <- coda.samples(j1, c("b0","b1","b2","b3", "a1","a2","a3"),
-                     n.iter=10000)
-
-  # Results nearly identical go Congdon
-  print(summary(c1)$statistics[,1:2],dig=1)
-  # libs(lucid)
-  # print(vc(c1),3)
-  ##       Mean     SD
-  ## a1  44.85  4.123
-  ## a2  23.63  7.37
-  ## a3  35.42  8.57
-  ## b0   0.092 0.0076
-  ## b1  13.23  1.34
-  ## b2   1.186 0.47
-  ## b3   1.50  0.48
-
-  d2 <- coda.samples(j1, "yhat", n.iter=10000)
-  dat$yhat <- summary(d2)$statistics[,1]
-  with(dat, plot(yield, yield-yhat))
-}
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wheatley.carrot-1.png b/docs/reference/wheatley.carrot-1.png deleted file mode 100644 index 03421fc9..00000000 Binary files a/docs/reference/wheatley.carrot-1.png and /dev/null differ diff --git a/docs/reference/wheatley.carrot.html b/docs/reference/wheatley.carrot.html deleted file mode 100644 index c2552a3f..00000000 --- a/docs/reference/wheatley.carrot.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Insecticide treatments for carrot fly larvae — wheatley.carrot • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Insecticide treatments for carrot fly larvae

    - - -
    - -
    -

    Insecticide treatments for carrot fly larvae. - Two insecticides with five depths.

    -
    - - 
    data("wheatley.carrot")
    - - -

    Format

    - -

    A data frame with 36 observations on the following 6 variables.

    -
    treatment

    treatment factor, 11 levels

    -
    insecticide

    insecticide factor

    -
    depth

    depth

    -
    rep

    block

    -
    damaged

    number of damaged plants

    -
    total

    total number of plants

    - -
    - -

    Details

    - - - -

    In 1964 an experiment was conducted with microplots to evaluate the - effectiveness of treatments against carrot fly larvae. - The treatment factor is a combination of insecticide and depth.

    -

    Hardin & Hilbe used this data to fit a generalized binomial model.

    -

    Famoye (1995) used the same data to fit a generalized binomial regression model. - Results for Famoye are not shown.

    -

    Source

    - -

    G A Wheatley & H Freeman. (1982). - A method of using the proportions of undamaged carrots or parsnips to - estimate the relative population densities of carrot fly (Psila rosae) - larvae, and its practical applications. - Annals of Applied Biology, 100, 229-244. Table 2.

    -

    https://doi.org/10.1111/j.1744-7348.1982.tb01935.x

    -

    References

    - - - -

    James William Hardin, Joseph M. Hilbe. - Generalized Linear Models and Extensions, 2nd ed.

    -

    F Famoye (1995). Generalized Binomial Regression. - Biom J, 37, 581-594.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(wheatley.carrot)
-dat <- wheatley.carrot
-
-# Observed proportions of damage
-dat <- transform(dat, prop=damaged/total)
-libs(lattice)
-xyplot(prop~depth|insecticide, data=dat, subset=treatment!="T11",
-       cex=1.5, main="wheatley.carrot", ylab="proportion damaged")
-
    
-# Model for Wheatley. Deviance for treatment matches Wheatley, but other
-# deviances do not.  Why?
-# treatment:rep is the residual
-m1 <- glm(cbind(damaged,total-damaged) ~ rep + treatment + treatment:rep,
-          data=dat, family=binomial("cloglog"))
-anova(m1)
-
    #> Analysis of Deviance Table
-#> 
-#> Model: binomial, link: cloglog
-#> 
-#> Response: cbind(damaged, total - damaged)
-#> 
-#> Terms added sequentially (first to last)
-#> 
-#> 
-#>               Df Deviance Resid. Df Resid. Dev
-#> NULL                             35    1824.41
-#> rep            2    64.69        33    1759.73
-#> treatment     10  1636.16        23     123.56
-#> rep:treatment 20   108.69         3      14.88
    
-# GLM of Hardin & Hilbe p. 161. By default, R uses T01 as the base,
-# but Hardin uses T11. Results match.
-m2 <- glm(cbind(damaged,total-damaged) ~ rep + C(treatment, base=11),
-          data=dat, family=binomial("cloglog"))
-summary(m2)
-
    #> 
-#> Call:
-#> glm(formula = cbind(damaged, total - damaged) ~ rep + C(treatment, 
-#>     base = 11), family = binomial("cloglog"), data = dat)
-#> 
-#> Deviance Residuals: 
-#>     Min       1Q   Median       3Q      Max  
-#> -3.4888  -1.4974  -0.2190   0.8434   3.8726  
-#> 
-#> Coefficients:
-#>                           Estimate Std. Error z value Pr(>|z|)    
-#> (Intercept)                0.52335    0.04668  11.212  < 2e-16 ***
-#> repR2                      0.33307    0.04528   7.356  1.9e-13 ***
-#> repR3                      0.37659    0.04527   8.319  < 2e-16 ***
-#> C(treatment, base = 11)1  -0.56727    0.06696  -8.471  < 2e-16 ***
-#> C(treatment, base = 11)2  -1.20193    0.07448 -16.138  < 2e-16 ***
-#> C(treatment, base = 11)3  -2.46298    0.11231 -21.931  < 2e-16 ***
-#> C(treatment, base = 11)4  -2.84076    0.13190 -21.538  < 2e-16 ***
-#> C(treatment, base = 11)5  -1.11758    0.07293 -15.323  < 2e-16 ***
-#> C(treatment, base = 11)6  -0.77311    0.06910 -11.188  < 2e-16 ***
-#> C(treatment, base = 11)7  -0.89890    0.07009 -12.825  < 2e-16 ***
-#> C(treatment, base = 11)8  -1.28975    0.07503 -17.189  < 2e-16 ***
-#> C(treatment, base = 11)9  -1.83174    0.08787 -20.846  < 2e-16 ***
-#> C(treatment, base = 11)10 -0.61234    0.06772  -9.043  < 2e-16 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for binomial family taken to be 1)
-#> 
-#>     Null deviance: 1824.41  on 35  degrees of freedom
-#> Residual deviance:  123.56  on 23  degrees of freedom
-#> AIC: 339.51
-#> 
-#> Number of Fisher Scoring iterations: 5
-#> 
    
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wiebe.wheat.uniformity-1.png b/docs/reference/wiebe.wheat.uniformity-1.png deleted file mode 100644 index 8b191b91..00000000 Binary files a/docs/reference/wiebe.wheat.uniformity-1.png and /dev/null differ diff --git a/docs/reference/wiebe.wheat.uniformity-2.png b/docs/reference/wiebe.wheat.uniformity-2.png deleted file mode 100644 index 2a977ddd..00000000 Binary files a/docs/reference/wiebe.wheat.uniformity-2.png and /dev/null differ diff --git a/docs/reference/wiebe.wheat.uniformity-3.png b/docs/reference/wiebe.wheat.uniformity-3.png deleted file mode 100644 index 8d385938..00000000 Binary files a/docs/reference/wiebe.wheat.uniformity-3.png and /dev/null differ diff --git a/docs/reference/wiebe.wheat.uniformity-4.png b/docs/reference/wiebe.wheat.uniformity-4.png deleted file mode 100644 index 26d21358..00000000 Binary files a/docs/reference/wiebe.wheat.uniformity-4.png and /dev/null differ diff --git a/docs/reference/wiebe.wheat.uniformity-5.png b/docs/reference/wiebe.wheat.uniformity-5.png deleted file mode 100644 index 8e7b837d..00000000 Binary files a/docs/reference/wiebe.wheat.uniformity-5.png and /dev/null differ diff --git a/docs/reference/wiebe.wheat.uniformity.html b/docs/reference/wiebe.wheat.uniformity.html deleted file mode 100644 index ab52290f..00000000 --- a/docs/reference/wiebe.wheat.uniformity.html +++ /dev/null @@ -1,315 +0,0 @@ - - - - - - - - -Uniformity trial of wheat — wiebe.wheat.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of wheat

    - - -
    - -
    -

    Uniformity trial of wheat at Aberdeen, Idaho, 1927.

    -
    - - - -

    Format

    - -

    A data frame with 1500 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column (series)

    -
    yield

    yield in grams per plot

    - -
    - -

    Details

    - - - -

    Yield trial conducted in 1927 near Aberdeen, Idaho. The crop was - Federation wheat (C.I. no 4734). Plots were seeded on April 18 with a - drill that sowed eight rows at a time. Individual rows were harvested - in August and threshed with a small nursery thresher. Some authors - recommend analyzing the square root of the yields.

    -

    Rows were 15 feet long, 1 foot apart.

    -

    Field width: 12 columns * 15 feet = 180 feet wide.

    -

    Field length: 125 rows * 12 in = 125 feet

    -

    Source

    - -

    Wiebe, G.A. 1935. - Variation and Correlation in Grain Yield among 1,500 Wheat - Nursery Plots. - Journal of Agricultural Research, 50, 331-357. - https://naldc.nal.usda.gov/download/IND43968632/PDF

    -

    References

    - -

    D.A. Preece, 1981, - Distributions of final digits in data, - The Statistician, 30, 31--60. - https://doi.org/10.2307/2987702

    -

    Wilkinson et al. (1983). - Nearest Neighbour (NN) Analysis of Field Experiments. - J. R. Statist. Soc. B, 45, 151-211. - https://doi.org/10.1111/j.2517-6161.1983.tb01240.x - https://www.jstor.org/stable/2345523

    -

    Wiebe, G.A. 1937. - The Error in grain yield attending misspaced wheat nursery rows and - the extent of the misspacing effect. - Journal of the American Society of Agronomy, 29, 713-716.

    -

    F. Yates (1939). - The comparative advantages of systematic and randomized arrangements - in the design of agricultural and biological experiments. - Biometrika, 30, 440-466, p. 465 - https://archive.org/details/in.ernet.dli.2015.231848/page/n473

    - -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(wiebe.wheat.uniformity)
-  dat <- wiebe.wheat.uniformity
-  
-  libs(desplot)
-  desplot(dat, yield~col+row,
-          aspect=125/180, flip=TRUE, # true aspect
-          main="wiebe.wheat.uniformity: yield") # row 1 is at south
-
      
-  
-  # Preece (1981) found the last digits have an interesting distribution
-  # with 0 and 5 much more common than other digits.
-  dig <- substring(dat$yield, nchar(dat$yield))
-  dig <- as.numeric(dig)
-  hist(dig, breaks=0:10-.5, xlab="Last digit",
-       main="wiebe.wheat.uniformity - histogram of last digit")
-
      table(dat$col, dig) # Table 3 of Preece
-
    #>     dig
-#>       0  1  2  3  4  5  6  7  8  9
-#>   1  61  0  0  0  0 64  0  0  0  0
-#>   2  67  0  0  0  0 58  0  0  0  0
-#>   3  65  0  0  0  0 60  0  0  0  0
-#>   4  66  0  0  0  0 59  0  0  0  0
-#>   5  55  0  0  0  0 70  0  0  0  0
-#>   6  54  0  0  0  0 71  0  0  0  0
-#>   7  69  0  0  0  0 56  0  0  0  0
-#>   8  65  0  0  0  0 60  0  0  0  0
-#>   9   9 10  7  9 19 19 13 16 13 10
-#>   10 13 18 12 10 11 17 14  9  8 13
-#>   11 13  9 16 13 16 18  8  6 16 10
-#>   12 17  0 11 16 12 17  1 16 20 15
      
-  # Wilkinson (1983, p. 152) noted that an 8-row planter was used which
-  # produced a recurring pattern of row effects on yield.  This can be seen
-  # in the high autocorrelations of row means at lag 8 and lag 16
-  rowm <- tapply(dat$yield, dat$row, mean)
-  acf(rowm, main="wiebe.wheat.uniformity row means")
-
      # Plot the row mean against the planter row unit 1-8
-  libs("lattice")
-  xyplot(rowm~rep(1:8, length=125),
-         main="wiebe.wheat.uniformity",
-         xlab="Planter row unit", ylab="Row mean yield")
-
    
-  # Wiebe (1937) and Yates (1939) show the effect of "guess rows"
-  # caused by the 8-row drill passing back and forth through
-  # the field.
-  # Yates gives the distance between strips (8 rows per strip) as:
-  # 10.2,12.4,11.7,13.4,10.6,14.2,11.8,13.8,12.2,13.1,11.2,14,11.3,12.9,12.4
-
-  # First give each row 12 inches of growing width between rows
-  tmp <- data.frame(row=1:125,area=12)
-  # Distance between rows 8,9 is 10.2 inches, so we give these two
-  # rows 6 inches (on the 'inside' of the strip) and 10.2/2=5.1 inches
-  # on the outside of the strip, total 11.1 inches
-  tmp$area[8:9] <- 6 + 10.2/2
-  tmp$area[16:17] <- 6 + 12.4/2
-  tmp$area[24:25] <- 6 + 11.7/2
-  tmp$area[32:33] <- 6 + 13.4/2
-  tmp$area[40:41] <- 6 + 10.6/2
-  tmp$area[48:49] <- 6 + 14.2/2
-  tmp$area[56:57] <- 6 + 11.8/2
-  tmp$area[64:65] <- 6 + 13.8/2
-  tmp$area[72:73] <- 6 + 12.2/2
-  tmp$area[80:81] <- 6 + 13.1/2
-  tmp$area[88:89] <- 6 + 11.2/2
-  tmp$area[96:97] <- 6 + 14.0/2
-  tmp$area[104:105] <- 6 + 11.3/2
-  tmp$area[112:113] <- 6 + 12.9/2
-  tmp$area[120:121] <- 6 + 12.4/2
-  dat <- merge(dat, tmp)
-  
-  # It's not clear if Wiebe used border rows...we delete them
-  dat <- subset(dat, row >  1 & row < 125)
-
-  # Wiebe (1937) calculated a moving average to adjust for fertility
-  # effects, then used only the OUTER rows of each 8-row drill strip
-  # and found 21.5 g / inch of space between rows.  We used all the
-  # data without correcting for fertility and obtained 33.1 g / inch.
-  xyplot(yield ~ area, dat, type=c('p','r'),
-         xlab="Average area per row", ylab="Yield")
-
      coef(lm(yield ~ area, dat))[2]
-
    #>    area 
-#> 33.1033 
      # 33.1
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/wiedemann.safflower.uniformity-1.png b/docs/reference/wiedemann.safflower.uniformity-1.png deleted file mode 100644 index de95b79a..00000000 Binary files a/docs/reference/wiedemann.safflower.uniformity-1.png and /dev/null differ diff --git a/docs/reference/wiedemann.safflower.uniformity-2.png b/docs/reference/wiedemann.safflower.uniformity-2.png deleted file mode 100644 index 7b965fd8..00000000 Binary files a/docs/reference/wiedemann.safflower.uniformity-2.png and /dev/null differ diff --git a/docs/reference/wiedemann.safflower.uniformity.html b/docs/reference/wiedemann.safflower.uniformity.html deleted file mode 100644 index 93cb16c7..00000000 --- a/docs/reference/wiedemann.safflower.uniformity.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - - - -Uniformity trial of safflower — wiedemann.safflower.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Uniformity trial of safflower

    - - -
    - -
    -

    Uniformity trial of safflower at Farmington, Utah, 1960.

    -
    - - 
    data("wiedemann.safflower.uniformity")
    - - -

    Format

    - -

    A data frame with 1782 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    yield, grams

    - -
    - -

    Details

    - - - -

    This trial was planted at University Field Station, Farmington, Utah, - in 1960, on a plot of land about one half acre in size. The soil was - not too uniform...the northern third of the field was clay and the - rest was gravelly. Rows were planted 22 inches apart, 62 rows total, - each row running the length of the field. Before harvest, 4 rows were - removed from each side, and 12 feet was removed from each end. Each - row was harvested in five-foot lengths, threshed, and the seed weighed - to the nearest gram.

    -

    The northern third of the field had yields twice as high as the - remaining part of the field because the soil had better moisture - retention. The remaining part of the field had yields that were more - uniform.

    -

    Wiedemann determined the optimum plot size to be about 8 basic - plots. The shape of the plot was not very important. But, two-row - plots were recommended for simplicity of harvest, so 3.33 feet by 20 - feet.

    -

    Based on operational costs, K1=74 percent and K2=26 percent.

    -

    Field width: 33 plots/ranges * 5ft = 165 feet

    -

    Field length: 54 rows * 22 in/row = 99 feet

    -

    For this R package, the tables in Wiedemann were converted by OCR to - digital format, and all values were checked by hand.

    -

    The original source document has columns labeled 33, 32, ... 1. Here - the columns are labeled 1:33 so that plotting tools work normally. - See Wiedemann figure 8.

    -

    Wiedemann notes the statistical analysis of the data required 100 - hours of labor. Today the analysis takes only a second.

    -

    Source

    - -

    Wiedemann, Alfred Max. 1962. - Estimation of Optimum Plot Size and Shape for Use in Safflower Yield Trails. Table 5. - All Graduate Theses and Dissertations. Paper 3600. Table 5. - https://digitalcommons.usu.edu/etd/3600

    -

    References

    - -

    None.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-  data(wiedemann.safflower.uniformity)
-  dat <- wiedemann.safflower.uniformity
-
-  # CV of entire field = 39
-  sd(dat$yield)/mean(dat$yield)
-
    #> [1] 0.3916795
    
-  libs(desplot)
-  desplot(dat, yield~col*row,
-          flip=TRUE, tick=TRUE, aspect =99/165, # true aspect
-          main="wiedemann.safflower.uniformity (true shape)")
-
    
-  libs(agricolae)
-  libs(reshape2)
-  dmat <- acast(dat, row~col, value.var='yield')
-  agricolae::index.smith(dmat,
-              main="wiedemann.safflower.uniformity",
-              col="red")
-
      
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/williams.barley.uniformity-1.png b/docs/reference/williams.barley.uniformity-1.png deleted file mode 100644 index b6d03ebd..00000000 Binary files a/docs/reference/williams.barley.uniformity-1.png and /dev/null differ diff --git a/docs/reference/williams.barley.uniformity-2.png b/docs/reference/williams.barley.uniformity-2.png deleted file mode 100644 index 4e5f61d4..00000000 Binary files a/docs/reference/williams.barley.uniformity-2.png and /dev/null differ diff --git a/docs/reference/williams.barley.uniformity-3.png b/docs/reference/williams.barley.uniformity-3.png deleted file mode 100644 index d030dc34..00000000 Binary files a/docs/reference/williams.barley.uniformity-3.png and /dev/null differ diff --git a/docs/reference/williams.barley.uniformity.html b/docs/reference/williams.barley.uniformity.html deleted file mode 100644 index 2773a3a3..00000000 --- a/docs/reference/williams.barley.uniformity.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Uniformity trial of barley — williams.barley.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - -
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    -

    Uniformity trial of barley

    - - -
    - -
    -

    Uniformity trial of barley at Narrabri, New South Wales, 1984.

    -
    - - - -

    Format

    - -

    A data frame with 720 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    grain yield kg/ha divided by 10

    - -
    - -

    Details

    - -

    Grown at Roseworthy Agricultural College. Plots were 5 m long (4 m - sown, 3.3 m harvested) by 0.75 m wide.

    -

    A three-plot seeder was used, planting in a serpentine fashion. - Williams noted that it appears that the middle plot of each pass - has a lower yield, possibly due to soil compaction from the tractor.

    -

    Field width: 48 plots * .75 m = 36 m

    -

    Field length: 15 plots * 5 m = 75 m

    -

    Source

    - -

    Williams, ER and Luckett, DJ. 1988. - The use of uniformity data in the design and analysis of cotton and barley variety trials. - Australian Journal of Agricultural Research, 39, 339-350. - https://doi.org/10.1071/AR9880339

    -

    References

    - -

    Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul - H. C. Eilersd (2018). - Correcting for spatial heterogeneity in plant breeding experiments - with P-splines. - Spatial Statistics, 23, 52-71. - https://doi.org/10.1016/j.spasta.2017.10.003

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(williams.barley.uniformity)
-dat <- williams.barley.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        aspect= 75/36, # true aspect
-        main="williams.barley.uniformity")
-
    
-
-# Smoothed contour/persp plot like Williams Fig 1b, 2b
-libs(lattice)
-dat$fit <- fitted(loess(yield~col*row, dat, span=.1))
-contourplot(fit~col*row, data=dat,
-            aspect=75/36, region=TRUE, col.regions=RedGrayBlue,
-            main="williams.barley.uniformity")
-
    wireframe(fit~col*row, data=dat, zlim=c(100, 350),
-          main="williams.barley.uniformity")
-
    
-# Williams table 1
-anova(aov(yield ~ factor(row) + factor(col), dat))
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> factor(row)  14 142828 10202.0  26.941 < 2.2e-16 ***
-#> factor(col)  47 209087  4448.6  11.748 < 2.2e-16 ***
-#> Residuals   658 249174   378.7                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/williams.cotton.uniformity-1.png b/docs/reference/williams.cotton.uniformity-1.png deleted file mode 100644 index 9e5aca3b..00000000 Binary files a/docs/reference/williams.cotton.uniformity-1.png and /dev/null differ diff --git a/docs/reference/williams.cotton.uniformity-2.png b/docs/reference/williams.cotton.uniformity-2.png deleted file mode 100644 index 952653f1..00000000 Binary files a/docs/reference/williams.cotton.uniformity-2.png and /dev/null differ diff --git a/docs/reference/williams.cotton.uniformity.html b/docs/reference/williams.cotton.uniformity.html deleted file mode 100644 index 677fafc3..00000000 --- a/docs/reference/williams.cotton.uniformity.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Uniformity trial of cotton — williams.cotton.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of cotton

    - - -
    - -
    -

    Uniformity trial of cotton at Narrabri, New South Wales, 1984.

    -
    - - - -

    Format

    - -

    A data frame with 288 observations on the following 3 variables.

    -
    row

    row

    -
    col

    column

    -
    yield

    lint yield, kg/ha divided by 10

    - -
    - -

    Details

    - -

    Cotton uniformity trial grown at Narrabri, New South Wales, 1984-1985. - Plots were 12m long, 1m apart, 12 rows by 24 columns, with an - irrigation furrow between columns.

    -

    Field width: 24 plots * 1 m = 24 m

    -

    Field length: 12 plots * 12 m = 144 m

    -

    Source

    - -

    Williams, ER and Luckett, DJ. 1988. - The use of uniformity data in the design and analysis of cotton and barley variety trials. - Australian Journal of Agricultural Research, 39, 339-350. - https://doi.org/10.1071/AR9880339

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(williams.cotton.uniformity)
-dat <- williams.cotton.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        aspect=144/24, # true aspect
-        main="williams.cotton.uniformity")
-
    
-
-# Smoothed contour/persp plot like Williams 1988 Fig 1a, 2a
-dat$fit <- fitted(loess(yield~col*row, dat, span=.5))
-libs("lattice")
-contourplot(fit~col*row, data=dat,
-            aspect=144/24,
-            region=TRUE, cuts=6, col.regions=RedGrayBlue,
-            main="williams.cotton.uniformity")
-
    # wireframe(fit~col*row, data=dat, zlim=c(100, 250),
-#           main="williams.cotton.uniformity")
-
-# Williams table 1
-anova(aov(yield ~ factor(row) + factor(col), dat))
-
    #> Analysis of Variance Table
-#> 
-#> Response: yield
-#>              Df Sum Sq Mean Sq F value    Pr(>F)    
-#> factor(row)  11  30707 2791.58  14.450 < 2.2e-16 ***
-#> factor(col)  23  44762 1946.16  10.074 < 2.2e-16 ***
-#> Residuals   253  48875  193.18                      
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/williams.trees-1.png b/docs/reference/williams.trees-1.png deleted file mode 100644 index 2ff7d8d7..00000000 Binary files a/docs/reference/williams.trees-1.png and /dev/null differ diff --git a/docs/reference/williams.trees.html b/docs/reference/williams.trees.html deleted file mode 100644 index 258b170f..00000000 --- a/docs/reference/williams.trees.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - -Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand

    - - -
    - -
    -

    Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand

    -
    - - - -

    Format

    - -

    A data frame with 222 observations on the following 4 variables.

    -
    env

    Environment factor, 6 levels

    -
    gen

    Genetic factor, 37 levels

    -
    height

    Height (cm)

    -
    survival

    Survival percentage

    - -
    - -

    Details

    - -

    Planted in 1985 at six sites in Thailand. RCB with 3 reps. The data - here is the mean of the three reps. Plots were 5 meters square with - spacing 2m x 2m. Measurements collected at 24 months. The gen - column in the data is actually seedlot, as some tree species - have multiple seed lots. The trees are mostly acacia and eucalyptus.

    -

    Used with permission of Emlyn Williams.

    -

    Source

    - -

    Williams, ER and Luangviriyasaeng, V. 1989. - Statistical analysis of tree species trial and seedlot:site - interaction in Thailand. Chapter 14 of Trees for the - Tropics: Growing Australian Multipurpose Trees and Shrubs in - Developing Countries. Pages 145--152. - https://aciar.gov.au/publication/MN010

    -

    References

    - -

    E. R. Williams and A. C. Matheson and C. E Harwood, - Experimental Design and Analysis for Tree Improvement. - CSIRO Publishing, 2002.

    - -

    Examples

    - 
    
-library(agridat)
-
-data(williams.trees)
-dat <- williams.trees
-
-libs(lattice)
-xyplot(survival~height|env,dat, main="williams.trees", xlab="Height",
-ylab="Percent surviving")
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/woodman.pig-1.png b/docs/reference/woodman.pig-1.png deleted file mode 100644 index cba1ed2e..00000000 Binary files a/docs/reference/woodman.pig-1.png and /dev/null differ diff --git a/docs/reference/woodman.pig.html b/docs/reference/woodman.pig.html deleted file mode 100644 index 847a0747..00000000 --- a/docs/reference/woodman.pig.html +++ /dev/null @@ -1,226 +0,0 @@ - -Weight gain in pigs for different treatments — woodman.pig • agridat - - -
    -
    - - - -
    -
    -
    -

    Weight gain in pigs for different treatments

    - - -
    - -
    -

    Weight gain in pigs for different treatments, with initial weight and - feed eaten as covariates.

    -
    - -
    - 
    data("woodman.pig")
    -
    - -
    -

    Format

    -

    A data frame with 30 observations on the following 7 variables.

    pen
    -

    pen

    - -
    treatment
    -

    diet

    - -
    pig
    -

    pig number

    - -
    sex
    -

    sex

    - -
    weight1
    -

    initial weight in pounds, week 0

    - -
    weight2
    -

    final weight in pounds, week 16

    - -
    feed
    -

    feed eaten in pounds

    - -
    w0
    -

    initial weight

    - -
    g
    -

    average weekly gain

    - -
    h
    -

    half rate of change in growth

    - - -
    -
    -

    Details

    - - -

    Six pigs in each of 5 pens were fed individually. From each litter - there were 3 males and 3 females chosen for a pen. Three different - diet treatments were used.

    -

    Note: Woodman gives the initial weights to the nearest 0.5 - pounds.

    -

    The w0, g, h columns are from Wishart 1938. Wishart used the weekly - weight measurements (not available) to fit quadratic growth curves for - each pig and then reported the constants. These are the data that are - widely used by many authors.

    -
    -
    -

    Source

    -

    Woodman, Evans, Callow & Wishart (1936). - The nutrition of the bacon pig. I. The influence of high levels of protein intake on growth, conformation and quality in the bacon pig. - The Journal of Agricultural Science, 26, 546 - 619. - Table V, Page 557. - https://doi.org/10.1017/S002185960002308X

    -

    Wishart, J. (1938). - Growth-rate determinations in nutrition studies with the bacon pig and their analysis. - Biometrika, 30: 16-28. Page 20, table 2. - https://doi.org/10.2307/2332221

    -
    -
    -

    References

    -

    Wishart (1950) Table 2, p 17.

    -

    Bernard Ostle (1963). Statistics in Research, 2nd ed. Page 455. - https://archive.org/details/secondeditionsta001000mbp

    -

    Henry Scheffe (1999). - The Analysis of Variance. Page 217.

    -

    Peter H Westfall, Randall Tobias, Russell D Wolfinger (2011). - Multiple Comparisons and Multiple Tests using SAS. Sec 8.3.

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-  library(agridat)
-  data(woodman.pig)
-  dat <- woodman.pig
-  
-  # add day of year for each weighing
-  dat <- transform(dat, date1=36, date2=148)
-  plot(NA, xlim=c(31,153), ylim=c(28,214),
-       xlab="day of year", ylab="weight")
-  segments(dat$date1, dat$weight1, dat$date2, dat$weight2,
-           col=as.numeric(as.factor(dat$treatment)))
-  title("woodman.pig")
-
-
-  # Average gain per week
-  dat <- transform(dat, pen=factor(pen), treatment=factor(treatment),
-                   sex=factor(sex))
-  m1 <- lm(g ~ -1 + pen + treatment +sex + treatment:sex + w0, data=dat)
-  anova(m1)
-#> Analysis of Variance Table
-#> 
-#> Response: g
-#>               Df  Sum Sq Mean Sq   F value    Pr(>F)    
-#> pen            5 2603.57  520.71 1938.4699 < 2.2e-16 ***
-#> treatment      2    2.32    1.16    4.3262  0.028294 *  
-#> sex            1    0.27    0.27    1.0079  0.328016    
-#> w0             1    3.62    3.62   13.4800  0.001622 ** 
-#> treatment:sex  2    0.33    0.16    0.6074  0.555012    
-#> Residuals     19    5.10    0.27                        
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-  # Compare diets. Results similar to Westfall 8.13
-  libs(emmeans)
-  pairs(emmeans(m1, "treatment"))
-#> NOTE: Results may be misleading due to involvement in interactions
-#>  contrast estimate    SE df t.ratio p.value
-#>  A - B       0.446 0.233 19   1.916  0.1614
-#>  A - C       0.681 0.232 19   2.938  0.0220
-#>  B - C       0.235 0.233 19   1.009  0.5806
-#> 
-#> Results are averaged over the levels of: pen, sex 
-#> P value adjustment: tukey method for comparing a family of 3 estimates 
-  # NOTE: Results may be misleading due to involvement in interactions
-  #  contrast estimate    SE df t.ratio p.value
-  #  A - B      0.4283 0.288 19 1.490   0.3179 
-  #  A - C      0.5200 0.284 19 1.834   0.1857 
-  #  B - C      0.0918 0.288 19 0.319   0.9456 
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/wyatt.multi.uniformity-1.png b/docs/reference/wyatt.multi.uniformity-1.png deleted file mode 100644 index 603711ad..00000000 Binary files a/docs/reference/wyatt.multi.uniformity-1.png and /dev/null differ diff --git a/docs/reference/wyatt.multi.uniformity-2.png b/docs/reference/wyatt.multi.uniformity-2.png deleted file mode 100644 index f44ae0ef..00000000 Binary files a/docs/reference/wyatt.multi.uniformity-2.png and /dev/null differ diff --git a/docs/reference/wyatt.multi.uniformity-3.png b/docs/reference/wyatt.multi.uniformity-3.png deleted file mode 100644 index f9e80f94..00000000 Binary files a/docs/reference/wyatt.multi.uniformity-3.png and /dev/null differ diff --git a/docs/reference/wyatt.multi.uniformity.html b/docs/reference/wyatt.multi.uniformity.html deleted file mode 100644 index 3160ff67..00000000 --- a/docs/reference/wyatt.multi.uniformity.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - - -Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Uniformity trial of oats and wheat on the same ground.

    - - -
    - -
    -

    Uniformity trial of oats and wheat on the same ground.

    -
    - - 
    data("wyatt.multi.uniformity")
    - - -

    Format

    - -

    A data frame with 258 observations on the following 5 variables.

    -
    col

    column

    -
    row

    row

    -
    yield

    yield, bu/ac

    -
    year

    year

    -
    crop

    crop

    - -
    - -

    Details

    - -

    Experiments conducted at the Soils Experimental field at the University of Alberta, Canada. - Oats were grown in 1925, The average yield was 88 bushels. - Wheat was grown in 1926, with an average yield of 32.2 bu/acre. - The data reported are relative yields within each year.

    -

    The plot size in rows 1 and 2 (Series A and B in the original paper) is 1/10th acre. - The plot size in row 3 is 1/11 acre.

    -

    Field length: 3 plots (140 ft, 140 ft, 128 ft) + 2 roads * 16 feet = 440 feet.

    -

    Field width: 43 plots * 37 ft = 1591 feet.

    -

    Source

    - -

    F. A. Wyatt (1927). - Variation in plot yields due to soil heterogeneity. - Scientific Agriculture, 7, 248-256. Table 1. - https://doi.org/10.4141/sa-1927-0020

    -

    References

    - -

    None

    - -

    Examples

    - 
    
-library(agridat)
-data(wyatt.multi.uniformity)
-dat <- wyatt.multi.uniformity
-
-# range of yields. Wyatt has 48.6 bu/ac for oats, 10.4 for wheat
-# diff(range(na.omit(subset(dat, crop=="oats")$yield)/100*88)) # 48.4
-# diff(range(na.omit(subset(dat, crop=="wheat")$yield)/100*32.8)) # 10.5 
-
-# std dev. Wyatt has 9.18 bu/ac for oats, 2.06 for wheat, 2.06 for wheat
-# sd(na.omit(subset(dat, crop=="oats")$yield)/100*88) # 9.11
-# sd(na.omit(subset(dat, crop=="wheat")$yield)/100*32.8) # 2.14
-
-# correlation across years. Wyatt has .08
-# cor(reshape2::acast(dat, row+col ~ crop, value.var="yield"), use="pair")
-
-# Fig 3
-libs(lattice)
-xyplot(yield ~ col|factor(row), dat, group=crop,
-       main="wyatt.multi.uniformity",
-       type='l', layout=c(1,3), auto.key=TRUE )                
-
    
-# \dontrun{
-libs(desplot)
-desplot(dat, yield ~ col*row, subset=crop=="oats",
-        tick=TRUE,
-        aspect=(440)/(1591), # true aspect
-        main="wyatt.multi.uniformity - 1925 oats")
-
    desplot(dat, yield ~ col*row, subset=crop=="wheat",
-        aspect=(440)/(1591), # true aspect
-        main="wyatt.multi.uniformity - 1926 wheat")
-
    # }
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/yan.winterwheat-1.png b/docs/reference/yan.winterwheat-1.png deleted file mode 100644 index b16af53d..00000000 Binary files a/docs/reference/yan.winterwheat-1.png and /dev/null differ diff --git a/docs/reference/yan.winterwheat.html b/docs/reference/yan.winterwheat.html deleted file mode 100644 index fc437d51..00000000 --- a/docs/reference/yan.winterwheat.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - - - -Multi-environment trial of winter wheat in Ontario — yan.winterwheat • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of winter wheat in Ontario

    - - -
    - -
    -

    Yield of 18 varieties of winter wheat grown at 9 environments in - Ontario in 1993.

    -
    - - - -

    Details

    - -

    The yield is the mean of several reps, measured in metric tons per hectare.

    -

    This data has often been used to illustrate GGE biplots.

    -

    Format

    - -

    A data frame with 162 observations on the following 3 variables.

    -
    gen

    genotype

    -
    env

    environment

    -
    yield

    yield in metric tons per hectare

    - -
    - -

    Used with permission of Weikai Yan.

    -

    Source

    - -

    Weikai Yan and M.S. Kang (2002). - GGE biplot analysis: A graphical tool for breeders, geneticists, and - agronomists. - CRC. Page 59.

    -

    Weikai Yan and Nicholas A. Tinker. 2006. - Biplot analysis of multi-environment trial data: Principles and - applications. Table 1.

    -

    References

    - - - -

    Weikai Yan and Manjit S. Kang and Baoluo Ma and Sheila Woods, 2007, - GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data, - Crop Science, 2007, 47, 641--653. - https://doi.org/10.2135/cropsci2006.06.0374

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-
-data(yan.winterwheat)
-dat <- yan.winterwheat
-
-libs(gge)
-m1 <- gge(dat, yield ~ gen*env)
-biplot(m1, flip=c(1,1), hull=TRUE,
-       main="yan.winterwheat - GGE biplot")
-
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/yang.barley.html b/docs/reference/yang.barley.html deleted file mode 100644 index adfe8b2e..00000000 --- a/docs/reference/yang.barley.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta.

    - - -
    - -
    -

    Yield of 6 barley varieties at 18 locations in Alberta.

    -
    - - 
    data("yang.barley")
    - - -

    Format

    - -

    A data frame with 108 observations on the following 3 variables.

    -
    site

    site factor, 18 levels

    -
    gen

    genotype factor, 6 levels

    -
    yield

    yield, Mg/ha

    - -
    - -

    Details

    - -

    From an experiment in 2003. Yang (2013) uses this data to illustrate - a procedure for bootstrapping biplots.

    -

    - - - - - - - - - - - - - - - - - - - -
    sitelonglat
    Beaverlodge119.4355.21
    BigLakes113.7053.61
    Calmar113.8553.26
    CdcNorth113.3353.63
    DawsonCreek120.2355.76
    FtKent110.6154.31
    FtStJohn120.8556.25
    Irricana113.6051.32
    Killam111.8552.78
    Lacombe113.7352.46
    LethbridgeDry112.8149.70
    LethbridgeIrr112.8149.70
    Lomond112.6550.35
    Neapolis113.8651.65
    NorthernSunriseNANA
    Olds114.0951.78
    StPaul111.2853.98
    Stettler112.7152.31
    - - - -

    Used with permission of Rong-Cai Yang.

    -

    Source

    - -

    Rong-Cai Yang (2007). - Mixed-Model Analysis of Crossover Genotype-Environment Interactions. - Crop Science, 47, 1051-1062. - https://doi.org/10.2135/cropsci2006.09.0611

    -

    References

    - -

    Zhiqiu Hu and Rong-Cai Yang, (2013). - Improved Statistical Inference for Graphical Description and - Interpretation of Genotype x Environment Interaction. - Crop Science, 53, 2400-2410. - https://doi.org/10.2135/cropsci2013.04.0218

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(yang.barley)
-dat <- yang.barley
-
-libs(reshape2)
-dat <- acast(dat, gen~site, value.var='yield')
-
-  ## For bootstrapping of a biplot, see the non-cran packages:
-  ## 'bbplot' and 'distfree.cr'
-  ## https://statgen.ualberta.ca/index.html?open=software.html
-  ## install.packages("https://statgen.ualberta.ca/download/software/bbplot_1.0.zip")
-  ## install.packages("https://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip")
-
-  ## libs(SDMTools)
-  ## libs(distfree.cr)
-  ## libs(bbplot)
-
-  ## d1 <- bbplot.boot(dat, nsample=2000) # bootstrap the data
-  ## plot(d1) # plot distributions of principal components
-  ## b1 <- bbplot(d1) # create data structures for the biplot
-  ## plot(b1) # create the confidence regions on the biplot
-
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/yates.missing-1.png b/docs/reference/yates.missing-1.png deleted file mode 100644 index fcfca21a..00000000 Binary files a/docs/reference/yates.missing-1.png and /dev/null differ diff --git a/docs/reference/yates.missing.html b/docs/reference/yates.missing.html deleted file mode 100644 index 64908a2f..00000000 --- a/docs/reference/yates.missing.html +++ /dev/null @@ -1,270 +0,0 @@ - - - - - - - - -Factorial experiment with missing values — yates.missing • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Factorial experiment with missing values

    - - -
    - -
    -

    Potato factorial experiment with missing values

    -
    - - - -

    Format

    - -

    A data frame with 80 observations on the following 3 variables.

    -
    trt

    treatment factor with levels 0 K - N P NK KP NP NKP

    -
    block

    block, 10 levels

    -
    y

    infection intensity

    - -
    - -

    Details

    - -

    The response variable y is the intensity of infection of potato - tubers innoculated with Phytophthora Erythroseptica.

    -

    Yates (1933) presents an iterative algorithm to estimate missing - values in a matrix, using this data as an example.

    -

    Source

    - -

    F. Yates, 1933. - The analysis of replicated experiments when the field results are incomplete. - Emp. J. Exp. Agric., 1, 129--142.

    -

    References

    - -

    Steel & Torrie, 1980, Principles and Procedures of Statistics, - 2nd Edition, page 212.

    - -

    Examples

    - 
    # \dontrun{
-
-library(agridat)
-data(yates.missing)
-dat <- yates.missing
-
-libs(lattice)
-bwplot(y ~ trt, data=dat,
-       xlab="Treatment", ylab="Infection intensity",
-       main="yates.missing")
-
    
-libs(reshape2)
-mat0 <- acast(dat[, c('trt','block','y')], trt~block,
-               id.var=c('trt','block'), value.var='y')
-
-# Use lm to estimate missing values.  The estimated missing values
-# are the same as in Yates (1933)
-m1 <- lm(y~trt+block, dat)
-dat$pred <- predict(m1, new=dat[, c('trt','block')])
-dat$filled <- ifelse(is.na(dat$y), dat$pred, dat$y)
-mat1 <- acast(dat[, c('trt','block','pred')], trt~block,
-               id.var=c('trt','block'), value.var='pred')
-
-
-# Another method to estimate missing values via PCA
-libs("nipals")
-m2 <- nipals(mat0, center=FALSE, ncomp=3, fitted=TRUE)
-# mat2 <- m2$scores 
-mat2 <- m2$fitted
-
-# Compare
-ord <- c("0","N","K","P","NK","NP","KP","NKP")
-print(mat0[ord,], na.print=".")
-
    #>      B01  B02  B03  B04  B05  B06  B07  B08  B09  B10
-#> 0   3.55 2.29    . 2.00 3.34 3.83 3.86 3.50 2.23 2.91
-#> N   2.30 4.03 2.54 2.82 3.29 2.93    . 2.55 2.20 2.30
-#> K   3.96 3.62 3.46 2.50 2.94 3.70 3.82 2.54 3.18 3.69
-#> P   2.99 3.99 2.90 3.97 4.49 4.70 3.86    . 3.50 3.59
-#> NK     . 3.07 3.49 1.07 3.99 3.48 3.80 3.68 3.24 2.70
-#> NP  2.36 3.47 2.64 3.17 3.26 3.28    .    . 3.07 3.12
-#> KP  2.16 2.34 1.96 2.60 3.77    . 3.20 3.47 2.67 3.33
-#> NKP 3.16 2.52 2.39 3.68    .    . 3.85 3.36 2.50 4.13
    round(mat1[ord,] ,2)
-
    #>      B01  B02  B03  B04  B05  B06  B07  B08  B09  B10
-#> 0   2.75 3.00 2.58 2.56 3.43 3.46 3.50 3.11 2.66 3.05
-#> N   2.57 2.82 2.39 2.38 3.25 3.28 3.31 2.93 2.47 2.87
-#> K   3.08 3.33 2.91 2.89 3.77 3.79 3.83 3.44 2.99 3.39
-#> P   3.53 3.78 3.36 3.34 4.21 4.24 4.27 3.89 3.43 3.83
-#> NK  2.88 3.13 2.71 2.69 3.57 3.59 3.63 3.24 2.79 3.18
-#> NP  2.86 3.11 2.69 2.67 3.54 3.57 3.61 3.22 2.77 3.16
-#> KP  2.63 2.87 2.45 2.43 3.31 3.33 3.37 2.98 2.53 2.93
-#> NKP 3.05 3.30 2.88 2.86 3.73 3.76 3.79 3.41 2.95 3.35
    round(mat2[ord,] ,2)
-
    #>      B01  B02  B03  B04  B05  B06  B07  B08  B09  B10
-#> 0   3.56 2.17 3.00 1.58 3.46 3.58 3.80 3.44 2.53 3.09
-#> N   2.37 3.63 2.52 2.84 3.01 3.07 3.03 2.46 2.72 2.72
-#> K   3.59 3.92 3.56 2.22 3.29 3.52 3.82 2.91 3.14 3.01
-#> P   2.77 4.05 2.77 4.09 4.35 4.27 3.99 3.69 3.36 3.88
-#> NK  3.74 2.94 3.38 1.78 3.44 3.64 3.92 3.30 2.85 3.11
-#> NP  2.59 3.67 2.64 3.18 3.50 3.52 3.41 2.95 2.92 3.15
-#> KP  2.23 2.29 1.92 3.03 3.69 3.56 3.26 3.35 2.41 3.25
-#> NKP 2.77 2.60 2.39 3.12 4.07 3.98 3.75 3.75 2.74 3.60
    
-# SVD with 3 components recovers original data better
-sum((mat0-mat1)^2, na.rm=TRUE)
-
    #> [1] 17.68986
    sum((mat0-mat2)^2, na.rm=TRUE) # Smaller SS => better fit
-
    #> [1] 5.42107
    # }
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/yates.oats-1.png b/docs/reference/yates.oats-1.png deleted file mode 100644 index c35ace7a..00000000 Binary files a/docs/reference/yates.oats-1.png and /dev/null differ diff --git a/docs/reference/yates.oats-2.png b/docs/reference/yates.oats-2.png deleted file mode 100644 index 51f48de7..00000000 Binary files a/docs/reference/yates.oats-2.png and /dev/null differ diff --git a/docs/reference/yates.oats-3.png b/docs/reference/yates.oats-3.png deleted file mode 100644 index 56f1a57b..00000000 Binary files a/docs/reference/yates.oats-3.png and /dev/null differ diff --git a/docs/reference/yates.oats.html b/docs/reference/yates.oats.html deleted file mode 100644 index a0205131..00000000 --- a/docs/reference/yates.oats.html +++ /dev/null @@ -1,351 +0,0 @@ - -Split-plot experiment of oats — yates.oats • agridat - - -
    -
    - - - -
    -
    -
    -

    Split-plot experiment of oats

    - - -
    - -
    - - -

    The yield of oats from a split-plot field trial conducted at - Rothamsted in 1931.

    -

    Varieties were applied to the main plots.

    -

    Manurial (nitrogen) treatments were applied to the sub-plots.

    -

    Each plot is 1/80 acre = 28.4 links * 44 links.

    -

    Field width: 4 plots * 44 links = 176 links.

    -

    Field length: 18 rows * 28.4 links = 511 links

    -

    The 'block' numbers in this data are as given in the Rothamsted Report. - The 'grain' and 'straw' values are the actual pounds per sub-plot as - shown in the Rothamsted Report. Each sub-plot is 1/80 acre, and a - 'hundredweight (cwt)' is 112 pounds, so converting from sub-plot - weight to hundredweight/acre needs a conversion factor of 80/112.

    -

    The 'yield' values are the values as they appeared in the paper by - Yates, who used 1/4-pounds as the units (i.e. he multiplied the - original weight by 4) for simpler calculations.

    -
    - - -
    -

    Format

    -

    row
    -

    row

    - -
    col
    -

    column

    - -
    yield
    -

    yield in 1/4 pounds per sub-plot, each 1/80 acre

    - -
    nitro
    -

    nitrogen treatment in hundredweight per acre

    - -
    gen
    -

    genotype, 3 levels

    - -
    block
    -

    block, 6 levels

    - -
    grain
    -

    grain weight in pounds per sub-plot

    - -
    straw
    -

    straw weight in pounds per sub-plot

    - - -
    -
    -

    Source

    -

    Report for 1931. Rothamsted Experiment Station. Page 143. - https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1931-141-159

    -
    -
    -

    References

    -

    Yates, Frank (1935) Complex experiments, - Journal of the Royal Statistical Society Suppl - 2, 181-247. Figure 2. - https://doi.org/10.2307/2983638

    -
    - -
    -

    Examples

    - 
    # \dontrun{
-
-  library(agridat)
-  data(yates.oats)
-  dat <- yates.oats
-
-
-  ## # Means match Rothamsted report p. 144
-  ## libs(dplyr)
-  ## dat 
-  ##   summarize(grain=mean(grain)*80/112,
-  ##             straw=mean(straw)*80/112)
-
-  libs(desplot)
-  # Experiment design & yield heatmap
-  desplot(dat, block ~ col*row, col.regions=c("black","yellow"),
-          out1=block, num=nitro, col=gen,
-          cex=1, aspect=511/176, # true aspect
-          main="yates.oats")
-
-
-
-  # Roughly linear gradient across the field.  The right-half of each
-  # block has lower yield.  The blocking is inadequate!
-  libs("lattice")
-  xyplot(yield ~ col|factor(nitro), dat,
-         type = c('p', 'r'), xlab='col', as.table = TRUE,
-         main="yates.oats")
-
-
-  libs(lme4)
-  # Typical split-plot analysis. Non-significant gen differences
-  m3 <- lmer(yield ~ factor(nitro) * gen + (1|block/gen), data=dat)
-  # Residuals still show structure
-  xyplot(resid(m3) ~ dat$col, xlab='col', type=c('p','smooth'),
-         main="yates.oats")
-#> Warning: pseudoinverse used at 2
-#> Warning: neighborhood radius 1
-#> Warning: reciprocal condition number  0
-#> Warning: There are other near singularities as well. 1
-#> Warning: pseudoinverse used at 2
-#> Warning: neighborhood radius 1
-#> Warning: reciprocal condition number  0
-#> Warning: There are other near singularities as well. 1
-#> Warning: pseudoinverse used at 2
-#> Warning: neighborhood radius 1
-#> Warning: reciprocal condition number  0
-#> Warning: There are other near singularities as well. 1
-#> Warning: pseudoinverse used at 2
-#> Warning: neighborhood radius 1
-#> Warning: reciprocal condition number  0
-#> Warning: There are other near singularities as well. 1
-#> Warning: pseudoinverse used at 2
-#> Warning: neighborhood radius 1
-#> Warning: reciprocal condition number  0
-#> Warning: There are other near singularities as well. 1
-
-
-  # Add a linear trend for column
-  m4 <- lmer(yield ~ col + factor(nitro) * gen + (1|block/gen), data=dat)
-  # xyplot(resid(m4) ~ dat$col, type=c('p','smooth'), xlab='col')
-
-  ## Compare fits
-  AIC(m3,m4)
-#>    df      AIC
-#> m3 15 559.0285
-#> m4 16 533.3343
-  ##    df      AIC
-  ## m3  9 581.2372
-  ## m4 10 557.9424 # Substantially better
-
-
-  # ----------
-
-  # Marginal predictions from emmeans package and asreml::predict
-
-  # --- nlme ---
-  libs(nlme)
-  libs(emmeans)
-  # create unbalance
-  dat2 <- yates.oats[-c(1,2,3,5,8,13,21,34,55),]
-  m5l <- lme(yield ~ factor(nitro) + gen, random = ~1 | block/gen,
-             data = dat2)
-
-  # asreml r 4 has a bug with asreml( factor(nitro))
-  dat2$nitrof <- factor(dat2$nitro)
-
-  # --- asreml4  ---
-  libs(asreml)
-  m5a <- asreml(yield ~ nitrof + gen,
-                random = ~ block + block:gen, data=dat2)
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:52 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -202.393       297.150     57 15:17:52    0.0
-#>  2      -198.059       229.020     57 15:17:52    0.0
-#>  3      -194.582       178.247     57 15:17:52    0.0
-#>  4      -193.036       152.265     57 15:17:52    0.0
-#>  5      -192.591       140.059     57 15:17:52    0.0
-#>  6      -192.551       137.448     57 15:17:52    0.0
-#>  7      -192.550       137.136     57 15:17:52    0.0
-  libs(lucid)
-  vc(m5l)
-#>         effect variance stddev
-#>        block =       NA     NA
-#>    (Intercept)    186    13.64
-#>          gen =       NA     NA
-#>  (Intercept).1    179.6  13.4 
-#>       Residual    137.1  11.71
-  vc(m5a)
-#>     effect component std.error z.ratio bound %ch
-#>      block     186      167.4      1.1     P 0.1
-#>  block:gen     179.6     99.63     1.8     P 0  
-#>    units!R     137.1     29.93     4.6     P 0  
-
-  emmeans::emmeans(m5l, "gen")
-#>  gen        emmean   SE df lower.CL upper.CL
-#>  GoldenRain  103.4 8.37  5     81.9      125
-#>  Marvellous  108.9 8.18  5     87.8      130
-#>  Victory      97.3 8.18  5     76.3      118
-#> 
-#> Results are averaged over the levels of: nitro 
-#> Degrees-of-freedom method: containment 
-#> Confidence level used: 0.95 
-  predict(m5a, data=dat2, classify="gen")$pvals
-#> Model fitted using the gamma parameterization.
-#> ASReml 4.1.0 Fri Dec 17 15:17:52 2021
-#>           LogLik        Sigma2     DF     wall    cpu
-#>  1      -192.550       137.129     57 15:17:52    0.0
-#>  2      -192.550       137.129     57 15:17:52    0.0
-#>  3      -192.550       137.129     57 15:17:52    0.0
-#> 
-#> Notes:
-#> - The predictions are obtained by averaging across the hypertable
-#>   calculated from model terms constructed solely from factors in
-#>   the averaging and classify sets.
-#> - Use 'average' to move ignored factors into the averaging set.
-#> - The simple averaging set: nitrof
-#> - The ignored set: block
-#> 
-#> 
-#>          gen predicted.value std.error    status
-#> 1 GoldenRain       103.42039  8.368025 Estimable
-#> 2 Marvellous       108.86384  8.183540 Estimable
-#> 3    Victory        97.30395  8.183515 Estimable
-
-  # ----------
-
-  if(0){
-
-    # Demonstrate use of regress package, compare to lme
-
-    libs(regress)
-    m6 <- regress(yield ~ nitrof + gen, ~block + I(block:gen), identity=TRUE,
-                  verbose=1, data=dat)
-    summary(m6)
-    ## Variance Coefficients:
-    ##                Estimate Std. Error
-    ##   block         214.468    168.794
-    ##   I(block:gen)  109.700     67.741
-    ##   In            162.558     32.189
-
-    # ordinal causes clash with VarCorr
-    if(is.element("package:ordinal", search())) detach(package:ordinal)
-
-    m7 <- lme(yield ~ nitrof + gen, random = ~ 1|block/gen, data=dat)
-    lme4::VarCorr(m7)
-    ##             Variance     StdDev
-    ## block =     pdLogChol(1)
-    ## (Intercept) 214.4716     14.64485
-    ## gen =       pdLogChol(1)
-    ## (Intercept) 109.6929     10.47344
-    ## Residual    162.5591     12.74987
-  }
-
-# }
-
    -
    -
    - -
    - - -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 2.0.1.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/zuidhof.broiler-1.png b/docs/reference/zuidhof.broiler-1.png deleted file mode 100644 index 6b2b7167..00000000 Binary files a/docs/reference/zuidhof.broiler-1.png and /dev/null differ diff --git a/docs/reference/zuidhof.broiler.html b/docs/reference/zuidhof.broiler.html deleted file mode 100644 index 77497ec3..00000000 --- a/docs/reference/zuidhof.broiler.html +++ /dev/null @@ -1,239 +0,0 @@ - - - - - - - - -Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler • agridat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Daily weight, feed, egg measurements for a broiler chicken

    - - -
    - -
    -

    Daily weight, feed, egg measurements for a broiler chicken

    -
    - - - -

    Format

    - -

    A data frame with 59 observations on the following 6 variables.

    -
    bw

    Body weight, grams

    -
    targetbw

    Target body weight, grams

    -
    adfi

    Average daily feed intake, grams

    -
    adg

    Average daily gain, grams

    -
    eggwt

    Egg weight, grams

    -
    age

    Age, days

    - -
    - -

    Details

    - -

    Using graphs like the one in the examples section, the authors - discovered that a drop in body weight commonly occurs around the time - of first egg production.

    -

    Used with permission of Martin Zuidhof.

    -

    Source

    - -

    Martin J. Zuidhof and Robert A. Renema and Frank E. Robinson, (2008). - Understanding Multiple, Repeated Animal Measurements with the Help of - PROC GPLOT. SAS Global Forum 2008, Paper 250-2008. - https://support.sas.com/resources/papers/proceedings/pdfs/sgf2008/250-2008.pdf

    - -

    Examples

    - 
    
-library(agridat)
-
-data(zuidhof.broiler)
-dat <- zuidhof.broiler
-
-dat <- transform(dat, age=age/7) # Change days into weeks
-
-# Reproducing figure 1 of Zuidhof et al.
-
-# Plot using left axis
-op <- par(mar=c(5,4,4,4))
-plot(bw~age, dat, xlab="Age (weeks)", ylab="Bodyweight (g)",
-     main="zuidhof.broiler",
-     xlim=c(20,32), ylim=c(0,4000), pch=20)
-
    lines(targetbw~age, subset(dat, !is.na(targetbw)), col="black")
-
    
-# Now plot using the right axis
-par(new=TRUE)
-plot(adfi~age, subset(dat, !is.na(adfi)),
-     xlab="", ylab="", xlim=c(20,32), xaxt="n",yaxt="n",
-     ylim=c(-50,175), type="s", lty=2)
-
    axis(4, at=c(-50,-25,0,25,50,75,100,125,150,175), col="red", col.axis="red")
-
    mtext("Weight (g)", side=4, line=2, col="red")
-
    lines(adg~age, subset(dat, !is.na(adg)), col="red", type="s", lty=1, lwd=2)
-
    abline(h=c(0,52), col="red")
-
    with(dat, segments(age, 0, age, eggwt, col="red"))
-
    
-legend(20, -40, c("Body weight", "Target BW", "Feed/day", "Gain/day", "Egg wt"),
-       bty="n", cex=.5, ncol=5,
-       col=c("black","black","red","red","red"),
-       lty=c(-1,1,2,1,1), lwd=c(1,1,1,2,1), pch=c(20,-1,-1,-1,-1))
-
    par(op)
-
-
    -
    - -
    - - -
    -
    -

    Developed by Kevin Wright.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
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