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+ + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

About

+
+ + + +
+ + + + +
+ + +
+ + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/markstat-call-R-example/index.html b/docs/examples/markstat-call-R-example/index.html new file mode 100644 index 00000000..8ff441ba --- /dev/null +++ b/docs/examples/markstat-call-R-example/index.html @@ -0,0 +1,590 @@ + + + + + + + + + +mrrobust – index + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + +
+

Run MR-Base from a Stata do-file or Stata Markdown script

+
+

Introduction

+

This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.

+

This means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base.

+

Before you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).

+
ssc install whereis
+ssc install markstat
+
+
+

Example Stata Markdown document

+

The following is the example Stata Markdown document.

+
---
+title: Using mrrobust in a Markstat document example
+---
+
+We first need to register the R executable with Stata.
+```s
+if c(os) == "Windows" local rpath "C:\\Program Files\\R\\R-4.2.3\\bin\\x64\\R.exe"
+else if c(os) == "Unix" local rpath "/usr/bin/R"
+else local rpath "/usr/local/bin/R"
+whereis R "`rpath'"
+```
+
+
+Next we have an R code chunk in which we install the required packages in R.
+```r
+if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
+if (!requireNamespace("TwoSampleMR", quietly = TRUE)) remotes::install_github("MRCIEU/TwoSampleMR")
+if (!requireNamespace("MRInstruments", quietly = TRUE)) remotes::install_github("MRCIEU/MRInstruments")
+```
+
+## Extracting data from MR-Base
+
+We will be running the script from the MR-Base paper 
+([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). The R code we will use is from 
+[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).
+
+First, we load the packages into our R session. Note that the **foreign** package provides the 
+`write.dta()` function which we will use to save the data in Stata format.
+```r
+library(TwoSampleMR)
+library(MRInstruments)
+library(foreign)
+```
+
+Our edited version of the code starts by reading in some code to generate a set of plots in R.
+```r
+source("mrplots.R")
+```
+
+We can access the data using the **MRInstruments** package.
+```r
+data(gwas_catalog)
+
+# Get published SNPs for LDL cholesterol
+ldl_snps <- subset(gwas_catalog, grepl("LDL choles", Phenotype) & Author == "Willer CJ")$SNP
+
+# Extract from GLGC dataset
+exposure <- convert_outcome_to_exposure(extract_outcome_data(ldl_snps, "ieu-a-300"))
+
+# Get outcome data from Cardiogram 2015
+outcome <- extract_outcome_data(exposure$SNP, "ieu-a-7")
+
+# Harmonise exposure and outcome datasets
+# Assume alleles are on the forward strand
+dat <- harmonise_data(exposure, outcome, action=1)
+```
+At this point we have our harmonised genotype-exposure and genotype-outcome association data saved 
+in an object in our R session called `dat`.
+
+The next two code chunks perform the analysis in R.
+```r
+# Perform MR
+mr(dat)
+mr_heterogeneity(dat)
+dat$exposure <- "LDL cholesterol"
+dat$outcome <- "Coronary heart disease"
+
+# Label outliers and create plots
+dat$labels <- dat$SNP
+dat$labels[! dat$SNP %in% c("rs11065987", "rs1250229", "rs4530754")] <- NA
+```
+
+```r
+png("ldl-chd.png", width=1000, height=1000)
+mr_plots(dat)
+dev.off()
+```
+
+![Plots generated by the TwoSampleMR R package.](ldl-chd.png)
+
+We now save our `dat` object as a Stata dataset.
+```r
+write.dta(dat, file = "dat.dta")
+```
+
+## Performing the analysis using mrrobust in Stata
+
+We now switch from using R code chunks to Stata code chunks. We read the data into Stata and list 
+the variable names (note any `.` in the colnames of `dat` have been replaced with `_`).
+```s
+use dat, clear
+ds, v(28)
+di _N
+```
+
+We can then run the IVW model using `mregger` with multiplicative standard errors.
+```s
+mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw
+```
+
+It is helpful to view the forest plot of genotype specific IV estimates.
+```s
+mrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///
+    xlabel(-3,-2,-1,0,1,2,3)
+graph export ldl-chd-mrforest.svg, width(600) replace
+```
+
+![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)
+
+We can visualise this model with `mreggerplot`.
+```s
+mreggerplot beta_outcome se_outcome beta_exposure se_exposure
+graph export ldl-chd-mreggerplot.svg, width(600) replace
+```
+
+![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)
+
+We then fit the MR-Egger, median, and modal based estimators.
+```s
+mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]
+```
+
+```s
+mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted
+```
+
+```s
+mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted
+```
+

And we could continue with additional Stata code (or indeed R code) as we liked.

+

Note to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).

+
markstat using markstat-call-R-example
+
+
+

References

+ + + +
+
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/mrrobust-examples/index.html b/docs/examples/mrrobust-examples/index.html new file mode 100644 index 00000000..90c7802f --- /dev/null +++ b/docs/examples/mrrobust-examples/index.html @@ -0,0 +1,806 @@ + + + + + + + + + +mrrobust - Examples from helpfiles in the mrrobust package + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

Examples from helpfiles in the mrrobust package

+
+ + + +
+ + + + +
+ + +
+ +
+

Install the mrrobust package

+
net install github, from("https://haghish.github.io/github/")
+gitget mrrobust
+
+
+

Read in example data

+

Read in example data.

+
+
use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+
+

Select observations (p-value with exposure < 10-8).

+
+
gen byte sel1 = (ldlcp2 < 1e-8)
+
+
+
+

mrforest examples

+

Forest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the genotypes with their RSID.

+
+
mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) ///
+    xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)
+qui gr export mrforest.svg, width(600) replace
+
+
+
+
+
+

Example forest plot of genotype specific IV estimates.

+

Example forest plot of genotype specific IV estimates.

+
+
+
+
+
+
+

mregger examples

+

Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c “All genetic variants” estimates.

+

IVW (with fixed effect standard errors, i.e. variance of residuals [residual variance] constrained to 1).

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+
+
                                                      Number of genotypes = 73
+                                      Residual standard error constrained at 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173
+------------------------------------------------------------------------------
+
+
+

MR-Egger (with SEs using an unconstrained residual variance [multiplicative random effects]).

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.548
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858
+       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723
+------------------------------------------------------------------------------
+
+
+

MR-Egger reporting I2GX statistic and heterogeneity Q-test.

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi
+
+
                                           Q_GX statistic (weighted) = 3454.26
+                                         I^2_GX statistic (weighted) =  97.92%
+                                                      Number of genotypes = 73
+                                              Residual standard error =  1.548
+                Ruecker's Q for heterogeneity; chi2(71) = 170.11 (p =  0.0000)
+                             I-squared statistic = 58.3% (95% CI 45.8%, 67.8%)
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858
+       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723
+------------------------------------------------------------------------------
+
+
+

MR-Egger using a t-distribution for inference (p-values) & CI limits.

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.548
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+       slope |   .6173131   .1034573     5.97   0.000     .4110251    .8236012
+       _cons |  -.0087706   .0054812    -1.60   0.114    -.0196998    .0021585
+------------------------------------------------------------------------------
+
+
+

MR-Egger using the radial formulation.

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.547
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+radialGD     |
+    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205
+       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062
+------------------------------------------------------------------------------
+
+
+

MR-Egger using the radial formulation and reporting heterogeneity (Rucker’s) Q-test.

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.547
+                Ruecker's Q for heterogeneity; chi2(71) = 169.98 (p =  0.0000)
+                             I-squared statistic = 58.2% (95% CI 45.8%, 67.8%)
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+radialGD     |
+    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205
+       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062
+------------------------------------------------------------------------------
+
+
+
+
+

mreggersimex examples

+

SIMEX suppressing bootstrapped SEs (for speed - remove the noboot option to obtain SEs).

+
+
mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot
+qui gr export mreggersimex-plot.svg, width(600) replace
+
+
                                                      Number of genotypes = 73
+                                                    Bootstrap replications = 0
+                                                  Simulation replications = 50
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+       slope |   .6256194          .        .       .            .           .
+       _cons |  -.0089987          .        .       .            .           .
+------------------------------------------------------------------------------
+
+
+
+
+
+
+

SIMEX plots for the MR-Egger model.

+

SIMEX plots for the MR-Egger model.

+
+
+
+
+
+
+

mreggerplot examples

+
+
mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+qui gr export mreggerplot.svg, width(600) replace
+
+
+
+
+
+
+
+
+

Scatter plot showing the genotype summary level data points and the fitted MR-Egger model.

+

Scatter plot showing the genotype summary level data points and the fitted MR-Egger model.

+
+
+
+
+
+
+

mrmedian examples

+

Weighted median estimator.

+
+
mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4582573   .0633137     7.24   0.000     .3341648    .5823499
+------------------------------------------------------------------------------
+
+
+
+
+

mrmodal examples

+
+
mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+qui gr export mrmodalplot.svg, width(600) replace
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                     Phi = .25
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4198713   .2267297     1.85   0.064    -.0245107    .8642533
+------------------------------------------------------------------------------
+
+                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                      Phi = .5
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4218667   .1981169     2.13   0.033     .0335647    .8101688
+------------------------------------------------------------------------------
+
+                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4917353   .1356432     3.63   0.000     .2258796     .757591
+------------------------------------------------------------------------------
+
+
+
+
+
+
+

Densities of the IV estimates using different values of the phi parameter.

+

Densities of the IV estimates using different values of the phi parameter.

+
+
+
+
+

Simple mode estimator.

+
+
mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4917353   .1276278     3.85   0.000     .2415894    .7418812
+------------------------------------------------------------------------------
+
+
+

Weighted mode estimator.

+
+
mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441
+------------------------------------------------------------------------------
+
+
+

Simple mode estimator with NOME assumption.

+
+
mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4917353   .1266936     3.88   0.000     .2434204    .7400501
+------------------------------------------------------------------------------
+
+
+
+
+

mrfunnel examples

+
+
mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)
+qui gr export mrfunnel.svg, width(600) replace
+
+
+
+
+
+
+
+
+

Example funnel plot.

+

Example funnel plot.

+
+
+
+
+
+
+

Leave one out analysis

+

Create a smaller subset of data.

+
+
gen byte sel2 = (ldlcp2 < 1e-25)
+
+
+
+
+

Perform leave one out analysis using the IVW estimator

+
+
mrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid) noprint
+qui gr export mrleaveoneout-plot-01.svg, width(600) replace
+
+
+
+
+
+
+
+
+

Plot showing leave one (genotype) out at a time analysis for the IVW estimate.

+

Plot showing leave one (genotype) out at a time analysis for the IVW estimate.

+
+
+
+
+

Perform leave one out analysis using MVMR collecting the estimate for LDL-c.

+
+
mrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, ///
+    method(mvmr) gyse(chdse) genotype(rsid) noprint
+qui gr export mrleaveoneout-plot-02.svg, width(600) replace
+
+
+
+
+
+

Plot showing leave one (genotype) out at a time analysis for the MVMR estimate.

+

Plot showing leave one (genotype) out at a time analysis for the MVMR estimate.

+
+
+
+
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/mrrobust-examples/mreggerplot.svg b/docs/examples/mrrobust-examples/mreggerplot.svg new file mode 100644 index 00000000..5473009a --- /dev/null +++ b/docs/examples/mrrobust-examples/mreggerplot.svg @@ -0,0 +1,435 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -.1 + + 0 + + .1 + + .2 + + .3 + + .4 + Instrument-outcome associations + + + 0 + + .1 + + .2 + + .3 + + .4 + + .5 + Instrument-exposure associations + + + + + + + + + + + Instruments + 95% CIs + MR-Egger + MR-Egger 95% CI + diff --git a/docs/examples/mrrobust-examples/mreggersimex-plot.svg b/docs/examples/mrrobust-examples/mreggersimex-plot.svg new file mode 100644 index 00000000..ffe43b05 --- /dev/null +++ b/docs/examples/mrrobust-examples/mreggersimex-plot.svg @@ -0,0 +1,261 @@ + + + + + Stata Graph - mreggersimex + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + .59 + + .6 + + .61 + + .62 + + .63 + MR-Egger slope + + + -1 + + 0 + + 1 + + 2 + λ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -.009 + + -.0085 + + -.008 + + -.0075 + MR-Egger intercept + + + -1 + + 0 + + 1 + + 2 + λ + + + + + + + + + + + + + + SIMEX + Original + Simulated + Quadratic fit + Extrapolation + diff --git a/docs/examples/mrrobust-examples/mrforest.svg b/docs/examples/mrrobust-examples/mrforest.svg new file mode 100644 index 00000000..f730b2fd --- /dev/null +++ b/docs/examples/mrrobust-examples/mrforest.svg @@ -0,0 +1,503 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Genotypes + rs1169288 + rs17345563 + rs10790162 + rs579459 + rs10832962 + rs2980885 + rs1564348 + rs2247056 + rs1010167 + rs688 + rs2954022 + rs10401969 + rs11220462 + rs2297374 + rs4722551 + rs8176720 + rs3780181 + rs2288002 + rs6544713 + rs868943 + rs2710642 + rs217386 + rs9875338 + rs17508045 + rs2000999 + rs6511720 + rs4148218 + rs646776 + rs2642438 + rs7225700 + rs1883025 + rs515135 + rs6882076 + rs6065311 + rs7703051 + rs6016381 + rs2073547 + rs1800562 + rs314253 + rs1998013 + rs10102164 + rs10903129 + rs9989419 + rs6603981 + rs4240624 + rs1367117 + rs364585 + rs7254892 + rs174532 + rs6859 + rs267733 + rs1535 + rs492602 + rs4942486 + rs2587534 + rs2294261 + rs5763662 + rs2326077 + rs2328223 + rs12670798 + rs2255141 + rs2737252 + rs4530754 + rs16831243 + rs7832643 + rs2287623 + rs4587594 + rs1800961 + rs8017377 + rs903319 + rs1250229 + rs6489818 + rs653178 + Summary + IVW + MR-Egger + 1.84 (0.98, 2.71) + 1.83 (0.51, 3.16) + 1.71 (0.94, 2.48) + 1.46 (0.89, 2.03) + 1.38 (0.36, 2.39) + 1.32 (0.18, 2.46) + 1.27 (0.45, 2.09) + 1.20 (-0.10, 2.50) + 1.12 (-0.41, 2.65) + 1.04 (0.51, 1.57) + 1.02 (0.50, 1.53) + 0.92 (0.42, 1.41) + 0.90 (0.15, 1.64) + 0.88 (-0.05, 1.81) + 0.85 (-0.55, 2.24) + 0.82 (-0.05, 1.69) + 0.78 (-0.39, 1.94) + 0.76 (-0.19, 1.71) + 0.75 (0.35, 1.16) + 0.73 (-0.33, 1.79) + 0.71 (-0.54, 1.96) + 0.69 (-0.11, 1.50) + 0.67 (-0.39, 1.72) + 0.65 (-0.38, 1.69) + 0.62 (0.05, 1.18) + 0.59 (0.28, 0.90) + 0.59 (-0.38, 1.56) + 0.59 (0.37, 0.80) + 0.57 (-0.35, 1.49) + 0.50 (-0.51, 1.51) + 0.47 (-0.65, 1.58) + 0.46 (0.19, 0.73) + 0.46 (-0.17, 1.08) + 0.45 (-0.20, 1.10) + 0.45 (0.07, 0.84) + 0.44 (-0.35, 1.24) + 0.43 (-0.64, 1.50) + 0.42 (-0.52, 1.36) + 0.42 (-0.77, 1.61) + 0.39 (-0.10, 0.89) + 0.38 (-0.65, 1.40) + 0.36 (-0.45, 1.18) + 0.36 (-1.02, 1.73) + 0.35 (-0.63, 1.34) + 0.31 (-0.42, 1.05) + 0.29 (0.04, 0.54) + 0.29 (-0.82, 1.40) + 0.29 (-0.04, 0.62) + 0.28 (-1.00, 1.55) + 0.23 (-0.25, 0.70) + 0.08 (-1.19, 1.36) + 0.04 (-0.52, 0.60) + 0.03 (-1.04, 1.11) + 0.00 (-1.60, 1.60) + -0.02 (-0.66, 0.62) + -0.12 (-1.05, 0.82) + -0.13 (-1.43, 1.18) + -0.16 (-1.02, 0.69) + -0.18 (-2.01, 1.66) + -0.22 (-1.19, 0.75) + -0.25 (-1.29, 0.78) + -0.25 (-1.24, 0.73) + -0.27 (-1.24, 0.71) + -0.32 (-1.44, 0.81) + -0.32 (-1.15, 0.50) + -0.33 (-1.58, 0.91) + -0.35 (-0.95, 0.26) + -0.43 (-1.68, 0.81) + -0.60 (-1.89, 0.69) + -1.15 (-2.33, 0.04) + -1.42 (-3.01, 0.17) + -1.96 (-3.36, -0.57) + -3.35 (-5.11, -1.59) + 0.48 (0.41, 0.56) + 0.62 (0.41, 0.82) + Estimate (95% CI) + 1.84 (0.98, 2.71) + 1.83 (0.51, 3.16) + 1.71 (0.94, 2.48) + 1.46 (0.89, 2.03) + 1.38 (0.36, 2.39) + 1.32 (0.18, 2.46) + 1.27 (0.45, 2.09) + 1.20 (-0.10, 2.50) + 1.12 (-0.41, 2.65) + 1.04 (0.51, 1.57) + 1.02 (0.50, 1.53) + 0.92 (0.42, 1.41) + 0.90 (0.15, 1.64) + 0.88 (-0.05, 1.81) + 0.85 (-0.55, 2.24) + 0.82 (-0.05, 1.69) + 0.78 (-0.39, 1.94) + 0.76 (-0.19, 1.71) + 0.75 (0.35, 1.16) + 0.73 (-0.33, 1.79) + 0.71 (-0.54, 1.96) + 0.69 (-0.11, 1.50) + 0.67 (-0.39, 1.72) + 0.65 (-0.38, 1.69) + 0.62 (0.05, 1.18) + 0.59 (0.28, 0.90) + 0.59 (-0.38, 1.56) + 0.59 (0.37, 0.80) + 0.57 (-0.35, 1.49) + 0.50 (-0.51, 1.51) + 0.47 (-0.65, 1.58) + 0.46 (0.19, 0.73) + 0.46 (-0.17, 1.08) + 0.45 (-0.20, 1.10) + 0.45 (0.07, 0.84) + 0.44 (-0.35, 1.24) + 0.43 (-0.64, 1.50) + 0.42 (-0.52, 1.36) + 0.42 (-0.77, 1.61) + 0.39 (-0.10, 0.89) + 0.38 (-0.65, 1.40) + 0.36 (-0.45, 1.18) + 0.36 (-1.02, 1.73) + 0.35 (-0.63, 1.34) + 0.31 (-0.42, 1.05) + 0.29 (0.04, 0.54) + 0.29 (-0.82, 1.40) + 0.29 (-0.04, 0.62) + 0.28 (-1.00, 1.55) + 0.23 (-0.25, 0.70) + 0.08 (-1.19, 1.36) + 0.04 (-0.52, 0.60) + 0.03 (-1.04, 1.11) + 0.00 (-1.60, 1.60) + -0.02 (-0.66, 0.62) + -0.12 (-1.05, 0.82) + -0.13 (-1.43, 1.18) + -0.16 (-1.02, 0.69) + -0.18 (-2.01, 1.66) + -0.22 (-1.19, 0.75) + -0.25 (-1.29, 0.78) + -0.25 (-1.24, 0.73) + -0.27 (-1.24, 0.71) + -0.32 (-1.44, 0.81) + -0.32 (-1.15, 0.50) + -0.33 (-1.58, 0.91) + -0.35 (-0.95, 0.26) + -0.43 (-1.68, 0.81) + -0.60 (-1.89, 0.69) + -1.15 (-2.33, 0.04) + -1.42 (-3.01, 0.17) + -1.96 (-3.36, -0.57) + -3.35 (-5.11, -1.59) + 0.48 (0.41, 0.56) + 0.62 (0.41, 0.82) + Estimate (95% CI) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +   +   + + 0 + + -5 + + -4 + + -3 + + -2 + + -1 + + 0 + + 1 + + 2 + + 3 + + 4 + + 5 + + + I + GX + 2 + =97.9% + + diff --git a/docs/examples/mrrobust-examples/mrfunnel.svg b/docs/examples/mrrobust-examples/mrfunnel.svg new file mode 100644 index 00000000..2d9cf379 --- /dev/null +++ b/docs/examples/mrrobust-examples/mrfunnel.svg @@ -0,0 +1,691 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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a/docs/examples/mrrobust-examples/mrleaveoneout-plot-01.svg b/docs/examples/mrrobust-examples/mrleaveoneout-plot-01.svg new file mode 100644 index 00000000..b5462692 --- /dev/null +++ b/docs/examples/mrrobust-examples/mrleaveoneout-plot-01.svg @@ -0,0 +1,162 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Omitted genotype + rs1998013 + rs4587594 + rs646776 + rs2587534 + rs1367117 + rs515135 + rs6544713 + rs7703051 + rs6882076 + rs4240624 + rs2954022 + rs579459 + rs1535 + rs10790162 + rs2000999 + rs6511720 + rs688 + rs10401969 + rs6859 + rs7254892 + rs6065311 + All genotypes + IVW + rsid + 0.53 (0.44, 0.62) + 0.54 (0.45, 0.63) + 0.51 (0.41, 0.61) + 0.53 (0.45, 0.62) + 0.56 (0.46, 0.65) + 0.53 (0.44, 0.63) + 0.51 (0.42, 0.60) + 0.53 (0.44, 0.62) + 0.53 (0.44, 0.61) + 0.53 (0.44, 0.62) + 0.51 (0.42, 0.60) + 0.50 (0.41, 0.59) + 0.54 (0.45, 0.63) + 0.51 (0.42, 0.59) + 0.52 (0.43, 0.61) + 0.52 (0.43, 0.61) + 0.51 (0.42, 0.60) + 0.51 (0.42, 0.60) + 0.53 (0.45, 0.62) + 0.54 (0.45, 0.63) + 0.53 (0.44, 0.61) + 0.52 (0.44, 0.61) + Estimate (95% CI) + 0.53 (0.44, 0.62) + 0.54 (0.45, 0.63) + 0.51 (0.41, 0.61) + 0.53 (0.45, 0.62) + 0.56 (0.46, 0.65) + 0.53 (0.44, 0.63) + 0.51 (0.42, 0.60) + 0.53 (0.44, 0.62) + 0.53 (0.44, 0.61) + 0.53 (0.44, 0.62) + 0.51 (0.42, 0.60) + 0.50 (0.41, 0.59) + 0.54 (0.45, 0.63) + 0.51 (0.42, 0.59) + 0.52 (0.43, 0.61) + 0.52 (0.43, 0.61) + 0.51 (0.42, 0.60) + 0.51 (0.42, 0.60) + 0.53 (0.45, 0.62) + 0.54 (0.45, 0.63) + 0.53 (0.44, 0.61) + 0.52 (0.44, 0.61) + Estimate (95% CI) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +   +   + + 0 + + -.65 + + 0 + + .65 + + diff --git a/docs/examples/mrrobust-examples/mrleaveoneout-plot-02.svg b/docs/examples/mrrobust-examples/mrleaveoneout-plot-02.svg new file mode 100644 index 00000000..406bc250 --- /dev/null +++ b/docs/examples/mrrobust-examples/mrleaveoneout-plot-02.svg @@ -0,0 +1,162 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Omitted genotype + rs1998013 + rs4587594 + rs646776 + rs2587534 + rs1367117 + rs515135 + rs6544713 + rs7703051 + rs6882076 + rs4240624 + rs2954022 + rs579459 + rs1535 + rs10790162 + rs2000999 + rs6511720 + rs688 + rs10401969 + rs6859 + rs7254892 + rs6065311 + All genotypes + MVMR + rsid + 0.47 (0.32, 0.62) + 0.48 (0.35, 0.61) + 0.46 (0.29, 0.63) + 0.48 (0.33, 0.63) + 0.50 (0.36, 0.65) + 0.47 (0.32, 0.63) + 0.45 (0.30, 0.60) + 0.47 (0.31, 0.62) + 0.47 (0.32, 0.62) + 0.45 (0.30, 0.60) + 0.47 (0.32, 0.62) + 0.42 (0.31, 0.54) + 0.47 (0.31, 0.63) + 0.48 (0.33, 0.63) + 0.46 (0.31, 0.61) + 0.46 (0.30, 0.62) + 0.45 (0.31, 0.60) + 0.46 (0.31, 0.61) + 0.48 (0.33, 0.63) + 0.49 (0.33, 0.64) + 0.47 (0.32, 0.62) + 0.47 (0.32, 0.61) + Estimate (95% CI) + 0.47 (0.32, 0.62) + 0.48 (0.35, 0.61) + 0.46 (0.29, 0.63) + 0.48 (0.33, 0.63) + 0.50 (0.36, 0.65) + 0.47 (0.32, 0.63) + 0.45 (0.30, 0.60) + 0.47 (0.31, 0.62) + 0.47 (0.32, 0.62) + 0.45 (0.30, 0.60) + 0.47 (0.32, 0.62) + 0.42 (0.31, 0.54) + 0.47 (0.31, 0.63) + 0.48 (0.33, 0.63) + 0.46 (0.31, 0.61) + 0.46 (0.30, 0.62) + 0.45 (0.31, 0.60) + 0.46 (0.31, 0.61) + 0.48 (0.33, 0.63) + 0.49 (0.33, 0.64) + 0.47 (0.32, 0.62) + 0.47 (0.32, 0.61) + Estimate (95% CI) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +   +   + + 0 + + -.652 + + 0 + + .652 + + diff --git a/docs/examples/mrrobust-examples/mrmodalplot.svg b/docs/examples/mrrobust-examples/mrmodalplot.svg new file mode 100644 index 00000000..1a2d6c60 --- /dev/null +++ b/docs/examples/mrrobust-examples/mrmodalplot.svg @@ -0,0 +1,47 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + 0 + + .5 + + 1 + + 1.5 + Density + + + -4 + + -2 + + 0 + + 2 + + 4 + IV estimates + + + + + + φ = .25 + φ = .5 + φ = 1 + diff --git a/docs/examples/mvcommands-example/index.html b/docs/examples/mvcommands-example/index.html new file mode 100644 index 00000000..9a47ebc3 --- /dev/null +++ b/docs/examples/mvcommands-example/index.html @@ -0,0 +1,611 @@ + + + + + + + + + +mrrobust – index + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + +
+

Example demonstrating MVMR commands

+

Read in the Do et al. example dataset.

+
+
use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+
+
+
+
+

Select observations (p-value with LDL-C < 10-8)

+
+
gen byte sel1 = (ldlcp2 < 1e-8)
+
+
+
+
+
+

MV-IVW

+

Fit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).

+
+
mrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1
+
+
                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 2
+                                                Standard errors: Random effect
+                                              Residual standard error =  1.514
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .4670719   .0581901     8.03   0.000     .3530214    .5811224
+    hdlcbeta |  -.2930048   .1211822    -2.42   0.016    -.5305175   -.0554921
+------------------------------------------------------------------------------
+
+
+

Additionally include a third phenotype – triglycerides.

+
+
mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+
+
                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 3
+                                                Standard errors: Random effect
+                                              Residual standard error =  1.490
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113
+    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211
+      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755
+------------------------------------------------------------------------------
+
+
+

Report the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).

+
+
mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)
+
+
                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 3
+                                                Standard errors: Random effect
+                                              Residual standard error =  1.490
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |     .42862   .0609661     7.03   0.000     .3091286    .5481113
+    hdlcbeta |  -.1941989   .1308289    -1.48   0.138    -.4506189    .0622211
+      tgbeta |   .2260456   .1232828     1.83   0.067    -.0155842    .4676755
+------------------------------------------------------------------------------
+ Q_A statistic for instrument validity; chi2(70) = 152.88 (p =  0.0000)
+ Conditional F-statistics for instrument strength:
+ F_x1 = 130.31   (ldlcbeta)
+ F_x2 = 36.29    (hdlcbeta)
+ F_x3 = 40.44    (tgbeta)
+
+
+
+
+

MVMR-Egger

+

Fit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.

+
+
mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+
+
                                       MVMR-Egger model oriented wrt: ldlcbeta
+                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 3
+                                              Residual standard error =  1.469
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .5672993   .1002611     5.66   0.000      .370791    .7638075
+    hdlcbeta |  -.1364113   .1332727    -1.02   0.306    -.3976209    .1247983
+      tgbeta |   .2739803   .1246927     2.20   0.028     .0295871    .5183735
+       _cons |  -.0093655   .0054187    -1.73   0.084     -.019986     .001255
+------------------------------------------------------------------------------
+
+
+

We can also orient the model wrt HDL-C instead of LDL-C.

+
+
mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)
+
+
                                       MVMR-Egger model oriented wrt: hdlcbeta
+                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 3
+                                              Residual standard error =  1.501
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .4286398   .0614056     6.98   0.000      .308287    .5489926
+    hdlcbeta |  -.1989637   .1541909    -1.29   0.197    -.5011723    .1032449
+      tgbeta |   .2256794   .1243221     1.82   0.069    -.0179875    .4693463
+       _cons |   .0002155   .0036218     0.06   0.953     -.006883    .0073141
+------------------------------------------------------------------------------
+
+
+

Or we can orient the model wrt triglycerides instead of LDL-C.

+
+
mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)
+
+
                                         MVMR-Egger model oriented wrt: tgbeta
+                                                      Number of genotypes = 73
+                                                      Number of phenotypes = 3
+                                              Residual standard error =  1.499
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .4203073   .0660026     6.37   0.000     .2909447      .54967
+    hdlcbeta |  -.1903089   .1321536    -1.44   0.150    -.4493252    .0687075
+      tgbeta |   .2065651   .1365427     1.51   0.130    -.0610537     .474184
+       _cons |   .0013499    .003951     0.34   0.733    -.0063939    .0090936
+------------------------------------------------------------------------------
+
+
+
+
+

References

+
+
+Burgess, S, F Dudbridge, and SG Thompson. 2015. “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.” American Journal of Epidemiology 181 (4): 251–60. https://doi.org/10.1093/aje/kwu283. +
+
+Rees, J, A Wood, and S Burgess. 2017. “Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.” Statistics in Medicine 36 (29): 4705–18. https://doi.org/10.1002/sim.7492. +
+
+Sanderson, E, G Davey Smith, F Windmeijer, and J Bowden. 2019. “An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.” International Journal of Epidemiology 48 (3): 713–27. https://doi.org/10.1093/ije/dyy262. +
+
+Sanderson, E, W Spiller, and J Bowden. 2021. “Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.” Statistics in Medicine 40 (25): 5434–52. https://doi.org/10.1002/sim.9133. +
+
+ + +
+
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/rmarkdown-call-stata-example/index.html b/docs/examples/rmarkdown-call-stata-example/index.html new file mode 100644 index 00000000..7788a858 --- /dev/null +++ b/docs/examples/rmarkdown-call-stata-example/index.html @@ -0,0 +1,568 @@ + + + + + + + + + +mrrobust – index + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + +
+

How to include mrrobust Stata code in an R Markdown or Quarto document using the Statamarkdown R package

+
+

Introduction

+

This example shows how to run R and Stata code within the same R Markdown (.Rmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package and load it into your current R session run the following in R.

+
# install.packages("Statamarkdown") # uncomment on first run
+library(Statamarkdown)
+

Note when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.

+

Using R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base.

+

To see the R Markdown code which generates this page see here. It consists of R code chunks and Stata code chunks.

+

Next we install the other required packages in R. Note I don’t run these lines of code in this script because I already have these packages installed.

+
remotes::install_github("MRCIEU/TwoSampleMR") # uncomment on first run
+remotes::install_github("MRCIEU/MRInstruments") # uncomment on first run
+
+
+

Example R Markdown document

+
---
+title: Using mrrobust in an R Markdown or Quarto document
+---
+
+## Extracting data from MR-Base
+
+We will be running the script from the MR-Base paper ([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). The R code we will use is from [here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).
+
+We load the packages into our R session. Note that the **foreign** package provides the `write.dta()` function which we will use to save the data in Stata format.
+```{r}
+library(TwoSampleMR)
+library(MRInstruments)
+library(foreign)
+```
+
+
+We can access the data using the **MRInstruments** package.
+
+```{r}
+data(gwas_catalog)
+
+# Get published SNPs for LDL cholesterol
+ldl_snps <- subset(gwas_catalog, grepl("LDL choles", Phenotype) & Author == "Willer CJ")$SNP
+
+# Extract from GLGC dataset
+exposure <- convert_outcome_to_exposure(extract_outcome_data(ldl_snps, "ieu-a-300"))
+
+# Get outcome data from Cardiogram 2015
+outcome <- extract_outcome_data(exposure$SNP, "ieu-a-7")
+
+# Harmonise exposure and outcome datasets
+# Assume alleles are on the forward strand
+dat <- harmonise_data(exposure, outcome, action = 1)
+```
+
+At this point we have our harmonised genotype-exposure and genotype-outcome association data saved in an object in our R session called `dat`.
+
+The next two code chunks perform the analysis in R.
+
+```{r}
+# Perform MR analysis
+mr(dat)
+mr_heterogeneity(dat)
+dat$exposure <- "LDL cholesterol"
+dat$outcome <- "Coronary heart disease"
+
+# Label outliers and create plots
+dat$labels <- dat$SNP
+dat$labels[! dat$SNP %in% c("rs11065987", "rs1250229", "rs4530754")] <- NA
+```
+
+
+To proceed in Stata we can save our `dat` object as a Stata dataset
+
+```{r}
+write.dta(dat, file = "dat.dta")
+```
+
+
+## Analysis in Stata using the mrrobust package
+
+At this point in Stata install the **mrrobust** package and its dependencies if you have not done so previously.
+```stata
+net install github, from("https://haghish.github.io/github/")
+gitget mrrobust
+```
+
+We now read the dataset into Stata and look at the variable names and the number of observations.
+
+```{stata, collectcode=TRUE}
+qui use dat, clear
+```
+
+```{stata}
+ds, v(28)
+```
+
+```{stata}
+di _N
+```
+
+
+We can then run the IVW model using `mregger` with fixed effect standard errors.
+
+```{stata}
+mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe
+```
+
+
+We then fit the MR-Egger, median, and modal based estimators.
+
+```{stata}
+mregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]
+```
+
+```{stata}
+mrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted
+```
+
+```{stata}
+mrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted
+```
+
+
+And we could continue with additional Stata code (or indeed R code) as we like.
+

To run this R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run

+
rmarkdown::render('rmarkdown-call-stata-example.Rmd')
+
+
+

References

+ + + +
+
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/save-estimates/index.html b/docs/examples/save-estimates/index.html new file mode 100644 index 00000000..12db5c3f --- /dev/null +++ b/docs/examples/save-estimates/index.html @@ -0,0 +1,673 @@ + + + + + + + + + +mrrobust – index + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + +
+

How to collect and export results

+

This example shows how to conveniently save and export your estimates using the r(table) matrix that is now returned by each command.

+
+

Setup example data

+
+
use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+
+
+
+
+

Select observations (p-value with exposure < 10-8)

+
+
gen byte sel1 = (ldlcp2 < 1e-8)
+
+
+
+
+
+
+

Fit estimators - collecting results using r(table) matrix

+

IVW (with fixed effect standard errors)

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+mat ivw = r(table)
+
+
                                                      Number of genotypes = 73
+                                      Residual standard error constrained at 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+    ldlcbeta |   .4815055    .038221    12.60   0.000     .4065938    .5564173
+------------------------------------------------------------------------------
+
+
+

MR-Egger (with SEs using an unconstrained residual variance)

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1
+mat mregger = r(table)
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.548
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+chdbeta      |
+       slope |   .6173131   .1034573     5.97   0.000     .4145405    .8200858
+       _cons |  -.0087706   .0054812    -1.60   0.110    -.0195136    .0019723
+------------------------------------------------------------------------------
+
+
+

MR-Egger using the radial formulation

+
+
mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial
+mat radial = r(table)
+
+
                                                      Number of genotypes = 73
+                                              Residual standard error =  1.547
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+radialGD     |
+    radialGP |    .642582   .1157871     5.55   0.000     .4156434    .8695205
+       _cons |  -.5737301   .3545658    -1.62   0.106    -1.268666    .1212062
+------------------------------------------------------------------------------
+
+
+

Weighted mode estimator

+
+
mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+mat mode = r(table)
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4789702   .0663145     7.22   0.000     .3489963    .6089441
+------------------------------------------------------------------------------
+
+
+

Weighted median estimator

+
+
mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+mat median = r(table)
+
+
                                                      Number of genotypes = 73
+                                                           Replications = 1000
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .4582573   .0653722     7.01   0.000     .3301302    .5863845
+------------------------------------------------------------------------------
+
+
+
+
+

Combine and export results

+

Check our matrices

+
+
mat dir
+mat list ivw
+mat list mregger
+mat list radial
+mat list mode
+mat list median
+
+
       median[9,1]
+         mode[9,1]
+       radial[9,2]
+      mregger[9,2]
+          ivw[9,1]
+
+
+ivw[9,1]
+          chdbeta:
+         ldlcbeta
+     b  .48150551
+    se  .03822098
+     z  12.597937
+pvalue  2.167e-36
+    ll  .40659377
+    ul  .55641726
+    df          .
+  crit   1.959964
+ eform          0
+
+
+mregger[9,2]
+           chdbeta:    chdbeta:
+             slope       _cons
+     b   .61731315  -.00877065
+    se   .10345735   .00548118
+     z   5.9668371    -1.60014
+pvalue   2.419e-09   .10956752
+    ll   .41454047  -.01951356
+    ul   .82008582   .00197226
+    df           .           .
+  crit    1.959964    1.959964
+ eform           0           0
+
+
+radial[9,2]
+          radialGD:   radialGD:
+          radialGP       _cons
+     b   .64258196  -.57373006
+    se   .11578709   .35456584
+     z   5.5496858    -1.61812
+pvalue   2.862e-08   .10563675
+    ll   .41564344  -1.2686663
+    ul   .86952048   .12120621
+    df           .           .
+  crit    1.959964    1.959964
+ eform           0           0
+
+
+mode[9,1]
+             beta
+     b   .4789702
+    se  .06631445
+     z   7.222712
+pvalue  5.096e-13
+    ll  .34899626
+    ul  .60894415
+    df          .
+  crit   1.959964
+ eform          0
+
+
+median[9,1]
+             beta
+     b  .45825733
+    se   .0653722
+     z  7.0099725
+pvalue  2.384e-12
+    ll  .33013017
+    ul  .58638448
+    df          .
+  crit   1.959964
+ eform          0
+
+
+

Combined into single matrix

+
+
mat output = (ivw, mregger, radial, mode, median)
+mat colnames output = ivw_beta mregger_beta mregger_cons ///
+    radial_beta radial_cons mode_beta median_beta
+mat coleq output = "" "" "" "" "" "" ""
+mat output = output'
+mat list output, format(%4.3f)
+
+
output[7,9]
+                   b      se       z  pvalue      ll      ul      df    crit   eform
+    ivw_beta   0.482   0.038  12.598   0.000   0.407   0.556       .   1.960   0.000
+mregger_beta   0.617   0.103   5.967   0.000   0.415   0.820       .   1.960   0.000
+mregger_cons  -0.009   0.005  -1.600   0.110  -0.020   0.002       .   1.960   0.000
+ radial_beta   0.643   0.116   5.550   0.000   0.416   0.870       .   1.960   0.000
+ radial_cons  -0.574   0.355  -1.618   0.106  -1.269   0.121       .   1.960   0.000
+   mode_beta   0.479   0.066   7.223   0.000   0.349   0.609       .   1.960   0.000
+ median_beta   0.458   0.065   7.010   0.000   0.330   0.586       .   1.960   0.000
+
+
+

Export matrix to dataset

+
+
drop _all
+svmat output, names(col)
+local rownames : rownames output
+di "`rownames'"
+tokenize `rownames'
+gen str15 estimate = ""
+forvalues i = 1/7 {
+    replace estimate = "``i''" in `i'
+}
+
+

Show dataset

+
+
list estimate b se z pvalue ll ul, clean noobs
+
+
        estimate           b         se          z     pvalue          ll         ul  
+        ivw_beta    .4815055    .038221   12.59794   2.17e-36    .4065938   .5564172  
+    mregger_beta    .6173131   .1034573   5.966837   2.42e-09    .4145405   .8200858  
+    mregger_cons   -.0087707   .0054812   -1.60014   .1095675   -.0195136   .0019723  
+     radial_beta    .6425819   .1157871   5.549686   2.86e-08    .4156434   .8695205  
+     radial_cons   -.5737301   .3545658   -1.61812   .1056367   -1.268666   .1212062  
+       mode_beta    .4789702   .0663145   7.222712   5.10e-13    .3489963   .6089441  
+     median_beta    .4582573   .0653722   7.009973   2.38e-12    .3301302   .5863845
+
+
+

Save dataset

+
save myestimates, replace
+

Export as tab-delimited textfile

+
export delimited using myestimates.txt, delimiter(tab) replace
+ + +
+
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/spiller-ije-2018-examples/index.html b/docs/examples/spiller-ije-2018-examples/index.html new file mode 100644 index 00000000..03cff6fa --- /dev/null +++ b/docs/examples/spiller-ije-2018-examples/index.html @@ -0,0 +1,744 @@ + + + + + + + + + +mrrobust - Examples from our IJE paper + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

Examples from our IJE paper

+
+ + + +
+ + + + +
+ + +
+ +

The paper is available here.

+
+

mrrobust set-up

+

Install the mrrobust package using the user-written github package.

+
net install github, from("https://haghish.github.io/github/")
+gitget mrrobust
+

If you have Stata 12 or earlier you will need to install some of these manually (see here for instructions).

+
+
+

Summary data description and overview

+

Accompanying this paper are two sets of data BMI.csv, and Height.csv, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:

+
    +
  • SNP: A set of identifying numbers (rsids) for each genetic variant
    • +
  • beta.exposure: a set of values representing the coefficient from regressing the exposure upon the genetic variant within a GWAS
    • +
  • beta.outcome: a set of values representing the coefficient from regressing the outcome upon the genetic variant within a GWAS
    • +
  • se.exposure: a set of values representing the standard error corresponding to the coefficient in beta.exposure
    • +
  • se.outcome: a set of values representing the standard error corresponding to the coefficient in beta.outcome.
    • +
+

Note Stata removes the . in the variable names when the data is imported.

+

In BMI.csv the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by Locke et al. In Height.csv the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by Wood et al.

+

For both analyses log transformed serum glucose was used as an outcome, reported by Shin et al. All the data was obtained from the MRBase GWAS catalogue available at https://www.mrbase.org/. Genetic variants were pruned so as to be independent (\(R^2\) = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing mrrobust.

+
+
+

Stata output for each estimation method using mrrobust: BMI-Serum Glucose

+
+

Read in data

+
+
import delimited using BMI.csv, clear
+
+
+
+

IVW

+
+
mregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw
+
+
                                                      Number of genotypes = 79
+                                              Residual standard error =  1.039
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+betaoutcome  |
+betaexposure |   .0231866   .0079957     2.90   0.004     .0075154    .0388578
+------------------------------------------------------------------------------
+
+
+
+
+

MR-Egger

+
+
mregger betaoutcome betaexposure [aw=1/(seoutcome^2)]
+
+
                                                      Number of genotypes = 79
+                                              Residual standard error =  1.046
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+betaoutcome  |
+       slope |   .0218507   .0221852     0.98   0.325    -.0216315    .0653329
+       _cons |    .000038   .0005877     0.06   0.948    -.0011138    .0011897
+------------------------------------------------------------------------------
+
+
+
+
+

Plot of the MR-Egger model

+
+
mreggerplot betaoutcome seoutcome betaexposure seexposure
+qui gr export mreggerplot-bmi.svg, width(600) replace
+
+
+
+
+
+
+
+
+

Plot of the MR-Egger model for the BMI data.

+

Plot of the MR-Egger model for the BMI data.

+
+
+
+
+
+
+

Weighted median

+
+
mrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)
+
+
                                                      Number of genotypes = 79
+                                                           Replications = 1000
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0339256   .0120248     2.82   0.005     .0103576    .0574937
+------------------------------------------------------------------------------
+
+
+
+
+
+

Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose

+

Using the mrmodalplot command, modal estimates are calculated using bandwidths of 0.25, 0.5, and 1 respectively. This command also produces three overlaid density plots for each value, as shown in the Figure.

+
+
mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)
+qui gr export mrmodalplot-bmi.svg, width(600) replace
+
+
                                                      Number of genotypes = 79
+                                                           Replications = 1000
+                                                                     Phi = .25
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0374507   .0424036     0.88   0.377    -.0456588    .1205602
+------------------------------------------------------------------------------
+
+                                                      Number of genotypes = 79
+                                                           Replications = 1000
+                                                                      Phi = .5
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0416424   .0369758     1.13   0.260    -.0308289    .1141137
+------------------------------------------------------------------------------
+
+                                                      Number of genotypes = 79
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0431816   .0281684     1.53   0.125    -.0120274    .0983906
+------------------------------------------------------------------------------
+
+
+
+
+
+
+

Densities of the IV estimates using different values of phi.

+

Densities of the IV estimates using different values of phi.

+
+
+
+
+
+
+

Stata output for each estimation method using mrrobust: Height-Serum Glucose

+
+

Read in data

+
+
import delimited using Height.csv, clear
+
+
+
+

IVW

+
+
mregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw
+
+
                                                     Number of genotypes = 367
+                                              Residual standard error =  1.044
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+betaoutcome  |
+betaexposure |   .0015412   .0033017     0.47   0.641      -.00493    .0080124
+------------------------------------------------------------------------------
+
+
+
+
+

MR-Egger

+
+
mregger betaoutcome betaexposure [aw=1/(seoutcome^2)]
+
+
                                                     Number of genotypes = 367
+                                              Residual standard error =  1.045
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+betaoutcome  |
+       slope |  -.0025878   .0091178    -0.28   0.777    -.0204584    .0152828
+       _cons |   .0001338   .0002754     0.49   0.627     -.000406    .0006736
+------------------------------------------------------------------------------
+
+
+
+
+

Plot of the MR-Egger model

+
+
mreggerplot betaoutcome seoutcome betaexposure seexposure
+qui gr export mreggerplot-height.svg, width(600) replace
+
+
+
+
+
+
+
+
+

Plot of the MR-Egger model for the Height data.

+

Plot of the MR-Egger model for the Height data.

+
+
+
+
+
+
+

Weighted median

+
+
mrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)
+
+
                                                     Number of genotypes = 367
+                                                           Replications = 1000
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |          0   .0052323     0.00   1.000    -.0102551    .0102551
+------------------------------------------------------------------------------
+
+
+
+
+
+

Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose

+
+
mrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)
+qui gr export mrmodalplot-height.svg, width(600) replace
+
+
                                                     Number of genotypes = 367
+                                                           Replications = 1000
+                                                                     Phi = .25
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0061368   .0245472     0.25   0.803    -.0419748    .0542484
+------------------------------------------------------------------------------
+
+                                                     Number of genotypes = 367
+                                                           Replications = 1000
+                                                                      Phi = .5
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |   .0015595   .0212232     0.07   0.941    -.0400372    .0431561
+------------------------------------------------------------------------------
+
+                                                     Number of genotypes = 367
+                                                           Replications = 1000
+                                                                       Phi = 1
+------------------------------------------------------------------------------
+             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
+-------------+----------------------------------------------------------------
+        beta |  -.0054772   .0149074    -0.37   0.713    -.0346952    .0237408
+------------------------------------------------------------------------------
+
+
+
+
+
+
+

Densities of the IV estimates using different values of phi.

+

Densities of the IV estimates using different values of phi.

+
+
+
+
+
+
+

References

+
    +
  1. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206.
    2. +
  2. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46(11):1173-86.
    3. +
  3. Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543-50.
    4. +
  4. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408. https://doi.org/10.7554/eLife.34408
    5. +
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg b/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg new file mode 100644 index 00000000..e9ba7dae --- /dev/null +++ b/docs/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg @@ -0,0 +1,461 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -.01 + + -.005 + + 0 + + .005 + + .01 + + .015 + Instrument-outcome associations + + + .02 + + .04 + + .06 + + .08 + Instrument-exposure associations + + + + + + + + + + + Instruments + 95% CIs + MR-Egger + MR-Egger 95% CI + diff --git a/docs/examples/spiller-ije-2018-examples/mreggerplot-height.svg b/docs/examples/spiller-ije-2018-examples/mreggerplot-height.svg new file mode 100644 index 00000000..ada4975c --- /dev/null +++ b/docs/examples/spiller-ije-2018-examples/mreggerplot-height.svg @@ -0,0 +1,1902 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -.02 + + -.01 + + 0 + + .01 + + .02 + Instrument-outcome associations + + + .02 + + .04 + + .06 + + .08 + + .1 + + .12 + Instrument-exposure associations + + + + + + + + + + + Instruments + 95% CIs + MR-Egger + MR-Egger 95% CI + diff --git a/docs/examples/spiller-ije-2018-examples/mrmodalplot-bmi.svg b/docs/examples/spiller-ije-2018-examples/mrmodalplot-bmi.svg new file mode 100644 index 00000000..1cd4c663 --- /dev/null +++ b/docs/examples/spiller-ije-2018-examples/mrmodalplot-bmi.svg @@ -0,0 +1,49 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + 0 + + 2 + + 4 + + 6 + + 8 + Density + + + -.2 + + 0 + + .2 + + .4 + IV estimates + + + + + + φ = .25 + φ = .5 + φ = 1 + diff --git a/docs/examples/spiller-ije-2018-examples/mrmodalplot-height.svg b/docs/examples/spiller-ije-2018-examples/mrmodalplot-height.svg new file mode 100644 index 00000000..d49812b8 --- /dev/null +++ b/docs/examples/spiller-ije-2018-examples/mrmodalplot-height.svg @@ -0,0 +1,48 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + 0 + + 2 + + 4 + + 6 + Density + + + -.4 + + -.2 + + 0 + + .2 + + .4 + IV estimates + + + + + + φ = .25 + φ = .5 + φ = 1 + diff --git a/docs/helpfiles/mr-helpfile.html b/docs/helpfiles/mr-helpfile.html new file mode 100644 index 00000000..e6d206f9 --- /dev/null +++ b/docs/helpfiles/mr-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mr-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mr.html b/docs/helpfiles/mr.html new file mode 100644 index 00000000..8a92431b --- /dev/null +++ b/docs/helpfiles/mr.html @@ -0,0 +1,76 @@ + + + + + + + + +
+Title
+


+     mr -- Mendelian randomization programs
+


+Syntax
+


+        mr subcommand ... [aweight] [if] [in] [, options]
+


+Description
+


+    mr is a simple wrapper to the commands in the mrrobust package.
+


+    The subcommand is specified as the mrrobust program name without its mr prefix, i.e. mregger
+    ... can alternatively be run using the syntax mr egger ....
+


+Options
+


+    mr takes the options for the specified subcommand.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    IVW (with fixed effect standard errors)
+        . mr egger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+


+    Scatter plot of MR-Egger model
+        . mr eggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Multivariable IVW
+        . mr mvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+Stored results
+


+    mr returns the results from the specified subcommand.
+


+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrdeps-helpfile.html b/docs/helpfiles/mrdeps-helpfile.html new file mode 100644 index 00000000..1a5ea143 --- /dev/null +++ b/docs/helpfiles/mrdeps-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrdeps-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrdeps.html b/docs/helpfiles/mrdeps.html new file mode 100644 index 00000000..57e84d80 --- /dev/null +++ b/docs/helpfiles/mrdeps.html @@ -0,0 +1,40 @@ + + + + + + + + +
+Title
+


+     mrdeps -- Install dependencies for mrrobust package
+


+Syntax
+


+        mrdeps
+


+Description
+


+    mrdeps simply installs the dependencies for the mrrobust package. It has no options.
+


+Examples
+


+    Install dependencies
+        . mrdeps
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mregger-helpfile.html b/docs/helpfiles/mregger-helpfile.html new file mode 100644 index 00000000..4b65b5bb --- /dev/null +++ b/docs/helpfiles/mregger-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mregger-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mregger.html b/docs/helpfiles/mregger.html new file mode 100644 index 00000000..a14c53e4 --- /dev/null +++ b/docs/helpfiles/mregger.html @@ -0,0 +1,200 @@ + + + + + + + + +
+Title
+


+     mregger -- Mendelian randomization Egger regression
+


+Syntax
+


+        mregger varname_gd varname_gp [aweight] [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      fe                  Fixed effect standard errors (default is multiplicative)
+      gxse(varname)       variable of genotype-phenotype (SNP-exposure) SEs
+      heterogi            Display heterogeneity/pleiotropy statistics
+      ivw                 Inverse-variance weighted estimator (default is MR-Egger)
+      level(#)            set confidence level; default is level(95)
+      norescale           Do not rescale residual variance to be 1 (if less than 1)
+      oldnames            Revert to using longer outcome name in b and V ereturned matrices
+      penweighted         Use penalized weights
+      radial              Use radial formulations of the models
+      tdist               Use t-distribution for Wald test and CI limits
+      unwi2gx             Additionally report unweighted Q_GX and I^2_GX statistics
+


+Description
+


+    mregger performs inverse-variance weighted (IVW; Burgess et al., 2013) and Mendelian
+    randomization Egger (MR-Egger) regression (Bowden et al., 2015) using summary level data (i.e.
+    using genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure] association
+    estimates and their standard errors).
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp variable containing the genotype-phenotype (SNP-exposure) association estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    fe specifies fixed effect standard errors (i.e. variance of residuals constrained to 1 as in
+        fixed effect meta-analysis models). The default is to use multiplicative standard errors
+        (i.e. variance of residuals unconstrained as in standard linear regression), see Thompson
+        and Sharp (1999) for further details.  We recommend specifying this option when using an
+        allelic score as the instrumental variable.
+


+    gxse(varname) specifies the variable containing the genotype-phenotype (SNP-exposure)
+        association standard errors. These are required for calculating the I^2_GX statistic
+        (Bowden et al., 2016). An I^2_GX statistic of 90% means that the likely bias due
+        measurement error in the MR-Egger slope estimate is around 10%. If I^2_GX values are less
+        than 90% estimates should be treated with caution.
+


+    heterogi displays heterogeneity/pleiotropy statistics. In the heterogeneity output the model
+        based Q-statistic is reported by multiplying the variance of the residuals by the degrees
+        of freedom (Del Greco et al., 2015).  For the IVW model this is the Cochran Q-statistic,
+        and for the MR-Egger model this is the Ruecker's Q-statistic. The corresponding I-squared
+        statistic and its 95% CI is also reported.
+


+    ivw specifies inverse-variance weighted (IVW) model (Burgess et al., 2013), the default is
+        MR-Egger.
+


+    level(#); see [R] estimation options.
+


+    norescale specifies that the residual variance is not set to 1 (if it is found to be less than
+        1). Bowden et al. (2016) rescale the residual variance to be 1 if it is found to be less
+        than 1.
+


+    oldnames revert to using the longer outcome variable name in the b and V ereturned matrices.
+


+    penweighted specifies using penalized weights as described in Burgess et al. (2016).
+


+    radial specifies the radial formulation of the IVW and MR-Egger models (Bowden et al., 2017).
+        Note there is only a difference for the MR-Egger model.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+    unwi2gx specifies the unweighted Q_GX and I^2_GX statistics to be additionally displayed in
+        the output and in the ereturn scalars.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    IVW (with fixed effect standard errors)
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+


+    MR-Egger (with SEs using an unconstrained residual variance)
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1
+


+    MR-Egger reporting I^2_GX statistic and heterogeneity Q-test
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi
+


+    MR-Egger using a t-distribution for inference & CI limits
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist
+


+    MR-Egger using the radial formulation
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial
+


+    MR-Egger using the radial formulation and reporting heterogeneity Q-test
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi
+


+


+Stored results
+


+    mregger stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(k)                number of instruments
+      e(I2GX)             I^2_GX (with gxse() option)
+      e(QGX)              Q_GX (with gxse() option)
+      e(phi)              Scale parameter (root mean squared error)
+


+    Macros         
+      e(cmd)              mregger
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+    If heterogi is specified mregger additionally returns the r-class results of heterogi in the
+    e-class results.
+


+    If unwi2gx is specified mregger additionally returns
+      e(I2GXunw)          Unweighted I^2_GX statistic
+      e(QGXunw)           Unweighted Q_GX statistic
+


+    mregger stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Bowden J, Davey Smith G, Burgess S.  Mendelian randomization with invalid instruments: effect
+        estimation and bias detection through Egger regression. International Journal of
+        Epidemiology, 2015, 44, 2, 512-525.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Bowden J, Del Greco F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR.  Assessing the
+        suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger
+        regression: the role of the I-squared statistic. International Journal of Epidemiology,
+        2016, 45, 6, 1961-1974.  DOI
+


+    Bowden J, Spiller W, Del-Greco F, Sheehan NA, Thompson JR, Minelli C, Davey Smith G.
+        Improving the visualisation, interpretation and analysis of two-sample summary data
+        Mendelian randomization via the radial plot and radial regression.  International Journal
+        of Epidemiology, 2018, 47, 4, 1264-1278. DOI
+


+    Burgess S, Bowden J, Dudbridge F, Thompson SG. Robust instrumental variable methods using
+        candidate instruments with application to Mendelian randomization. arXiv:1606.03729v1,
+        2016.  Link
+


+    Burgess S, Butterworth A, Thompson S. Mendelian randomization analysis with multiple genetic
+        variants using summarized data. Genetic Epidemiology, 2013, 37, 7, 658-665.   DOI
+


+    Del Greco F M, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian
+        randomization studies with summary data and a continuous outcome.  Statistics in Medicine,
+        2015, 34, 21, 2926-2940.  DOI
+


+    Do R et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352. DOI:  DOI
+


+    Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods.
+        Statistics in Medicine, 1999, 18, 20, 2693-2708.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mreggerplot-helpfile.html b/docs/helpfiles/mreggerplot-helpfile.html new file mode 100644 index 00000000..0001045d --- /dev/null +++ b/docs/helpfiles/mreggerplot-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mreggerplot-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mreggerplot.html b/docs/helpfiles/mreggerplot.html new file mode 100644 index 00000000..a509a271 --- /dev/null +++ b/docs/helpfiles/mreggerplot.html @@ -0,0 +1,140 @@ + + + + + + + + +
+Title
+


+     mreggerplot -- Scatter plot for MR-Egger type models
+


+Syntax
+


+        mreggerplot varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      egger               MR-Egger estimator for fitted line
+      ellipses            marker confidence intervals as ellipses
+      errorbars           marker confidence intervals as capped lines
+      fe                  fixed effect standard errors for fitted line
+      gpci                CIs around G-P associations
+      ivw                 IVW line (default is MR-Egger)
+      legend(string)      legend options
+      level(#)            set confidence level; default is level(95)
+      linetop             draw fitted line (and CI) on top of points
+      mleglabel(string)   Label for data points in legend; default is Instruments
+      median              median estimator for fitted line
+      mlabel(string)      variable containing marker labels
+      nolci               do not plot confidence interval around fitted line
+      noline              do not plot fitted line
+      nomcis              do not plot confidence intervals around markers
+      penweighted         penalized weighted estimator
+      re                  random effect estimator for fitted line
+      recons              random intercept for fitted line
+      reslope             random slope for fitted line
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted median estimator
+      wmarkers            weighted markers
+      *                   Other options passed to the twoway plot
+


+Description
+


+    mreggerplot plots a scatter plot for MR-Egger type models
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Figure 4, LDL-c
+    "All genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Scatter plot of MR-Egger model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Scatter plot of MR-Egger model labelling outlying genotypes
+        . gen mlabvar = rsid if abs(ldlcbeta) > .3
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, mlab(mlabvar) mlabsize(vsmall)
+            mlabp(7) mlabc(gs0)
+


+    Scatter plot of MR-Egger model with genotype-phenotype CIs
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, gpci
+


+    Scatter plot of MR-Egger model specifying own titles and legend label
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, title(Investigating pleiotropy:
+            MR-Egger model fit) xtitle(Genotype-LDLC associations) ytitle(Genotype-CHD
+            associations) mleglabel(Genotypes)
+


+    Scatter plot of IVW model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ivw
+


+    Scatter plot of unweighted median model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, median
+


+    Scatter plot of MR-Egger model using ellipses around points
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ellipses
+


+    Scatter plot of MR-Egger model adding an IVW line (with legend entry) for comparison
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw
+        . addplot : function _b[ldlcbeta]*x if sel1==1, range(0 0.5) lc(gs0) lp(longdash)
+            lw(vthin) legend(order(5 "Instruments" 4 "95% CIs" 3 "MR-Egger" 2 "MR-Egger 95% CI" 6
+            "IVW") rows(1) size(vsmall))
+


+    Scatter plot of MR-Egger model adding weighted median and modal lines (with legend entries)
+    for comparison
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+        . addplot : function _b[beta]*x if sel1==1, range(0 0.5) lc(gs0) lp(shortdash) lw(vthin)
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, phi(.25)
+        . addplot : function _b[beta]*x if sel1==1, range(0 0.5) lc(gs0) lp(longdash)
+            legend(order(5 "Instruments" 4 "95% CIs" 3 "MR-Egger" 2 "MR-Egger 95% CI" 6 "Weighted
+            median" 7 "Modal") rows(1) si(vsmall) symx(*.5))
+


+    Scatter plot without any fitted lines
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ivw noline nolci gpci
+            legend(order(4 "Instruments" 3 "95% CI") size(vsmall) rows(1))
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al.Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mreggersimex-helpfile.html b/docs/helpfiles/mreggersimex-helpfile.html new file mode 100644 index 00000000..123c42aa --- /dev/null +++ b/docs/helpfiles/mreggersimex-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mreggersimex-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mreggersimex.html b/docs/helpfiles/mreggersimex.html new file mode 100644 index 00000000..b3613244 --- /dev/null +++ b/docs/helpfiles/mreggersimex.html @@ -0,0 +1,96 @@ + + + + + + + + +
+Title
+


+     mreggersimex -- SIMEX for the MR-Egger estimator
+


+Syntax
+


+        mreggersimex varname_gd varname_gp [aweight] [if] [in] [, options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      mreggeropts           Options for mregger
+      bsopts                Options for bootstrap
+      gxse(varname)         Variable containg genotype-phenotype (SNP-exposure) SEs
+      noboot                Do not perform bootstrapping for SEs
+      nodraw                Do not draw SIMEX plot
+      reps(#)               No. bootstrap replications
+      seed(#)               Seed for random number generator, specify for reproducible results
+      simreps(#)            No. simulation replications
+


+Description
+


+    mreggersimex performs the simulation extrapolation (SIMEX) algorithm (Cook et al., 1995,
+    Hardin et al., 2003) on mregger using the commonly applied quadratic extrapolation (Bowden et
+    al., 2016).
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp variable containing the genotype-phenotype (SNP-exposure) association estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    SEs squared, i.e. aw=1/(gdse^2).
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et el. (2016), Figure 4, LDL-c
+    "All genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    SIMEX using default settings
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345)
+


+    Suppressing the bootstrap SEs
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot
+


+    Suppressing the bootstrap SEs and the plot
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot
+            nodraw
+


+References
+


+    Bowden J, Del Greco FM, Minelli C, Davey Smith G, Sheehan NA, Thompson JR.  Assessing the
+        suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger
+        regression: the role of the I2 statistic.  International Journal of Epidemiology, 2016,
+        45, 6, 1961-1974.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Cook J and Stefanski LA. A simulation extrapolation method for parametric measurement error
+        models. Journal of the American Statistical Association, 1995, 85, 652-663.  Link
+


+    Do R et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352. DOI:  DOI
+


+    Hardin JW, Schmiediche H, Carroll RJ. The simulation extrapolation method for fitting linear
+        models with additive measurement error. Stata Journal.  2003, 3, 4, 373-385.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrforest-helpfile.html b/docs/helpfiles/mrforest-helpfile.html new file mode 100644 index 00000000..d0c27134 --- /dev/null +++ b/docs/helpfiles/mrforest-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrforest-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrforest.html b/docs/helpfiles/mrforest.html new file mode 100644 index 00000000..9b1b800d --- /dev/null +++ b/docs/helpfiles/mrforest.html @@ -0,0 +1,134 @@ + + + + + + + + +
+Title
+


+     mrforest -- Forest plot for MR-Egger type models
+


+Syntax
+


+        mrforest varname_gd varname_gdse varname_gp varname_gpse [varname_cov] [if] [in] [,
+              options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      astext(#)             Percentage of plot taken up by areas for text - some trial and error
+                              may be required
+      effect(string)        Label for statistics column; default is Estimate
+      ivid(varname)         Variable to label genotypes, usually containing RSIDs
+      ividlabel(string)     Label for genotypes; default is Genotypes
+      ivwlabel(string)      Label for IVW model; default is IVW
+      gsort(string)         how to sort the estimates, if specified must be one of; ascending,
+                              descending, or unsorted; default is ascending
+      level(#)              set confidence level; default is level(95)
+      mreggerlabel(string)  Label for MR-Egger model; default is MR-Egger
+      mrmedianlabel(string) Label for median model; default is Median
+      mrmodallabel(string)  Label for modal model; default is Modal
+      models(#)             number of models to show (1 IVW, 2 & MR-Egger, 3 & Median, 4 & Modal);
+                              default is 2
+      modelslabel(string)   Label for models; default is Summary
+      modelsonly            Only show model estimates on plot
+      nonote                Suppress note reporting I^2_GX
+      nostats               Suppress the statistics column
+      textsize(#)           Scaling factor for text on plot. I have tried to use sensible numbers
+                              here but some trial and error may be required
+      zcis                  Use normal distribution CI limits (for IVW and MR-Egger)
+


+      Options passed to other commands:
+      ivwopts(string)       options for IVW estimate from mregger, ivw fe
+      mreggeropts(string)   options for MR-Egger estimate from mregger
+      mrivestopts(string)   options for mrivests used to generate genotype specific ratio
+                              estimates and SEs
+      mrmedianopts(string)  options for median estimate from mrmedian
+      mrmodal(string)       options for modal estimate from mrmodal
+      nofe                  Remove fe option (fixed effect SE) from the IVW fit
+


+      *                     Other options passed to the metan
+


+Description
+


+    mrforest plots a forest plot for MR-Egger type models.  It is really a wrapper program for a
+    call to metan (Harris et al., 2008), which must be installed.
+


+    If you do not already have metan, it can be installed by running: ssc install metan.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+    varname_cov is a variable containing the covariances between the genotype-disease
+    (SNP-outcome) and genotype-phenotype (SNP-exposure) associations.
+


+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016) Figure 4, LDL-c "All
+    genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Forest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the
+    genotypes with their RSID.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid)
+


+    Having seen the first plot we can now define nicer x-axis labels.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid)
+            xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)
+


+    Removing the column of estimates from the plot, and sorting in descending order of the IV
+    estimates.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) nostats gsort(descending)
+


+    Showing all 4 models and modifying some labels.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) models(4) modelslabel(All
+            genotypes)
+


+    If you have data which require more than 2 decimal places, use metan's undocumented dp option,
+    e.g. for 4 decimal places specify dp(4)
+        . gen chdbeta2 = chdbeta / 100
+        . gen chdse2 = chdse / 100
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) dp(4)
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+    Harris RJ, Bradburn MJ, Deeks JJ, Harbord RM, Altman DG, Sterne JAC. metan: fixed- and
+        random-effects meta-analysis.  Stata Journal, 2008, 8, 1, 3-28. Link
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrfunnel-helpfile.html b/docs/helpfiles/mrfunnel-helpfile.html new file mode 100644 index 00000000..59226a83 --- /dev/null +++ b/docs/helpfiles/mrfunnel-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrfunnel-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrfunnel.html b/docs/helpfiles/mrfunnel.html new file mode 100644 index 00000000..e8801512 --- /dev/null +++ b/docs/helpfiles/mrfunnel.html @@ -0,0 +1,111 @@ + + + + + + + + +
+Title
+


+     mrfunnel -- Funnel plot for two-sample MR analysis
+


+Syntax
+


+        mrfunnel varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      extraplots(string)  extra plots to add to the overall plot
+      metric(metric)      scale of y-axis
+      noivw               do not plot IVW line
+      nomregger           do not plot MR-Egger line
+      mrivestsopts(opts)  options passed to mrivests
+      scatteropts(opts)   options passed to the scatter command
+      xlrange(# #)        the range for the IVW and MR-Egger lines, see twoway_function range()
+                            option
+      *                   other options passed to twoway
+


+Description
+


+    mrfunnel provides a funnel plot for a two-sample Mendelian randomization analysis.
+


+    There are 3 choices of measures of instrument strength to plot on the y-axis, which are
+    specified using the metric option and are described below.
+ 
+    On the plot the MR-Egger estimate is the line with the longer dashes, the IVW estimate is
+    shown with the shorter dashes.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    metric(gpbeta|gpbetastd|invse) specifies the metric for the y-axis. Can be one of:
+    - gpbeta: the absolute value of the genotype-phenotype (SNP-exposure) estimates,
+    - gpbetastd: gpbeta standardised by the genotype-disease (SNP-outcome) standard errors (the
+    default),
+    - invse: the inverse of the standard errors on the genotype specific IV ratio estimates.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016) Web Figure A2
+    (top-right plot, LDL-C with 73 genotypes).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Funnel plot
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Without adding the IVW and MR-Egger estimates
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, noivw nomregger
+


+    Using an unstandardised y-axis
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(gpbeta)
+


+    Using inverse IV SEs on the y-axis
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(invse)
+


+    Remove the legend
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, legend(off)
+


+    Extend the IVW and MR-Egger lines to the y-axis limits (as per the original version of this
+    command)
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrivests-helpfile.html b/docs/helpfiles/mrivests-helpfile.html new file mode 100644 index 00000000..7635fc6a --- /dev/null +++ b/docs/helpfiles/mrivests-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrivests-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrivests.html b/docs/helpfiles/mrivests.html new file mode 100644 index 00000000..5c2b642c --- /dev/null +++ b/docs/helpfiles/mrivests.html @@ -0,0 +1,91 @@ + + + + + + + + +
+Title
+


+     mrivests -- Generate genotype specific instrumental variable ratio (Wald) estimates in a
+dataset
+


+Syntax
+


+        mrivests varname_gd varname_gdse varname_gp [varname_gpse varname_cov] [if] [in] [,
+                options]
+


+    options                      Description
+    ----------------------------------------------------------------------------------------------
+      generate(varlist, replace) Variables to contain IV estimates and SEs or CI limits
+      *                          options passed to mrratio
+


+Description
+


+    mrivests calls mrratio to put the instrumental variable ratio (Wald) estimate and its standard
+    error in the variables specified.
+


+    varname_gd is a variable containing genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the standard errors of the genotype-disease
+    (SNP-outcome) association estimates.
+


+    varname_gp is a variable containing genotype-phenotype (SNP-exposure) association estimates.
+


+    varname_gpse is a variable containing the standard errors of the genotype-phenotype
+    (SNP-exposure) association estimates.
+


+    varname_cov is a variable containing the covariance between the genotype-disease (SNP-outcome)
+    and the genotype-phenotype (SNP-exposure) estimates.
+


+Options
+


+    generate(varlist, replace) specifies the variables (2 or 3) to contain the IV estimates and
+        their standard errors or confidence interval limits. Specifying replace replaces the
+        values in these variables if they already exist in the dataset.
+


+    * options passed through to mrratio, e.g. nome.
+


+Examples
+


+    Using the data provided by Do et al. (2013) generate genotype specific estimates for the LDL-c
+    phenotype.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Generate IV estimates in dataset
+        . mrivests chdbeta chdse ldlcbeta ldlcse if sel1==1, generate(ivest ivse)
+


+    Generate IV estimates with SEs assuming NOME
+        . mrivests chdbeta chdse ldlcbeta if sel1==1, generate(ivest ivse, replace) nome
+


+    Generate IV estimates with CI limits using Fieller's Theorem
+        . drop ivest
+        . mrivests chdbeta chdse ldlcbeta ldlcse if sel1==1, generate(ivest ivcilow ivciupp)
+            fieller
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrleaveoneout-helpfile.html b/docs/helpfiles/mrleaveoneout-helpfile.html new file mode 100644 index 00000000..3e4bc9d3 --- /dev/null +++ b/docs/helpfiles/mrleaveoneout-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrleaveoneout-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrleaveoneout.html b/docs/helpfiles/mrleaveoneout.html new file mode 100644 index 00000000..b625e8ed --- /dev/null +++ b/docs/helpfiles/mrleaveoneout.html @@ -0,0 +1,93 @@ + + + + + + + + +
+Title
+


+     mrleaveoneout -- Leave one (genotype) out (at a time) analysis
+


+Syntax
+


+        mrleaveoneout varname_gd varname_gp [if] [in], gyse(varname) [options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      astext(#)             Percentage of plot taken up by areas for text - some trial and error
+                              may be required
+      genotype(varname)     Variable to label genotypes, usually containing RSIDs
+      gxse(varname)         Variable with genotype-phenotype (SNP-exposure) standard errors (if
+                              required by method)
+      gyse(varname)         Variable with genotype-disease (SNP-outcome) standard errors
+      metanopts(string)     Options passed to metan for the plot
+      method(string)        The method fitted for the analysis
+      noplot                Suppresses the plot
+      noprint               Suppresses the display of each leave one out model
+      textsize(#)           Scaling factor for text on plot. I have tried to use sensible numbers
+                              here but some trial and error may be required
+      *                     Other options passed to the analysis method command
+


+Description
+


+    mrleaveoneout performs leave one out analysis, in which each genotype is omitted in turn from
+    the analysis.  A plot of the estimates is shown by default.
+


+    For multiple exposure models such as MVMR and MVMR-Egger the estimate is collected for the
+    first phenotype.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+Options
+


+    method(string) Specifies the method used for the analysis.  Must be one of ivw, egger,
+        mregger, mrivw, median, mrmedian, mrmodal, modal, mode, mvmr, mvivw, mvegger.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel2 = (ldlcp2 < 1e-25)
+


+    Perform leave one out analysis using the IVW estimator
+        . mrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid)
+


+    Leave one out analysis using MVMR (collecting the estimate for LDL-c)
+        . mrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, method(mvmr) gyse(chdse)
+            genotype(rsid)
+


+    If you have data which require more than 2 decimal places, use metan's undocumented dp option,
+    e.g. for 4 decimal places specify dp(4)
+        . gen chdbeta2 = chdbeta / 100
+        . gen chdse2 = chdse / 100
+        . mrleaveoneout chdbeta2 ldlcbeta if sel2==1, gyse(chdse2) genotype(rsid) metanopts(dp(4))
+


+References
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmedian-helpfile.html b/docs/helpfiles/mrmedian-helpfile.html new file mode 100644 index 00000000..a5ff21e1 --- /dev/null +++ b/docs/helpfiles/mrmedian-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmedian-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmedian.html b/docs/helpfiles/mrmedian.html new file mode 100644 index 00000000..c2ec2b2d --- /dev/null +++ b/docs/helpfiles/mrmedian.html @@ -0,0 +1,123 @@ + + + + + + + + +
+Title
+


+     mrmedian -- Weighted median of instrumental variable estimates
+


+Syntax
+


+        mrmedian varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      level(#)            set confidence level; default is level(95)
+      penweighted         penalized weighted estimator
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted estimator
+


+Description
+


+    mrmedian performs unweighted, weighted, and penalized median IV estimator on summary level
+    data (i.e. reported genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure]
+    association estimates and their standard errors for individual genotypes).
+


+    See Bowden et al. (2016) for more information.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    level(#); see [R] estimation options.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 1000 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted use weights.
+


+    penweighted use penalized weights.
+


+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" median estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Unweighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Unweighted median estimator with reproducible standard error
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Weighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+


+    Penalized weighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, penweighted
+


+Stored results
+


+    mrmedian stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmedian
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmedianobs-helpfile.html b/docs/helpfiles/mrmedianobs-helpfile.html new file mode 100644 index 00000000..cb910a61 --- /dev/null +++ b/docs/helpfiles/mrmedianobs-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmedianobs-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmedianobs.html b/docs/helpfiles/mrmedianobs.html new file mode 100644 index 00000000..da0495fb --- /dev/null +++ b/docs/helpfiles/mrmedianobs.html @@ -0,0 +1,116 @@ + + + + + + + + +
+Title
+


+     mrmedianobs -- Weighted median of instrumental variable estimates
+


+Syntax
+


+        mrmedianobs depvar [varlist1] (varname_endog = varlist_ivs) [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      all                 report percentile and bias corrected confidence intervals
+      level(#)            set confidence level; default is level(95)
+      obsboot             obtain standard error by bootstrapping at observation level
+      penweighted         penalized weighted estimator
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted estimator
+


+Description
+


+    mrmedianobs performs unweighted, weighted, and penalized IV estimator on observation level
+    data (Bowden et al., 2016).
+


+         depvar          outcome variable
+         varlist1        covariates to adjust for
+         varname_endog   exposure/treatment received/endogenous variable
+         varlist_ivs     instrumental variables
+


+Options
+


+    all report percentile and bias corrected bootstrap confidence interval limits (only applies to
+        observation level bootstrapping with obsboot).
+


+    level(#); see [R] estimation options.
+


+    obsboot obtain bootstrap standard error by bootstrapping at the observation level.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 50 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted use weights.
+


+    penweighted use penalized weights.
+


+


+Examples
+


+    Simulated test dataset.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/mrmedianobs_testdata, clear
+


+    Unweighted median estimator
+        . mrmedianobs y (x = z1-z20)
+


+    Weighted median estimator
+        . mrmedianobs y (x = z1-z20), weighted
+


+    Penalized weighted median estimator
+        . mrmedianobs y (x = z1-z20), penweighted
+


+    Unweighted median estimator with percentile CI limits of observation level bootstrapping
+        . mrmedianobs y (x = z1-z20), obsboot all
+


+


+Stored results
+


+    mrmedian stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(N)                number of observations
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmedianobs
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmodal-helpfile.html b/docs/helpfiles/mrmodal-helpfile.html new file mode 100644 index 00000000..af3a7caa --- /dev/null +++ b/docs/helpfiles/mrmodal-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmodal-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmodal.html b/docs/helpfiles/mrmodal.html new file mode 100644 index 00000000..e9c1445d --- /dev/null +++ b/docs/helpfiles/mrmodal.html @@ -0,0 +1,138 @@ + + + + + + + + +
+Title
+


+     mrmodal -- Modal estimator for summary data
+


+Syntax
+


+        mrmodal varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      level(#)            set confidence level; default is level(95)
+      nome                NOME assumption
+      nosave              Do not save density and vector of IV estimates in Mata
+      phi(#)              value of phi (for bandwidth)
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted IV estimates
+


+Description
+


+    mrmodal implements the zero modal estimator of Hartwig et al. (2017) for use with summary
+    level data (i.e. reported genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure]
+    association estimates and their standard errors for individual genotypes).
+


+    Standard errors are obtained by parametric bootstrapping.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    level(#); see [R] estimation options.
+


+    nome specifies the NOME (no measurement error in the genotype-phenotype [SNP-exposure]
+        associations) assumption.
+


+    nosave specifies that the density of the IV estimates and column vector of IV estimates should
+        not be saved in Mata. If not specified these are saved in Mata as mrmodal_densityiv and
+        mrmodal_g respectively.
+


+    phi(#) specifies the parameter phi which is used in the calculation of the bandwidth for the
+        density estimation. Default is phi = 1, other values commonly chosen are 0.25 and 0.5.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 1000 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted weight the instrumental variable estimates.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Investigate what is a good value of phi to use (we want a smooth density plot)
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Simple mode estimator
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Simple mode estimator with reproducible standard error
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Weighted mode estimator
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+


+    Simple mode estimator with NOME assumption
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome
+


+    Weighted mode estimator with NOME assumption
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted nome
+


+Stored results
+


+    mrmodal stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(phi)              value of phi
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmodal
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+    Hartwig FP, Davey Smith G, Bowden J.  Robust inference in two-sample Mendelian randomisation
+        via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46,
+        6, 1985-1998.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmodalplot-helpfile.html b/docs/helpfiles/mrmodalplot-helpfile.html new file mode 100644 index 00000000..73c8d36f --- /dev/null +++ b/docs/helpfiles/mrmodalplot-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmodalplot-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmodalplot.html b/docs/helpfiles/mrmodalplot.html new file mode 100644 index 00000000..18520e4a --- /dev/null +++ b/docs/helpfiles/mrmodalplot.html @@ -0,0 +1,89 @@ + + + + + + + + +
+Title
+


+     mrmodalplot -- Density plot to investigate values of phi in mrmodal
+


+Syntax
+


+        mrmodalplot varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      lc(optlist)         list of line colors
+      lp(optlist)         list of line patterns
+      lw(optlist)         list of line widths
+      nome                NOME assumption
+      phi(numlist)        value/s of phi (for bandwidth), default is .25 .5 1
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted IV estimates
+      *                   Other options passed to the twoway plot
+


+Description
+


+    mrmodalplot plots the density of the IV estimates used in the mrmodal estimator.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et (2016), Figure 4, LDL-c "All
+    genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Densities with phi=.25, .5, 1 and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Densities with phi=.4, .6, .8, 1 and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, phi(.4(.2)1) seed(12345)
+


+    Lines in grayscale and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, lc(gs10 gs5 gs0) seed(12345)
+


+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmvegger-helpfile.html b/docs/helpfiles/mrmvegger-helpfile.html new file mode 100644 index 00000000..57db82ed --- /dev/null +++ b/docs/helpfiles/mrmvegger-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmvegger-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmvegger.html b/docs/helpfiles/mrmvegger.html new file mode 100644 index 00000000..1b26be00 --- /dev/null +++ b/docs/helpfiles/mrmvegger.html @@ -0,0 +1,119 @@ + + + + + + + + +
+Title
+


+     mrmvegger -- Multivariable MR-Egger regression (MVMR-Egger)
+


+Syntax
+


+        mrmvegger varname_gd varname_gp1 [varname_gp2 ...] [aweight] [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      orient(#)           orient the data wrt to the phenotype which corresponds with the #th
+                            genotype-phenotype (SNP-exposure) association variable in the varlist
+                            (default is 1)
+      level(#)            set confidence level; default is level(95)
+      tdist               use t-distribution for Wald test and CI limits
+


+Description
+


+    mrmvegger performs multivariable MR-Egger regression.  For further information see Rees et al.
+    (2017).
+


+    By default multiplicative random effect standard errors are reported.  However, if the
+    residual variance is found to be less than 1 the model is refitted with this constrained to 1.
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp# variable containing the #th genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    orient(#) specifies which phenotype to orient the data to. The default is 1, i.e. the
+        phenotype in the first genotype-phenotype (SNP-exposure) variable in the varlist.
+


+    level(#); see [R] estimation options.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with LDL-C < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    MVMR-Egger regression
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+    Orient wrt HDL-C instead of LDL-C
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)
+


+    Orient wrt triglycerides instead of LDL-C
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)
+


+Stored results
+


+    mrmvegger stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(N)                Number of genotypes
+      e(Np)               Number of phenotypes
+      e(phi)              Scale parameter (root mean squared error)
+


+    Macros         
+      e(cmd)              Command name
+      e(cmdline)          Command issued
+      e(orientvar)        Genotype-phenotype (SNP-exposure) association variable the model is
+                            oriented wrt
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+    mrmvegger stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Rees J, Wood A, Burgess S. Extending the MR-Egger method for multivariable Mendelian
+        randomization to correct for both measured and unmeasured pleiotropy.  Statistics in
+        Medicine, 2017, 36, 29, 4705-4718.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrmvivw-helpfile.html b/docs/helpfiles/mrmvivw-helpfile.html new file mode 100644 index 00000000..997704f0 --- /dev/null +++ b/docs/helpfiles/mrmvivw-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrmvivw-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrmvivw.html b/docs/helpfiles/mrmvivw.html new file mode 100644 index 00000000..bc40e62e --- /dev/null +++ b/docs/helpfiles/mrmvivw.html @@ -0,0 +1,143 @@ + + + + + + + + +
+Title
+


+     mrmvivw/mvivw/mvmr -- Multivariable inverse variance weighted regression (MVIVW)
+


+Syntax
+


+        mrmvivw/mvivw/mvmr varname_gd varname_gp1 [varname_gp2 ...] [aweight] [if] [in] [,
+                options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      fe                  fixed effect standard errors (default is multiplicative random effect)
+      gxse(varlist)       varlist of genotype-phenotype (SNP-exposure) standard errors
+      level(#)            set confidence level; default is level(95)
+      tdist               use t-distribution for Wald test and CI limits
+


+Description
+


+    mrmvivw/mvivw/mvmr performs multivariable inverse-variance weighted (IVW) regression using
+    summary level data.  See  Burgess et al. (2015) for more information.
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp# variable containing the #th genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    fe specifies fixed effect standard errors (i.e. variance of residuals constrained to 1 as in
+        fixed effect meta-analysis models). The default is multiplicative random effect standard
+        errors in which case the variance of the residuals is unconstrained and the square root of
+        the estimated residual variance is displayed (Residual standard error). If the residual
+        variance is found to be less than 1 an error message is shown and the model is refitted
+        with it constrained to 1.
+


+    gxse(varlist) specifies a varlist of genotype-phenotype (SNP-exposure) standard errors.  These
+        should be in the same order as the genotype-phenotype (SNP-exposure) variables in the main
+        varlist.  When this option is specified the Q_A statistic for instrument validity is
+        calculated.  When this is specified and there are two or more phenotypes conditional F
+        statistics for instrument strength are calculated.  See Sanderson et al. (2019) and
+        Sanderson et al. (2021) for more information.
+


+    level(#); see [R] estimation options.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with LDL-C < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Fit MVMR/MVIVW
+        . mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+        . mvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+        . mvmr chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+    Report Q_A statistic and conditional F-statistics
+        . mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse
+            tgse)
+


+Stored results
+


+    mrmvivw/mvivw/mvmr stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(N)                Number of genotypes
+      e(Np)               Number of phenotypes
+      e(phi)              Scale parameter (root mean squared error)
+    If gxse() specified
+      e(Qa)               Q_A statistic
+      e(Qadf)             Degrees of freedom of Q_A statistic
+      e(Qap)              P-value for Q_A chi-squared test
+


+    Macros         
+      e(cmd)              Command name
+      e(cmdline)          Command issued
+      e(setype)           Standard error type
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+    If gxse() specified
+      e(Fx)               vector of conditional F-statistics
+      e(Qx)               vector of Q_x statistics
+


+    mrmvivw/mvivw/mvmr stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Burgess S, Dudbridge F, Thompson SG. Multivariable Mendelian randomization:  the use of
+        pleiotropic genetic variants to estimate causal effects.  American Journal of
+        Epidemiology, 2015, 181, 4, 251-260.  DOI
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian
+        randomization in the single-sample and two-sample summary data settings. International
+        Journal of Epidemiology, 2019, 48, 3, 713-727.  DOI
+


+    Sanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments
+        in two-sample multivariable Mendelian randomization.  Statistics in Medicine, 2021, 40,
+        25, 5434-5452.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrratio-helpfile.html b/docs/helpfiles/mrratio-helpfile.html new file mode 100644 index 00000000..bb79b7a3 --- /dev/null +++ b/docs/helpfiles/mrratio-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrratio-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrratio.html b/docs/helpfiles/mrratio.html new file mode 100644 index 00000000..425e0da9 --- /dev/null +++ b/docs/helpfiles/mrratio.html @@ -0,0 +1,114 @@ + + + + + + + + +
+Title
+


+     mrratio -- Instrumental variable ratio (Wald) estimator
+


+Syntax
+


+        mrratio #gd #gdse #gp [#gpse #cov] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      eform               exp() estimate and CI limits
+      fieller             CI using Fieller's Theorem
+      level(#)            set confidence level; default is level(95)
+      nome                NOME assumption
+


+Description
+


+    mrratio implements the standard instrumental variable ratio (Wald) estimator.
+


+    mrratio can generate confidence interval limits using either a standard error from a Taylor
+    series expansion (the default), a standard error using the NOME (no measurement error) in the
+    genotype-phenotype (SNP-exposure) association assumption, or using Fieller's Theorem.
+


+    The user needs to have summary level genotype-disease (SNP-outcome) and genotype-phenotype
+    (SNP-exposure) association estimates and their standard errors. If assuming NOME the standard
+    error of the genotype-phenotype (SNP-exposure) association is not required.
+


+    It is also optional to provide the covariance between the genotype-disease (SNP-outcome) and
+    genotype-phenotype (SNP-exposure) estimates. If these are from independent samples then this
+    covariance is zero.
+


+    #gd genotype-disease (SNP-outcome) association estimate.
+


+    #gdse standard error of the genotype-disease (SNP-outcome) association estimate.
+


+    #gp genotype-phenotype (SNP-exposure) association estimate.
+


+    #gpse standard error of the genotype-phenotype (SNP-exposure) association estimate.
+


+    #cov covariance between the genotype-disease (SNP-outcome) and the genotype-phenotype
+    (SNP-exposure) estimates.
+


+Options
+


+    eform specifies reporting the exponentiated ratio estimate and confidence interval limits.
+


+    fieller specifies deriving the confidence interval using Fieller's Theorem.
+


+    level(#); see [R] estimation options.
+


+    nome specifies the NOME (no measurement error in the genotype-phenotype [SNP-exposure]
+        association) assumption.
+


+Examples
+


+    Hypothetical example
+        . mrratio 1 .5 1 .25
+


+    With NOME assumption
+        . mrratio 1 .5 1 .25, nome
+


+    With NOME assumption
+        . mrratio 1 .5 1, nome
+


+    Exponentiate estimate and CI limits
+        . mrratio 1 .5 1 .25, eform
+


+    CI using Fieller's Theorem
+        . mrratio 1 .5 1 .25, fieller
+


+Stored results
+


+    mrratio stores the following in e():
+


+    Scalars        
+      e(fiellerres)       Result code from the Fieller CI: . fieller not specified; 1 closed CI; 2
+                            union of two intervals; 3 CI is whole real line
+      e(level)            level specified in first call to mrratio.
+


+    When fieller specified additional scalars:
+      e(lowerci)          lower CI limit
+      e(upperci)          upper CI limit
+


+    Macros         
+      e(cmd)              mrratio
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimate
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/docs/helpfiles/mrrobust-helpfile.html b/docs/helpfiles/mrrobust-helpfile.html new file mode 100644 index 00000000..c3b1e7e9 --- /dev/null +++ b/docs/helpfiles/mrrobust-helpfile.html @@ -0,0 +1,365 @@ + + + + + + + + + +mrrobust – mrrobust-helpfile + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ + + + + + + +
+ +
+ + + + \ No newline at end of file diff --git a/docs/helpfiles/mrrobust.html b/docs/helpfiles/mrrobust.html new file mode 100644 index 00000000..2e484a6b --- /dev/null +++ b/docs/helpfiles/mrrobust.html @@ -0,0 +1,81 @@ + + + + + + + + +
+Title
+


+    mrrobust -- commands for two-sample Mendelian randomization (MR) analyses.
+


+    https://remlapmot.github.io/mrrobust/
+


+Commands
+


+      mr            Primary command syntax, i.e. use mr egger ... syntax instead of mregger ...
+


+      mrdeps        Install dependencies for the package
+


+      mregger       MR-Egger and inverse-variance weighted (IVW) estimators
+


+      mreggersimex  Simulation extrapolation algorithm for the MR-Egger model
+


+      mreggerplot   Scatter plot showing instrument specific estimates with IVW, MR-Egger, or
+                      median fitted line and confidence interval
+


+      mrmedian      Unweighted, weighted, and penalized weighted median estimators for summary
+                      level data
+


+      mrmedianobs   Unweighted, weighted, and penalized weighted median estimators for individual
+                      level data
+


+      mrmodal       Modal estimator for summary level data
+


+      mrmodalplot   Plot of density used in the modal estimator
+


+      mrratio       Ratio (Wald) estimator for summary level data for a single genotype
+


+      mrivests      Generate ratio (Wald) estimates for summary level data in dataset
+


+      mrforest      Forest plot of genotype specific and model (IVW, MR-Egger, Median, Modal) IV
+                      estimates
+


+      mrfunnel      Funnel plot of the genotype specific IV estimates
+


+      mrmvivw       Multivariable inverse variance weighted estimator (also mvivw and mvmr)
+


+      mrmvegger     Multivariable MR-Egger regression
+


+      mrleaveoneout Leave one out analysis
+


+Description
+


+    mrrobust is a suite of programs implementing recently developed estimators which are robust to
+    certain proportions of invalid instrumental variables.
+


+    Most of the commands are designed to use summary level data as provided by repositories such
+    as MR-Base.
+


+    The estimators were developed in the context of MR studies but could be used for other
+    applications of instrumental variables.
+


+    There is a website showing the examples from the helpfiles here: 
+    https://remlapmot.github.io/mrrobust/
+


+Author
+


+INCLUDE help mrrobust-author
+
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+mrrobust + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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mrrobust: Stata package for two-sample Mendelian randomization analyses

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Latest updates

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To obtain the latest update please see the instructions below.

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  • April 2023: +
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    • Improved the alt text descriptions for the images in the README and package website, and also centred the images
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    • Remade the mrrobust website using Quarto
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    • Updated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi
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    • Reran certification scripts under latest Stata 17.0
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  • February 2023: +
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    • Updated R Markdown example to use the CRAN version of the Statamarkdown package
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  • September 2022: +
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    • Updated manual installation instructions
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  • February 2022: +
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    • Ran cscripts under Stata 17.0
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    • Updated website examples to run under Stata 17.0
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  • September 2021: +
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    • Changed relevant http: URLs to https:
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    • Minor edits to the helpfiles
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  • June 2021: +
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    • Published an interactive Code Ocean capsule demonstrating the use of the mrrobust package here
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    • By default mrforest now specifies a fixed effect standard error for its IVW estimate
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  • April 2021: +
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    • Added the I-squared statistic and its 95% CI to the mregger ..., heterogi output
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  • February 2021: +
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    • Fixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan
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    • Checked cscripts pass
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    • Checked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data
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    • Updated dependency.do to make it more robust to the more frequent updates to metan
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    • Updated mrdeps to make it more robust to the more frequent updates to metan
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  • October 2020: +
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    • dependency.do and mrdeps now install the updated version of the moremata package
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    • Checked the cscripts run under Stata 16.1
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    • Added description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively
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    • In various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate
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  • August 2020: +
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    • Added html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item
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    • Added extra decimal places examples to helpfiles of mrforest and mrleaveoneout
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    • mrfunnel now includes a legend on its plot
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  • July 2020: +
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    • Added gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)
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    • The gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)
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    • Added tdist option to mrmvivw and mrmvegger
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    • mrmvivw and mrmvegger now ereturn the RMSE in e(phi)
      • +
    • mregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)
      • +
    • Checked that examples on website still run
      • +
    • Added mrleaveoneout command to perform leave one out analysis
      • +
    • +
  • June 2020: +
      +
    • Simplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option
      • +
    • Added basic multivariable MR-Egger command, mrmvegger
      • +
    • Added basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)
      • +
    • +
  • February 2020: +
      +
    • Updated contact details
      • +
    • Minor edits to helpfiles, to show examples setting seed() option where helpful
      • +
    • Fixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.
      • +
    • Minor amendments to formatting of mregger gxse output
      • +
    • mregger now ereturns e(phi), the scale parameter, in some cases
      • +
    • +
  • January 2020: +
      +
    • mregger now additionally returns r(table)
      • +
    • Certification scripts: added master.do and renamed and edited a few scripts
      • +
    • Added mr command. Commands may now be run as either mr egger ... or as previously mregger ....
      • +
    • Best of IJE 2019!
      • +
    • mrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)
      • +
    • Added an example showing how you can save and export your estimates using r(table), see here
      • +
    • +
  • December 2019: +
      +
    • Added Q_GX to ereturn and display output when gxse() option specified to mregger
      • +
    • Changed Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.
      • +
    • +
  • July 2019: +
      +
    • Checked that examples on website still run
      • +
    • +
  • December 2018: +
      +
    • Improved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.
      • +
    • mrdeps command added for conveniently installing dependencies
      • +
    • +
  • November 2018: +
      +
    • Example showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here
      • +
    • Example showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here
      • +
    • +
  • September 2018: +
      +
    • IJE paper published online here
      • +
    • +
  • August 2018: +
      +
    • Click here for the example code and output from our IJE article
      • +
    • +
  • May 2018: +
      +
    • Click here for code and output from the examples in the helpfiles
      • +
    • This page is now rendered on GitHub Pages here
      • +
    • +
  • April 2018: +
      +
    • mregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw
      • +
    • +
+
+
+

Short video introduction

+

Click here for a short video demonstrating the use of the package.

+

+A screenshot of a video demonstrating the use of the mrrobust package. +

+
+
+

Helpfile examples

+

Click here for some of the code and output from the examples in the helpfiles.

+

Once the package is installed, there is a summary helpfile which can be viewed in Stata with:

+
help mrrobust
+

This has links to the helpfile for each command, which has an example near the bottom. In these examples you can click on the code to run it.

+
+
+

Overview

+

The mrrobust package is a collection of commands for performing two-sample Mendelian randomization analyses using summary data of genotype-phenotype and genotype-outcome associations.

+

Such data can be obtained from repositories such as MR-Base https://www.mrbase.org (Hemani et al. 2016).

+

The package contains the following commands:

+
    +
  • mrdeps installs dependencies for the package
    • +
  • mrratio implements the standard instrumental variable ratio (Wald) estimate with a choice of standard errors/confidence intervals
    • +
  • mrivests automates calling mrratio on all the selected genotypes in your dataset
    • +
  • mregger implements the IVW and MR-Egger regression approaches introduced in Bowden et al. (2015)
    • +
  • mreggersimex implements the simulation extrapolation algorithm for the MR-Egger model
    • +
  • mreggerplot implements a scatter plot with fitted line (either from IVW, MR-Egger, or weighted median estimators) and confidence interval
    • +
  • mrmedian and mrmedianobs implement the unweighted, weighted, and penalized weighted median IV estimators robust to 50% invalid instruments in Bowden et al. (2016)
    • +
  • mrmodal implements the zero modal estimator of Hartwig et al. (2017)
    • +
  • mrmodalplot plot of density used in modal estimator
    • +
  • mrforest implements a forest plot of genotype specific IV estimates and estimates from models (e.g. IVW and MR-Egger)
    • +
  • mrfunnel funnel plot of genotype specific IV estimates
    • +
  • mr acts as a primary command, e.g. so the other commands can be run as mr egger ... as well as mregger ...
    • +
  • mrmvivw (mvmr, mvivw) implements the multivariable IVW model
    • +
  • mrmvegger implements the multivariable MR-Egger model
    • +
  • mrleaveoneout implements leave one (genotype) out analysis
    • +
+
+
+

Installing and updating mrrobust

+

To install mrrobust in Stata versions 13 and later you have two choices.

+
+

1. Use net install

+
net install mrrobust, from("https://raw.github.com/remlapmot/mrrobust/master/") replace
+mrdeps
+

In this code mrdeps installs the dependencies. These are addplot, kdens, and moremata packages (all by Ben Jann), the heterogi command (Orsini et al.), the metan command (Harris et al.), and the grc1leg command (Wiggins).

+

If you have previously installed the package and the net install command above fails with an error message that there are two copies of the package installed simply run adoupdate.

+

To check if there is an update available to any of your user-written Stata packages run adoupdate. To update mrrobust run:

+
adoupdate mrrobust, update
+

To uninstall mrrobust, issue in Stata:

+
ado uninstall mrrobust
+

If this fails with an error message mentioning that you have “multiple citations/instances of the package installed” simply issue adoupdate mrrobust. This should leave you with the most recent version of the package you previously installed. You can then run ado uninstall mrrobust.

+
+
+

2. Use the github package

+
net install github, from("https://haghish.github.io/github/")
+gitget mrrobust
+

This automatically installs the dependencies.

+

To update the package issue:

+
github update mrrobust
+

To uninstall mrrobust issue:

+
github uninstall mrrobust
+
+
+

Installation instructions for Stata version 12 and earlier versions (and perhaps Stata version 13)

+

The net install syntax for installing mrrobust does not work under Stata version 12 and earlier because this webpage has an address starting with https rather than http. In such cases you need to do a manual installation.

+
+

To download and install mrrobust manually:

+
    +

  • Click the green “Clone or download” button at the top of the GitHub repository here and download as a zip archive or click this direct link.

    • +

  • In your file explorer extract the zip archive and find its filepath, e.g. C:\Users\tom\Downloads\mrrobust-master\mrrobust-master

    • +

  • In Stata run

    +
    net install mrrobust, from("C:\Users\tom\Downloads\mrrobust-master\mrrobust-master") replace
    • +
+

The installation commands for the other dependencies should work. However, if you need to install them manually their zip archives are available at the following links (extract the files from the downloaded zip archives and place them in your PERSONAL directory on your adopath):

+
    +

  • the moremata package is available as a zip file here

    • +

  • the addplot command is available here

    • +

  • the heterogi command is available here

    • +

  • the kdens command is available here

    • +

  • the metan command is available here

    • +

  • the grc1leg command can be installed with

    +
    net install grc1leg, from("https://www.stata.com/users/vwiggins")
    • +
+
+
+
+
+

Unit tests

+

As far as I know, and unlike R which has the testthat package (and other testing packages), there is no recognised standard for writing unit tests for Stata commands. StataCorp. refer to do-files with tests as cscripts (certification scripts). I publish my cscripts (and their log files of output) in the cscripts directory.

+
+
+

Authors

+

Tom Palmer , Wesley Spiller, Neil Davies

+
+
+

How to cite the mrrobust package

+

Spiller W, Davies NM, Palmer TM. Software Application Profile: mrrobust - A tool for performing two-sample summary Mendelian randomization analyses. International Journal of Epidemiology, 2019, 48, 3, 684-690. https://doi.org/10.1093/ije/dyy195

+

Thank you to all our users who have cited mrrobust. We made The Best of IJE 2019!

+
+
+

Collaboration

+

If you would like to extend the code or add new commands I am open to receiving pull requests on GitHub or send me an email to .

+
+
+

References

+
    +
  • Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015, 44, 2, 512-525. doi
    • +
  • Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 2016, 40, 4, 304-314. doi
    • +
  • Hartwig FP, Davey Smith G, Bowden J. Robust inference in two-sample Mendelian randomisation via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46, 6, 1985-1998. doi
    • +
  • Hemani G et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018, 7:e34408. doi
    • +
  • Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713-727. doi
    • +
  • Sanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Statistics in Medicine, 2021, 40, 25, 5434-5452. doi
    • +
+
+
+

Acknowledgements

+

Thanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, Michael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, Kawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison, Emma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess.

+ + + +
+
+ +
+ +
+ + + + \ No newline at end of file diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 00000000..0d471a24 --- /dev/null +++ b/docs/search.json @@ -0,0 +1,170 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "mrrobust", + "section": "", + "text": "Latest updates\nShort video introduction\nHelpfile examples\nOverview\nInstalling and updating mrrobust\nUnit tests\nAuthors\nHow to cite the mrrobust package\nReferences\nCollaboration\nAcknowledgements\n\n\n\nTo obtain the latest update please see the instructions below.\n\nApril 2023:\n\nImproved the alt text descriptions for the images in the README and package website, and also centred the images\nRemade the mrrobust website using Quarto\nUpdated the Sanderson et al., bioRxiv, 2020, doi reference to its published version Sanderson et al., Statistics in Medicine, 2021, doi\nReran certification scripts under latest Stata 17.0\n\nFebruary 2023:\n\nUpdated R Markdown example to use the CRAN version of the Statamarkdown package\n\nSeptember 2022:\n\nUpdated manual installation instructions\n\nFebruary 2022:\n\nRan cscripts under Stata 17.0\nUpdated website examples to run under Stata 17.0\n\nSeptember 2021:\n\nChanged relevant http: URLs to https:\nMinor edits to the helpfiles\n\nJune 2021:\n\nPublished an interactive Code Ocean capsule demonstrating the use of the mrrobust package here\nBy default mrforest now specifies a fixed effect standard error for its IVW estimate\n\nApril 2021:\n\nAdded the I-squared statistic and its 95% CI to the mregger ..., heterogi output\n\nFebruary 2021:\n\nFixes to mrforest and mrleaveoneout related to the recent update to metan. mrforest and mrleaveoneout now use metan9 instead of metan because of the changes to metan syntax. No change was necessary in the dependency scripts because metan9 is also installed with ssc install metan\nChecked cscripts pass\nChecked examples on website run. And changed the 2 examples which use the TwoSampleMR R package to use the new ID code for the exposure data\nUpdated dependency.do to make it more robust to the more frequent updates to metan\nUpdated mrdeps to make it more robust to the more frequent updates to metan\n\nOctober 2020:\n\ndependency.do and mrdeps now install the updated version of the moremata package\nChecked the cscripts run under Stata 16.1\nAdded description of Q-statistic as Cochran’s and Ruecker’s for the IVW and MR-Egger models respectively\nIn various helpfiles added clarification that genotype-disease stands for SNP-outcome (or indeed instrument-outcome) and that genotype-phenotype stands for SNP-exposure (or indeed instrument-exposure) respectively; i.e. the estimates required for the top and bottom of the IV Wald ratio estimate\n\nAugust 2020:\n\nAdded html versions of the helpfiles to the website. These are available from the Helpfiles website menu bar item\nAdded extra decimal places examples to helpfiles of mrforest and mrleaveoneout\nmrfunnel now includes a legend on its plot\n\nJuly 2020:\n\nAdded gxse() option to mrmvivw to return instrument strength QA statistic for instrument validity in e(Qa) (Sanderson et al. 2019)\nThe gxse() option additionally returns the Qx and conditional F-statistics for each phenotype for instrument strength in e(Qx) and e(Fx) (Sanderson et al. 2020)\nAdded tdist option to mrmvivw and mrmvegger\nmrmvivw and mrmvegger now ereturn the RMSE in e(phi)\nmregger, ivw now displays the square root of the residual variance (residual standard error) and ereturns this is e(phi)\nChecked that examples on website still run\nAdded mrleaveoneout command to perform leave one out analysis\n\nJune 2020:\n\nSimplified the outcome variable name in mregger b and V e-returned matrices. Turn this off with new oldnames option\nAdded basic multivariable MR-Egger command, mrmvegger\nAdded basic multivariable IVW command, mrmvivw (currently command names mvmr and mvivw also work)\n\nFebruary 2020:\n\nUpdated contact details\nMinor edits to helpfiles, to show examples setting seed() option where helpful\nFixed mregger bug where r(table) was not returned with the gxse or heterogi options. The output for these options now appears before the coefficient table.\nMinor amendments to formatting of mregger gxse output\nmregger now ereturns e(phi), the scale parameter, in some cases\n\nJanuary 2020:\n\nmregger now additionally returns r(table)\nCertification scripts: added master.do and renamed and edited a few scripts\nAdded mr command. Commands may now be run as either mr egger ... or as previously mregger ....\nBest of IJE 2019!\nmrmedian, mrmedianobs, mreggersimex, mrmodal, and mrratio now additionally return the r(table) matrix (the information from the coefficient table)\nAdded an example showing how you can save and export your estimates using r(table), see here\n\nDecember 2019:\n\nAdded Q_GX to ereturn and display output when gxse() option specified to mregger\nChanged Q_GX and I^2_GX output to use first order weights in mregger output. This matches the output from the mr_egger() function in the MendelianRandomization R package. Use the unwi2gx option to report the unweighted statistics.\n\nJuly 2019:\n\nChecked that examples on website still run\n\nDecember 2018:\n\nImproved compatibility with the github Stata package, i.e., mrrobust and its dependencies can be installed simply by issuing: gitget mrrobust, if you have the github package installed. See below for instructions.\nmrdeps command added for conveniently installing dependencies\n\nNovember 2018:\n\nExample showing the use of the TwoSampleMR R package and mrrobust in the same R Markdown script (.Rmd file) is here\nExample showing the use of the TwoSampleMR R package and mrrobust in the same Stata Markdown script (.stmd file) is here\n\nSeptember 2018:\n\nIJE paper published online here\n\nAugust 2018:\n\nClick here for the example code and output from our IJE article\n\nMay 2018:\n\nClick here for code and output from the examples in the helpfiles\nThis page is now rendered on GitHub Pages here\n\nApril 2018:\n\nmregger now has option radial which implements the radial formulation of the MR-Egger model, and of the IVW model when used with option ivw\n\n\n\n\n\nClick here for a short video demonstrating the use of the package.\n\n\n\n\n\n\nClick here for some of the code and output from the examples in the helpfiles.\nOnce the package is installed, there is a summary helpfile which can be viewed in Stata with:\nhelp mrrobust\nThis has links to the helpfile for each command, which has an example near the bottom. In these examples you can click on the code to run it.\n\n\n\nThe mrrobust package is a collection of commands for performing two-sample Mendelian randomization analyses using summary data of genotype-phenotype and genotype-outcome associations.\nSuch data can be obtained from repositories such as MR-Base https://www.mrbase.org (Hemani et al. 2016).\nThe package contains the following commands:\n\nmrdeps installs dependencies for the package\nmrratio implements the standard instrumental variable ratio (Wald) estimate with a choice of standard errors/confidence intervals\nmrivests automates calling mrratio on all the selected genotypes in your dataset\nmregger implements the IVW and MR-Egger regression approaches introduced in Bowden et al. (2015)\nmreggersimex implements the simulation extrapolation algorithm for the MR-Egger model\nmreggerplot implements a scatter plot with fitted line (either from IVW, MR-Egger, or weighted median estimators) and confidence interval\nmrmedian and mrmedianobs implement the unweighted, weighted, and penalized weighted median IV estimators robust to 50% invalid instruments in Bowden et al. (2016)\nmrmodal implements the zero modal estimator of Hartwig et al. (2017)\nmrmodalplot plot of density used in modal estimator\nmrforest implements a forest plot of genotype specific IV estimates and estimates from models (e.g. IVW and MR-Egger)\nmrfunnel funnel plot of genotype specific IV estimates\nmr acts as a primary command, e.g. so the other commands can be run as mr egger ... as well as mregger ...\nmrmvivw (mvmr, mvivw) implements the multivariable IVW model\nmrmvegger implements the multivariable MR-Egger model\nmrleaveoneout implements leave one (genotype) out analysis\n\n\n\n\nTo install mrrobust in Stata versions 13 and later you have two choices.\n\n\nnet install mrrobust, from(\"https://raw.github.com/remlapmot/mrrobust/master/\") replace\nmrdeps\nIn this code mrdeps installs the dependencies. These are addplot, kdens, and moremata packages (all by Ben Jann), the heterogi command (Orsini et al.), the metan command (Harris et al.), and the grc1leg command (Wiggins).\nIf you have previously installed the package and the net install command above fails with an error message that there are two copies of the package installed simply run adoupdate.\nTo check if there is an update available to any of your user-written Stata packages run adoupdate. To update mrrobust run:\nadoupdate mrrobust, update\nTo uninstall mrrobust, issue in Stata:\nado uninstall mrrobust\nIf this fails with an error message mentioning that you have “multiple citations/instances of the package installed” simply issue adoupdate mrrobust. This should leave you with the most recent version of the package you previously installed. You can then run ado uninstall mrrobust.\n\n\n\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nThis automatically installs the dependencies.\nTo update the package issue:\ngithub update mrrobust\nTo uninstall mrrobust issue:\ngithub uninstall mrrobust\n\n\n\nThe net install syntax for installing mrrobust does not work under Stata version 12 and earlier because this webpage has an address starting with https rather than http. In such cases you need to do a manual installation.\n\n\n\nClick the green “Clone or download” button at the top of the GitHub repository here and download as a zip archive or click this direct link.\nIn your file explorer extract the zip archive and find its filepath, e.g. C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\nIn Stata run\nnet install mrrobust, from(\"C:\\Users\\tom\\Downloads\\mrrobust-master\\mrrobust-master\") replace\n\nThe installation commands for the other dependencies should work. However, if you need to install them manually their zip archives are available at the following links (extract the files from the downloaded zip archives and place them in your PERSONAL directory on your adopath):\n\nthe moremata package is available as a zip file here\nthe addplot command is available here\nthe heterogi command is available here\nthe kdens command is available here\nthe metan command is available here\nthe grc1leg command can be installed with\nnet install grc1leg, from(\"https://www.stata.com/users/vwiggins\")\n\n\n\n\n\n\nAs far as I know, and unlike R which has the testthat package (and other testing packages), there is no recognised standard for writing unit tests for Stata commands. StataCorp. refer to do-files with tests as cscripts (certification scripts). I publish my cscripts (and their log files of output) in the cscripts directory.\n\n\n\nTom Palmer tom.palmer@bristol.ac.uk, Wesley Spiller, Neil Davies\n\n\n\nSpiller W, Davies NM, Palmer TM. Software Application Profile: mrrobust - A tool for performing two-sample summary Mendelian randomization analyses. International Journal of Epidemiology, 2019, 48, 3, 684-690. https://doi.org/10.1093/ije/dyy195\nThank you to all our users who have cited mrrobust. We made The Best of IJE 2019!\n\n\n\nIf you would like to extend the code or add new commands I am open to receiving pull requests on GitHub or send me an email to tom.palmer@bristol.ac.uk.\n\n\n\n\nBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015, 44, 2, 512-525. doi\nBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 2016, 40, 4, 304-314. doi\nHartwig FP, Davey Smith G, Bowden J. Robust inference in two-sample Mendelian randomisation via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46, 6, 1985-1998. doi\nHemani G et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018, 7:e34408. doi\nSanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713-727. doi\nSanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Statistics in Medicine, 2021, 40, 25, 5434-5452. doi\n\n\n\n\nThanks for helpful feedback and suggestions to (in no particular order): Jasmine Khouja, Michael Holmes, Caroline Dale, Amy Taylor, Rebecca Richmond, Judith Brand, Yanchun Bao, Kawthar Al-Dabhani, Michalis Katsoulis, Ghazaleh Fatemifar, Lai-Te Chen, Sean Harrison, Emma Anderson, Cassianne Robinson-Cohen, Alisa Kjaergaard, and Steve Burgess." + }, + { + "objectID": "about.html", + "href": "about.html", + "title": "About", + "section": "", + "text": "This is the website for the mrrobust Stata package\nGitHub repo: https://github.com/remlapmot/mrrobust" + }, + { + "objectID": "examples/rmarkdown-call-stata-example/index.html", + "href": "examples/rmarkdown-call-stata-example/index.html", + "title": "mrrobust", + "section": "", + "text": "This example shows how to run R and Stata code within the same R Markdown (.Rmd) file using the Statamarkdown R package. More information about this package is available here and here. To install this package and load it into your current R session run the following in R.\n# install.packages(\"Statamarkdown\") # uncomment on first run\nlibrary(Statamarkdown)\nNote when writing our Stata code chunks we need to be careful when we specify the collectcode=TRUE code chunk option, because each Stata code chunk is run as a separate batch job. For example, we include this chunk option in a chunk which reads in a dataset which we wish to use in subsequent chunks.\nUsing R and Stata code in the same script means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base.\nTo see the R Markdown code which generates this page see here. It consists of R code chunks and Stata code chunks.\nNext we install the other required packages in R. Note I don’t run these lines of code in this script because I already have these packages installed.\nremotes::install_github(\"MRCIEU/TwoSampleMR\") # uncomment on first run\nremotes::install_github(\"MRCIEU/MRInstruments\") # uncomment on first run\n\n\n\n---\ntitle: Using mrrobust in an R Markdown or Quarto document\n---\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper ([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). The R code we will use is from [here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nWe load the packages into our R session. Note that the **foreign** package provides the `write.dta()` function which we will use to save the data in Stata format.\n```{r}\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\n\nWe can access the data using the **MRInstruments** package.\n\n```{r}\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps <- subset(gwas_catalog, grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure <- convert_outcome_to_exposure(extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome <- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat <- harmonise_data(exposure, outcome, action = 1)\n```\n\nAt this point we have our harmonised genotype-exposure and genotype-outcome association data saved in an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n\n```{r}\n# Perform MR analysis\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure <- \"LDL cholesterol\"\ndat$outcome <- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels <- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] <- NA\n```\n\n\nTo proceed in Stata we can save our `dat` object as a Stata dataset\n\n```{r}\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n\n## Analysis in Stata using the mrrobust package\n\nAt this point in Stata install the **mrrobust** package and its dependencies if you have not done so previously.\n```stata\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\n```\n\nWe now read the dataset into Stata and look at the variable names and the number of observations.\n\n```{stata, collectcode=TRUE}\nqui use dat, clear\n```\n\n```{stata}\nds, v(28)\n```\n\n```{stata}\ndi _N\n```\n\n\nWe can then run the IVW model using `mregger` with fixed effect standard errors.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw fe\n```\n\n\nWe then fit the MR-Egger, median, and modal based estimators.\n\n```{stata}\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```{stata}\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```{stata}\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n\nAnd we could continue with additional Stata code (or indeed R code) as we like.\nTo run this R Markdown file, either; open it in RStudio and click the Knit button in the toolbar of the Source code window, or run\nrmarkdown::render('rmarkdown-call-stata-example.Rmd')\n\n\n\n\nHemani et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018;7:e34408 https://doi.org/10.7554/eLife.34408" + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html", + "href": "examples/spiller-ije-2018-examples/index.html", + "title": "Examples from our IJE paper", + "section": "", + "text": "The paper is available here." + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#mrrobust-set-up", + "href": "examples/spiller-ije-2018-examples/index.html#mrrobust-set-up", + "title": "Examples from our IJE paper", + "section": "mrrobust set-up", + "text": "mrrobust set-up\nInstall the mrrobust package using the user-written github package.\nnet install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust\nIf you have Stata 12 or earlier you will need to install some of these manually (see here for instructions)." + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#summary-data-description-and-overview", + "href": "examples/spiller-ije-2018-examples/index.html#summary-data-description-and-overview", + "title": "Examples from our IJE paper", + "section": "Summary data description and overview", + "text": "Summary data description and overview\nAccompanying this paper are two sets of data BMI.csv, and Height.csv, containing the set of summary estimates required for performing the BMI-serum glucose and height-serum glucose analyses respectively. Each dataset is organised into 5 columns under the following headings:\n\nSNP: A set of identifying numbers (rsids) for each genetic variant\nbeta.exposure: a set of values representing the coefficient from regressing the exposure upon the genetic variant within a GWAS\nbeta.outcome: a set of values representing the coefficient from regressing the outcome upon the genetic variant within a GWAS\nse.exposure: a set of values representing the standard error corresponding to the coefficient in beta.exposure\nse.outcome: a set of values representing the standard error corresponding to the coefficient in beta.outcome.\n\nNote Stata removes the . in the variable names when the data is imported.\nIn BMI.csv the exposure is standardised body mass index (BMI), and is therefore interpreted on a standard deviation scale. The summary statistics are reported by Locke et al. In Height.csv the exposure is standardised height in meters and also interpreted on a standard deviation scale. The summary statistics are reported by Wood et al.\nFor both analyses log transformed serum glucose was used as an outcome, reported by Shin et al. All the data was obtained from the MRBase GWAS catalogue available at https://www.mrbase.org/. Genetic variants were pruned so as to be independent (\\(R^2\\) = 0.0001), and the effect alleles were aligned between the exposure and outcome datasets using the MRBase web application, prior to implementing mrrobust." + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#estimation", + "href": "examples/spiller-ije-2018-examples/index.html#estimation", + "title": "Examples from our IJE paper", + "section": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose", + "text": "Stata output for each estimation method using mrrobust: BMI-Serum Glucose\n\nRead in data\n\nimport delimited using BMI.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n Number of genotypes = 79\n Residual standard error = 1.039\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome |\nbetaexposure | .0231866 .0079957 2.90 0.004 .0075154 .0388578\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n Number of genotypes = 79\n Residual standard error = 1.046\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome |\n slope | .0218507 .0221852 0.98 0.325 -.0216315 .0653329\n _cons | .000038 .0005877 0.06 0.948 -.0011138 .0011897\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-bmi.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot of the MR-Egger model for the BMI data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n Number of genotypes = 79\n Replications = 1000\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0339256 .0120248 2.82 0.005 .0103576 .0574937\n------------------------------------------------------------------------------" + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#mode", + "href": "examples/spiller-ije-2018-examples/index.html#mode", + "title": "Examples from our IJE paper", + "section": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose", + "text": "Stata output using the mode-based estimator using mrrobust: BMI-Serum Glucose\nUsing the mrmodalplot command, modal estimates are calculated using bandwidths of 0.25, 0.5, and 1 respectively. This command also produces three overlaid density plots for each value, as shown in the Figure.\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-bmi.svg, width(600) replace\n\n Number of genotypes = 79\n Replications = 1000\n Phi = .25\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0374507 .0424036 0.88 0.377 -.0456588 .1205602\n------------------------------------------------------------------------------\n\n Number of genotypes = 79\n Replications = 1000\n Phi = .5\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0416424 .0369758 1.13 0.260 -.0308289 .1141137\n------------------------------------------------------------------------------\n\n Number of genotypes = 79\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0431816 .0281684 1.53 0.125 -.0120274 .0983906\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi." + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#height", + "href": "examples/spiller-ije-2018-examples/index.html#height", + "title": "Examples from our IJE paper", + "section": "Stata output for each estimation method using mrrobust: Height-Serum Glucose", + "text": "Stata output for each estimation method using mrrobust: Height-Serum Glucose\n\nRead in data\n\nimport delimited using Height.csv, clear\n\n\n\nIVW\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)], ivw\n\n Number of genotypes = 367\n Residual standard error = 1.044\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome |\nbetaexposure | .0015412 .0033017 0.47 0.641 -.00493 .0080124\n------------------------------------------------------------------------------\n\n\n\n\nMR-Egger\n\nmregger betaoutcome betaexposure [aw=1/(seoutcome^2)]\n\n Number of genotypes = 367\n Residual standard error = 1.045\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nbetaoutcome |\n slope | -.0025878 .0091178 -0.28 0.777 -.0204584 .0152828\n _cons | .0001338 .0002754 0.49 0.627 -.000406 .0006736\n------------------------------------------------------------------------------\n\n\n\n\nPlot of the MR-Egger model\n\nmreggerplot betaoutcome seoutcome betaexposure seexposure\nqui gr export mreggerplot-height.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot of the MR-Egger model for the Height data.\n\n\n\n\n\n\nWeighted median\n\nmrmedian betaoutcome seoutcome betaexposure seexposure, weighted seed(300818)\n\n Number of genotypes = 367\n Replications = 1000\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | 0 .0052323 0.00 1.000 -.0102551 .0102551\n------------------------------------------------------------------------------" + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#height-mode", + "href": "examples/spiller-ije-2018-examples/index.html#height-mode", + "title": "Examples from our IJE paper", + "section": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose", + "text": "Stata output using the mode-based estimator using mrrobust: Height-Serum Glucose\n\nmrmodalplot betaoutcome seoutcome betaexposure seexposure, lc(gs10 gs5 gs0) seed(300818)\nqui gr export mrmodalplot-height.svg, width(600) replace\n\n Number of genotypes = 367\n Replications = 1000\n Phi = .25\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0061368 .0245472 0.25 0.803 -.0419748 .0542484\n------------------------------------------------------------------------------\n\n Number of genotypes = 367\n Replications = 1000\n Phi = .5\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .0015595 .0212232 0.07 0.941 -.0400372 .0431561\n------------------------------------------------------------------------------\n\n Number of genotypes = 367\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | -.0054772 .0149074 -0.37 0.713 -.0346952 .0237408\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of phi." + }, + { + "objectID": "examples/spiller-ije-2018-examples/index.html#references", + "href": "examples/spiller-ije-2018-examples/index.html#references", + "title": "Examples from our IJE paper", + "section": "References", + "text": "References\n\nLocke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206.\nWood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46(11):1173-86.\nShin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543-50.\nHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408. https://doi.org/10.7554/eLife.34408" + }, + { + "objectID": "examples/mrrobust-examples/index.html", + "href": "examples/mrrobust-examples/index.html", + "title": "Examples from helpfiles in the mrrobust package", + "section": "", + "text": "net install github, from(\"https://haghish.github.io/github/\")\ngitget mrrobust" + }, + { + "objectID": "examples/mrrobust-examples/index.html#read-in-example-data", + "href": "examples/mrrobust-examples/index.html#read-in-example-data", + "title": "Examples from helpfiles in the mrrobust package", + "section": "Read in example data", + "text": "Read in example data\nRead in example data.\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\nSelect observations (p-value with exposure < 10-8).\n\ngen byte sel1 = (ldlcp2 < 1e-8)" + }, + { + "objectID": "examples/mrrobust-examples/index.html#mrforest-examples", + "href": "examples/mrrobust-examples/index.html#mrforest-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mrforest examples", + "text": "mrforest examples\nForest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the genotypes with their RSID.\n\nmrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) ///\n xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)\nqui gr export mrforest.svg, width(600) replace\n\n\n\n\n\n\nExample forest plot of genotype specific IV estimates." + }, + { + "objectID": "examples/mrrobust-examples/index.html#mregger-examples", + "href": "examples/mrrobust-examples/index.html#mregger-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mregger examples", + "text": "mregger examples\nUsing the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c “All genetic variants” estimates.\nIVW (with fixed effect standard errors, i.e. variance of residuals [residual variance] constrained to 1).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\n\n Number of genotypes = 73\n Residual standard error constrained at 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .4815055 .038221 12.60 0.000 .4065938 .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance [multiplicative random effects]).\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\n\n Number of genotypes = 73\n Residual standard error = 1.548\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n slope | .6173131 .1034573 5.97 0.000 .4145405 .8200858\n _cons | -.0087706 .0054812 -1.60 0.110 -.0195136 .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger reporting I2GX statistic and heterogeneity Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi\n\n Q_GX statistic (weighted) = 3454.26\n I^2_GX statistic (weighted) = 97.92%\n Number of genotypes = 73\n Residual standard error = 1.548\n Ruecker's Q for heterogeneity; chi2(71) = 170.11 (p = 0.0000)\n I-squared statistic = 58.3% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n slope | .6173131 .1034573 5.97 0.000 .4145405 .8200858\n _cons | -.0087706 .0054812 -1.60 0.110 -.0195136 .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using a t-distribution for inference (p-values) & CI limits.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist\n\n Number of genotypes = 73\n Residual standard error = 1.548\n------------------------------------------------------------------------------\n | Coefficient Std. err. t P>|t| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n slope | .6173131 .1034573 5.97 0.000 .4110251 .8236012\n _cons | -.0087706 .0054812 -1.60 0.114 -.0196998 .0021585\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\n\n Number of genotypes = 73\n Residual standard error = 1.547\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD |\n radialGP | .642582 .1157871 5.55 0.000 .4156434 .8695205\n _cons | -.5737301 .3545658 -1.62 0.106 -1.268666 .1212062\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation and reporting heterogeneity (Rucker’s) Q-test.\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi\n\n Number of genotypes = 73\n Residual standard error = 1.547\n Ruecker's Q for heterogeneity; chi2(71) = 169.98 (p = 0.0000)\n I-squared statistic = 58.2% (95% CI 45.8%, 67.8%)\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD |\n radialGP | .642582 .1157871 5.55 0.000 .4156434 .8695205\n _cons | -.5737301 .3545658 -1.62 0.106 -1.268666 .1212062\n------------------------------------------------------------------------------" + }, + { + "objectID": "examples/mrrobust-examples/index.html#mreggersimex-examples", + "href": "examples/mrrobust-examples/index.html#mreggersimex-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mreggersimex examples", + "text": "mreggersimex examples\nSIMEX suppressing bootstrapped SEs (for speed - remove the noboot option to obtain SEs).\n\nmreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot\nqui gr export mreggersimex-plot.svg, width(600) replace\n\n Number of genotypes = 73\n Bootstrap replications = 0\n Simulation replications = 50\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n slope | .6256194 . . . . .\n _cons | -.0089987 . . . . .\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nSIMEX plots for the MR-Egger model." + }, + { + "objectID": "examples/mrrobust-examples/index.html#mreggerplot-examples", + "href": "examples/mrrobust-examples/index.html#mreggerplot-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mreggerplot examples", + "text": "mreggerplot examples\n\nmreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1\nqui gr export mreggerplot.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nScatter plot showing the genotype summary level data points and the fitted MR-Egger model." + }, + { + "objectID": "examples/mrrobust-examples/index.html#mrmedian-examples", + "href": "examples/mrrobust-examples/index.html#mrmedian-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mrmedian examples", + "text": "mrmedian examples\nWeighted median estimator.\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n Number of genotypes = 73\n Replications = 1000\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4582573 .0633137 7.24 0.000 .3341648 .5823499\n------------------------------------------------------------------------------" + }, + { + "objectID": "examples/mrrobust-examples/index.html#mrmodal-examples", + "href": "examples/mrrobust-examples/index.html#mrmodal-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mrmodal examples", + "text": "mrmodal examples\n\nmrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)\nqui gr export mrmodalplot.svg, width(600) replace\n\n Number of genotypes = 73\n Replications = 1000\n Phi = .25\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4198713 .2267297 1.85 0.064 -.0245107 .8642533\n------------------------------------------------------------------------------\n\n Number of genotypes = 73\n Replications = 1000\n Phi = .5\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4218667 .1981169 2.13 0.033 .0335647 .8101688\n------------------------------------------------------------------------------\n\n Number of genotypes = 73\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4917353 .1356432 3.63 0.000 .2258796 .757591\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nDensities of the IV estimates using different values of the phi parameter.\n\n\n\n\nSimple mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1\n\n Number of genotypes = 73\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4917353 .1276278 3.85 0.000 .2415894 .7418812\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\n\n Number of genotypes = 73\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4789702 .0663145 7.22 0.000 .3489963 .6089441\n------------------------------------------------------------------------------\n\n\nSimple mode estimator with NOME assumption.\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome\n\n Number of genotypes = 73\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4917353 .1266936 3.88 0.000 .2434204 .7400501\n------------------------------------------------------------------------------" + }, + { + "objectID": "examples/mrrobust-examples/index.html#mrfunnel-examples", + "href": "examples/mrrobust-examples/index.html#mrfunnel-examples", + "title": "Examples from helpfiles in the mrrobust package", + "section": "mrfunnel examples", + "text": "mrfunnel examples\n\nmrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)\nqui gr export mrfunnel.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nExample funnel plot." + }, + { + "objectID": "examples/mrrobust-examples/index.html#leave-one-out-analysis", + "href": "examples/mrrobust-examples/index.html#leave-one-out-analysis", + "title": "Examples from helpfiles in the mrrobust package", + "section": "Leave one out analysis", + "text": "Leave one out analysis\nCreate a smaller subset of data.\n\ngen byte sel2 = (ldlcp2 < 1e-25)\n\n\n\n\nPerform leave one out analysis using the IVW estimator\n\nmrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-01.svg, width(600) replace\n\n\n\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the IVW estimate.\n\n\n\n\nPerform leave one out analysis using MVMR collecting the estimate for LDL-c.\n\nmrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, ///\n method(mvmr) gyse(chdse) genotype(rsid) noprint\nqui gr export mrleaveoneout-plot-02.svg, width(600) replace\n\n\n\n\n\n\nPlot showing leave one (genotype) out at a time analysis for the MVMR estimate." + }, + { + "objectID": "examples/mvcommands-example/index.html", + "href": "examples/mvcommands-example/index.html", + "title": "mrrobust", + "section": "", + "text": "Read in the Do et al. example dataset.\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\n\n\n\nSelect observations (p-value with LDL-C < 10-8)\n\ngen byte sel1 = (ldlcp2 < 1e-8)\n\n\n\n\n\n\nFit the multivariable inverse-variance weighted (MV-IVW a.k.a. multivariable Mendelian randomization, MVMR) estimator with phenotypes LDL-c and HDL-c (Burgess, Dudbridge, and Thompson 2015).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta [aw=1/(chdse^2)] if sel1==1\n\n Number of genotypes = 73\n Number of phenotypes = 2\n Standard errors: Random effect\n Residual standard error = 1.514\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .4670719 .0581901 8.03 0.000 .3530214 .5811224\n hdlcbeta | -.2930048 .1211822 -2.42 0.016 -.5305175 -.0554921\n------------------------------------------------------------------------------\n\n\nAdditionally include a third phenotype – triglycerides.\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n Number of genotypes = 73\n Number of phenotypes = 3\n Standard errors: Random effect\n Residual standard error = 1.490\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .42862 .0609661 7.03 0.000 .3091286 .5481113\n hdlcbeta | -.1941989 .1308289 -1.48 0.138 -.4506189 .0622211\n tgbeta | .2260456 .1232828 1.83 0.067 -.0155842 .4676755\n------------------------------------------------------------------------------\n\n\nReport the QA statistic for instrument validity and the conditional F-statistics for instrument strength for each phenotype (Sanderson et al. 2019; Sanderson, Spiller, and Bowden 2021).\n\nmrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse tgse)\n\n Number of genotypes = 73\n Number of phenotypes = 3\n Standard errors: Random effect\n Residual standard error = 1.490\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .42862 .0609661 7.03 0.000 .3091286 .5481113\n hdlcbeta | -.1941989 .1308289 -1.48 0.138 -.4506189 .0622211\n tgbeta | .2260456 .1232828 1.83 0.067 -.0155842 .4676755\n------------------------------------------------------------------------------\n Q_A statistic for instrument validity; chi2(70) = 152.88 (p = 0.0000)\n Conditional F-statistics for instrument strength:\n F_x1 = 130.31 (ldlcbeta)\n F_x2 = 36.29 (hdlcbeta)\n F_x3 = 40.44 (tgbeta)\n\n\n\n\n\nFit MVMR-Egger regression (Rees, Wood, and Burgess 2017), by default orienting the model to the first phenotype in the main varlist.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1\n\n MVMR-Egger model oriented wrt: ldlcbeta\n Number of genotypes = 73\n Number of phenotypes = 3\n Residual standard error = 1.469\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .5672993 .1002611 5.66 0.000 .370791 .7638075\n hdlcbeta | -.1364113 .1332727 -1.02 0.306 -.3976209 .1247983\n tgbeta | .2739803 .1246927 2.20 0.028 .0295871 .5183735\n _cons | -.0093655 .0054187 -1.73 0.084 -.019986 .001255\n------------------------------------------------------------------------------\n\n\nWe can also orient the model wrt HDL-C instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)\n\n MVMR-Egger model oriented wrt: hdlcbeta\n Number of genotypes = 73\n Number of phenotypes = 3\n Residual standard error = 1.501\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .4286398 .0614056 6.98 0.000 .308287 .5489926\n hdlcbeta | -.1989637 .1541909 -1.29 0.197 -.5011723 .1032449\n tgbeta | .2256794 .1243221 1.82 0.069 -.0179875 .4693463\n _cons | .0002155 .0036218 0.06 0.953 -.006883 .0073141\n------------------------------------------------------------------------------\n\n\nOr we can orient the model wrt triglycerides instead of LDL-C.\n\nmrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)\n\n MVMR-Egger model oriented wrt: tgbeta\n Number of genotypes = 73\n Number of phenotypes = 3\n Residual standard error = 1.499\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .4203073 .0660026 6.37 0.000 .2909447 .54967\n hdlcbeta | -.1903089 .1321536 -1.44 0.150 -.4493252 .0687075\n tgbeta | .2065651 .1365427 1.51 0.130 -.0610537 .474184\n _cons | .0013499 .003951 0.34 0.733 -.0063939 .0090936\n------------------------------------------------------------------------------\n\n\n\n\n\n\n\nBurgess, S, F Dudbridge, and SG Thompson. 2015. “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.” American Journal of Epidemiology 181 (4): 251–60. https://doi.org/10.1093/aje/kwu283.\n\n\nRees, J, A Wood, and S Burgess. 2017. “Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.” Statistics in Medicine 36 (29): 4705–18. https://doi.org/10.1002/sim.7492.\n\n\nSanderson, E, G Davey Smith, F Windmeijer, and J Bowden. 2019. “An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.” International Journal of Epidemiology 48 (3): 713–27. https://doi.org/10.1093/ije/dyy262.\n\n\nSanderson, E, W Spiller, and J Bowden. 2021. “Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.” Statistics in Medicine 40 (25): 5434–52. https://doi.org/10.1002/sim.9133." + }, + { + "objectID": "examples/save-estimates/index.html", + "href": "examples/save-estimates/index.html", + "title": "mrrobust", + "section": "", + "text": "This example shows how to conveniently save and export your estimates using the r(table) matrix that is now returned by each command.\n\n\n\nuse https://raw.github.com/remlapmot/mrrobust/master/dodata, clear\n\n\n\n\nSelect observations (p-value with exposure < 10-8)\n\ngen byte sel1 = (ldlcp2 < 1e-8)\n\n\n\n\n\n\n\nIVW (with fixed effect standard errors)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe\nmat ivw = r(table)\n\n Number of genotypes = 73\n Residual standard error constrained at 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n ldlcbeta | .4815055 .038221 12.60 0.000 .4065938 .5564173\n------------------------------------------------------------------------------\n\n\nMR-Egger (with SEs using an unconstrained residual variance)\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1\nmat mregger = r(table)\n\n Number of genotypes = 73\n Residual standard error = 1.548\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nchdbeta |\n slope | .6173131 .1034573 5.97 0.000 .4145405 .8200858\n _cons | -.0087706 .0054812 -1.60 0.110 -.0195136 .0019723\n------------------------------------------------------------------------------\n\n\nMR-Egger using the radial formulation\n\nmregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial\nmat radial = r(table)\n\n Number of genotypes = 73\n Residual standard error = 1.547\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\nradialGD |\n radialGP | .642582 .1157871 5.55 0.000 .4156434 .8695205\n _cons | -.5737301 .3545658 -1.62 0.106 -1.268666 .1212062\n------------------------------------------------------------------------------\n\n\nWeighted mode estimator\n\nmrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat mode = r(table)\n\n Number of genotypes = 73\n Replications = 1000\n Phi = 1\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4789702 .0663145 7.22 0.000 .3489963 .6089441\n------------------------------------------------------------------------------\n\n\nWeighted median estimator\n\nmrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted\nmat median = r(table)\n\n Number of genotypes = 73\n Replications = 1000\n------------------------------------------------------------------------------\n | Coefficient Std. err. z P>|z| [95% conf. interval]\n-------------+----------------------------------------------------------------\n beta | .4582573 .0653722 7.01 0.000 .3301302 .5863845\n------------------------------------------------------------------------------\n\n\n\n\n\nCheck our matrices\n\nmat dir\nmat list ivw\nmat list mregger\nmat list radial\nmat list mode\nmat list median\n\n median[9,1]\n mode[9,1]\n radial[9,2]\n mregger[9,2]\n ivw[9,1]\n\n\nivw[9,1]\n chdbeta:\n ldlcbeta\n b .48150551\n se .03822098\n z 12.597937\npvalue 2.167e-36\n ll .40659377\n ul .55641726\n df .\n crit 1.959964\n eform 0\n\n\nmregger[9,2]\n chdbeta: chdbeta:\n slope _cons\n b .61731315 -.00877065\n se .10345735 .00548118\n z 5.9668371 -1.60014\npvalue 2.419e-09 .10956752\n ll .41454047 -.01951356\n ul .82008582 .00197226\n df . .\n crit 1.959964 1.959964\n eform 0 0\n\n\nradial[9,2]\n radialGD: radialGD:\n radialGP _cons\n b .64258196 -.57373006\n se .11578709 .35456584\n z 5.5496858 -1.61812\npvalue 2.862e-08 .10563675\n ll .41564344 -1.2686663\n ul .86952048 .12120621\n df . .\n crit 1.959964 1.959964\n eform 0 0\n\n\nmode[9,1]\n beta\n b .4789702\n se .06631445\n z 7.222712\npvalue 5.096e-13\n ll .34899626\n ul .60894415\n df .\n crit 1.959964\n eform 0\n\n\nmedian[9,1]\n beta\n b .45825733\n se .0653722\n z 7.0099725\npvalue 2.384e-12\n ll .33013017\n ul .58638448\n df .\n crit 1.959964\n eform 0\n\n\nCombined into single matrix\n\nmat output = (ivw, mregger, radial, mode, median)\nmat colnames output = ivw_beta mregger_beta mregger_cons ///\n radial_beta radial_cons mode_beta median_beta\nmat coleq output = \"\" \"\" \"\" \"\" \"\" \"\" \"\"\nmat output = output'\nmat list output, format(%4.3f)\n\noutput[7,9]\n b se z pvalue ll ul df crit eform\n ivw_beta 0.482 0.038 12.598 0.000 0.407 0.556 . 1.960 0.000\nmregger_beta 0.617 0.103 5.967 0.000 0.415 0.820 . 1.960 0.000\nmregger_cons -0.009 0.005 -1.600 0.110 -0.020 0.002 . 1.960 0.000\n radial_beta 0.643 0.116 5.550 0.000 0.416 0.870 . 1.960 0.000\n radial_cons -0.574 0.355 -1.618 0.106 -1.269 0.121 . 1.960 0.000\n mode_beta 0.479 0.066 7.223 0.000 0.349 0.609 . 1.960 0.000\n median_beta 0.458 0.065 7.010 0.000 0.330 0.586 . 1.960 0.000\n\n\nExport matrix to dataset\n\ndrop _all\nsvmat output, names(col)\nlocal rownames : rownames output\ndi \"`rownames'\"\ntokenize `rownames'\ngen str15 estimate = \"\"\nforvalues i = 1/7 {\n replace estimate = \"``i''\" in `i'\n}\n\nShow dataset\n\nlist estimate b se z pvalue ll ul, clean noobs\n\n estimate b se z pvalue ll ul \n ivw_beta .4815055 .038221 12.59794 2.17e-36 .4065938 .5564172 \n mregger_beta .6173131 .1034573 5.966837 2.42e-09 .4145405 .8200858 \n mregger_cons -.0087707 .0054812 -1.60014 .1095675 -.0195136 .0019723 \n radial_beta .6425819 .1157871 5.549686 2.86e-08 .4156434 .8695205 \n radial_cons -.5737301 .3545658 -1.61812 .1056367 -1.268666 .1212062 \n mode_beta .4789702 .0663145 7.222712 5.10e-13 .3489963 .6089441 \n median_beta .4582573 .0653722 7.009973 2.38e-12 .3301302 .5863845 \n\n\nSave dataset\nsave myestimates, replace\nExport as tab-delimited textfile\nexport delimited using myestimates.txt, delimiter(tab) replace" + }, + { + "objectID": "examples/markstat-call-R-example/index.html", + "href": "examples/markstat-call-R-example/index.html", + "title": "mrrobust", + "section": "", + "text": "This example shows how to run R and Stata code within the same Stata Markdown (.stmd) script. The general approach is detailed on the Stata Markdown website here and here.\nThis means that we can use the functions provided by the TwoSampleMR package to obtain data from MR-Base.\nBefore you start please install the following two Stata packages from the SSC archive, so in Stata issue the following commands (I have commented them out because I have already installed them).\nssc install whereis\nssc install markstat\n\n\n\nThe following is the example Stata Markdown document.\n---\ntitle: Using mrrobust in a Markstat document example\n---\n\nWe first need to register the R executable with Stata.\n```s\nif c(os) == \"Windows\" local rpath \"C:\\\\Program Files\\\\R\\\\R-4.2.3\\\\bin\\\\x64\\\\R.exe\"\nelse if c(os) == \"Unix\" local rpath \"/usr/bin/R\"\nelse local rpath \"/usr/local/bin/R\"\nwhereis R \"`rpath'\"\n```\n\n\nNext we have an R code chunk in which we install the required packages in R.\n```r\nif (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\")\nif (!requireNamespace(\"TwoSampleMR\", quietly = TRUE)) remotes::install_github(\"MRCIEU/TwoSampleMR\")\nif (!requireNamespace(\"MRInstruments\", quietly = TRUE)) remotes::install_github(\"MRCIEU/MRInstruments\")\n```\n\n## Extracting data from MR-Base\n\nWe will be running the script from the MR-Base paper \n([Hemani et al., 2018](https://doi.org/10.7554/eLife.34408)). The R code we will use is from \n[here](https://raw.githubusercontent.com/explodecomputer/mr-base-methods-paper/master/scripts/ldl-chd.R).\n\nFirst, we load the packages into our R session. Note that the **foreign** package provides the \n`write.dta()` function which we will use to save the data in Stata format.\n```r\nlibrary(TwoSampleMR)\nlibrary(MRInstruments)\nlibrary(foreign)\n```\n\nOur edited version of the code starts by reading in some code to generate a set of plots in R.\n```r\nsource(\"mrplots.R\")\n```\n\nWe can access the data using the **MRInstruments** package.\n```r\ndata(gwas_catalog)\n\n# Get published SNPs for LDL cholesterol\nldl_snps <- subset(gwas_catalog, grepl(\"LDL choles\", Phenotype) & Author == \"Willer CJ\")$SNP\n\n# Extract from GLGC dataset\nexposure <- convert_outcome_to_exposure(extract_outcome_data(ldl_snps, \"ieu-a-300\"))\n\n# Get outcome data from Cardiogram 2015\noutcome <- extract_outcome_data(exposure$SNP, \"ieu-a-7\")\n\n# Harmonise exposure and outcome datasets\n# Assume alleles are on the forward strand\ndat <- harmonise_data(exposure, outcome, action=1)\n```\nAt this point we have our harmonised genotype-exposure and genotype-outcome association data saved \nin an object in our R session called `dat`.\n\nThe next two code chunks perform the analysis in R.\n```r\n# Perform MR\nmr(dat)\nmr_heterogeneity(dat)\ndat$exposure <- \"LDL cholesterol\"\ndat$outcome <- \"Coronary heart disease\"\n\n# Label outliers and create plots\ndat$labels <- dat$SNP\ndat$labels[! dat$SNP %in% c(\"rs11065987\", \"rs1250229\", \"rs4530754\")] <- NA\n```\n\n```r\npng(\"ldl-chd.png\", width=1000, height=1000)\nmr_plots(dat)\ndev.off()\n```\n\n![Plots generated by the TwoSampleMR R package.](ldl-chd.png)\n\nWe now save our `dat` object as a Stata dataset.\n```r\nwrite.dta(dat, file = \"dat.dta\")\n```\n\n## Performing the analysis using mrrobust in Stata\n\nWe now switch from using R code chunks to Stata code chunks. We read the data into Stata and list \nthe variable names (note any `.` in the colnames of `dat` have been replaced with `_`).\n```s\nuse dat, clear\nds, v(28)\ndi _N\n```\n\nWe can then run the IVW model using `mregger` with multiplicative standard errors.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)], ivw\n```\n\nIt is helpful to view the forest plot of genotype specific IV estimates.\n```s\nmrforest beta_outcome se_outcome beta_exposure se_exposure, ivid(SNP) ///\n xlabel(-3,-2,-1,0,1,2,3)\ngraph export ldl-chd-mrforest.svg, width(600) replace\n```\n\n![Forest plot of genotype specific IV estimates.](ldl-chd-mrforest.svg)\n\nWe can visualise this model with `mreggerplot`.\n```s\nmreggerplot beta_outcome se_outcome beta_exposure se_exposure\ngraph export ldl-chd-mreggerplot.svg, width(600) replace\n```\n\n![Plot of the MR-Egger model.](ldl-chd-mreggerplot.svg)\n\nWe then fit the MR-Egger, median, and modal based estimators.\n```s\nmregger beta_outcome beta_exposure [aw=1/(se_outcome^2)]\n```\n\n```s\nmrmedian beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\n\n```s\nmrmodal beta_outcome se_outcome beta_exposure se_exposure, weighted\n```\nAnd we could continue with additional Stata code (or indeed R code) as we liked.\nNote to run this .stmd file in Stata we do so with the following command (specifying additional options as required, see help markstat for more information).\nmarkstat using markstat-call-R-example\n\n\n\n\nHemani et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife, 2018;7:e34408 https://doi.org/10.7554/eLife.34408" + } +] \ No newline at end of file diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 00000000..f51d04bc --- /dev/null +++ b/docs/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,1704 @@ +@font-face { + font-family: "bootstrap-icons"; + src: 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.column-body-outset table{background:#fff}.page-columns .column-body-outset-left{grid-column:body-start-outset/body-content-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-body-outset-left table{background:#fff}.page-columns .column-body-outset-right{grid-column:body-content-start/body-end-outset;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-body-outset-right table{background:#fff}.page-columns .column-page{grid-column:page-start/page-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page table{background:#fff}.page-columns .column-page-inset{grid-column:page-start-inset/page-end-inset;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-inset table{background:#fff}.page-columns .column-page-inset-left{grid-column:page-start-inset/body-content-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-inset-left table{background:#fff}.page-columns 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0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof 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0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void 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o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const 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t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof 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b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,171 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; +} + +code span.at { + color: #657422; +} + +code span.ss { + color: #20794D; +} + +code span.an { + color: #5E5E5E; +} + +code span.fu { + color: #4758AB; +} + +code span.st { + color: #20794D; +} + +code span.cf { + color: #003B4F; +} + +code span.op { + color: #5E5E5E; +} + +code span.er { + color: #AD0000; +} + +code span.bn { + color: #AD0000; +} + +code span.al { + color: #AD0000; +} + +code span.va { + color: #111111; +} + +code span.pp { + color: #AD0000; +} + +code span.in { + color: #5E5E5E; +} + +code span.vs { + color: #20794D; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; +} + +code span.ch { + color: #20794D; +} + +code span.dt { + color: #AD0000; +} + +code span.fl { + color: #AD0000; +} + +code span.co { + color: #5E5E5E; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; +} + +code span.sc { + color: #5E5E5E; +} + +code span.dv { + color: #AD0000; +} + +code span.kw { + color: #003B4F; +} + +.prevent-inlining { + content: " { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append(titleEl.innerText, toggleIcon); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + const elRect = el.getBoundingClientRect(); + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + positionToggle(); + }, 50) + ); + positionToggle(); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + nexttick(() => { + let lastBottom = 0; + for (const marginChild of marginChildren) { + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + }); + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const top = + el.getBoundingClientRect().top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightSideConflictEls)); + sidebarScrollVisiblity(toRegions(leftSideConflictEls)); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility(toRegions(leftSideConflictEls)); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/docs/site_libs/quarto-html/tippy.css b/docs/site_libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/docs/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 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/dev/null +++ b/docs/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,222 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js new file mode 100644 index 00000000..6fd4b5b9 --- /dev/null +++ b/docs/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1123 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(query, mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" + ? "all" + : quartoSearchOptions.location === "navbar" + ? "(max-width: 991px)" + : "none"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + return createElement( + "div", + { class: "quarto-search-no-results" }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} diff --git a/docs/styles.css b/docs/styles.css new file mode 100644 index 00000000..39c78dc2 --- /dev/null +++ b/docs/styles.css @@ -0,0 +1,11 @@ +/* css styles */ + +/* Center figure caption text */ +.figure-caption { + text-align: center; +} + +/* Use Fira Code font for monospace text, i.e., code */ +/* Instructions from https://github.com/tonsky/FiraCode#browser-support */ +@import url(https://cdn.jsdelivr.net/npm/firacode@6.2.0/distr/fira_code.css); +code { font-family: 'Fira Code', monospace; } diff --git a/site/examples/mrrobust-examples/mreggerplot.svg b/site/examples/mrrobust-examples/mreggerplot.svg index 9d0128f9..5473009a 100644 --- a/site/examples/mrrobust-examples/mreggerplot.svg +++ b/site/examples/mrrobust-examples/mreggerplot.svg @@ -15,15 +15,15 @@ - + - - + + - - + + diff --git a/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg b/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg new file mode 100644 index 00000000..e9ba7dae --- /dev/null +++ b/site/examples/spiller-ije-2018-examples/mreggerplot-bmi.svg @@ -0,0 +1,461 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -.01 + + -.005 + + 0 + + .005 + + .01 + + .015 + Instrument-outcome associations + + + .02 + + .04 + + .06 + + .08 + Instrument-exposure associations + + + + + + + + + + + Instruments + 95% CIs + MR-Egger + MR-Egger 95% CI + diff --git a/site/examples/spiller-ije-2018-examples/mreggerplot-height.svg b/site/examples/spiller-ije-2018-examples/mreggerplot-height.svg new file mode 100644 index 00000000..ada4975c --- /dev/null +++ b/site/examples/spiller-ije-2018-examples/mreggerplot-height.svg @@ -0,0 +1,1902 @@ + + + + + Stata Graph - 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Graph + + + + + + + + + + + + + + + + 0 + + 2 + + 4 + + 6 + + 8 + Density + + + -.2 + + 0 + + .2 + + .4 + IV estimates + + + + + + φ = .25 + φ = .5 + φ = 1 + diff --git a/site/examples/spiller-ije-2018-examples/mrmodalplot-height.svg b/site/examples/spiller-ije-2018-examples/mrmodalplot-height.svg new file mode 100644 index 00000000..d49812b8 --- /dev/null +++ b/site/examples/spiller-ije-2018-examples/mrmodalplot-height.svg @@ -0,0 +1,48 @@ + + + + + Stata Graph - Graph + + + + + + + + + + + + + + + 0 + + 2 + + 4 + + 6 + Density + + + -.4 + + -.2 + + 0 + + .2 + + .4 + IV estimates + + + + + + φ = .25 + φ = .5 + φ = 1 + diff --git a/site/helpfiles/mr.html b/site/helpfiles/mr.html new file mode 100644 index 00000000..8a92431b --- /dev/null +++ b/site/helpfiles/mr.html @@ -0,0 +1,76 @@ + + + + + + + + +
+Title
+


+     mr -- Mendelian randomization programs
+


+Syntax
+


+        mr subcommand ... [aweight] [if] [in] [, options]
+


+Description
+


+    mr is a simple wrapper to the commands in the mrrobust package.
+


+    The subcommand is specified as the mrrobust program name without its mr prefix, i.e. mregger
+    ... can alternatively be run using the syntax mr egger ....
+


+Options
+


+    mr takes the options for the specified subcommand.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    IVW (with fixed effect standard errors)
+        . mr egger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+


+    Scatter plot of MR-Egger model
+        . mr eggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Multivariable IVW
+        . mr mvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+Stored results
+


+    mr returns the results from the specified subcommand.
+


+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrdeps.html b/site/helpfiles/mrdeps.html new file mode 100644 index 00000000..57e84d80 --- /dev/null +++ b/site/helpfiles/mrdeps.html @@ -0,0 +1,40 @@ + + + + + + + + +
+Title
+


+     mrdeps -- Install dependencies for mrrobust package
+


+Syntax
+


+        mrdeps
+


+Description
+


+    mrdeps simply installs the dependencies for the mrrobust package. It has no options.
+


+Examples
+


+    Install dependencies
+        . mrdeps
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mregger.html b/site/helpfiles/mregger.html new file mode 100644 index 00000000..a14c53e4 --- /dev/null +++ b/site/helpfiles/mregger.html @@ -0,0 +1,200 @@ + + + + + + + + +
+Title
+


+     mregger -- Mendelian randomization Egger regression
+


+Syntax
+


+        mregger varname_gd varname_gp [aweight] [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      fe                  Fixed effect standard errors (default is multiplicative)
+      gxse(varname)       variable of genotype-phenotype (SNP-exposure) SEs
+      heterogi            Display heterogeneity/pleiotropy statistics
+      ivw                 Inverse-variance weighted estimator (default is MR-Egger)
+      level(#)            set confidence level; default is level(95)
+      norescale           Do not rescale residual variance to be 1 (if less than 1)
+      oldnames            Revert to using longer outcome name in b and V ereturned matrices
+      penweighted         Use penalized weights
+      radial              Use radial formulations of the models
+      tdist               Use t-distribution for Wald test and CI limits
+      unwi2gx             Additionally report unweighted Q_GX and I^2_GX statistics
+


+Description
+


+    mregger performs inverse-variance weighted (IVW; Burgess et al., 2013) and Mendelian
+    randomization Egger (MR-Egger) regression (Bowden et al., 2015) using summary level data (i.e.
+    using genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure] association
+    estimates and their standard errors).
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp variable containing the genotype-phenotype (SNP-exposure) association estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    fe specifies fixed effect standard errors (i.e. variance of residuals constrained to 1 as in
+        fixed effect meta-analysis models). The default is to use multiplicative standard errors
+        (i.e. variance of residuals unconstrained as in standard linear regression), see Thompson
+        and Sharp (1999) for further details.  We recommend specifying this option when using an
+        allelic score as the instrumental variable.
+


+    gxse(varname) specifies the variable containing the genotype-phenotype (SNP-exposure)
+        association standard errors. These are required for calculating the I^2_GX statistic
+        (Bowden et al., 2016). An I^2_GX statistic of 90% means that the likely bias due
+        measurement error in the MR-Egger slope estimate is around 10%. If I^2_GX values are less
+        than 90% estimates should be treated with caution.
+


+    heterogi displays heterogeneity/pleiotropy statistics. In the heterogeneity output the model
+        based Q-statistic is reported by multiplying the variance of the residuals by the degrees
+        of freedom (Del Greco et al., 2015).  For the IVW model this is the Cochran Q-statistic,
+        and for the MR-Egger model this is the Ruecker's Q-statistic. The corresponding I-squared
+        statistic and its 95% CI is also reported.
+


+    ivw specifies inverse-variance weighted (IVW) model (Burgess et al., 2013), the default is
+        MR-Egger.
+


+    level(#); see [R] estimation options.
+


+    norescale specifies that the residual variance is not set to 1 (if it is found to be less than
+        1). Bowden et al. (2016) rescale the residual variance to be 1 if it is found to be less
+        than 1.
+


+    oldnames revert to using the longer outcome variable name in the b and V ereturned matrices.
+


+    penweighted specifies using penalized weights as described in Burgess et al. (2016).
+


+    radial specifies the radial formulation of the IVW and MR-Egger models (Bowden et al., 2017).
+        Note there is only a difference for the MR-Egger model.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+    unwi2gx specifies the unweighted Q_GX and I^2_GX statistics to be additionally displayed in
+        the output and in the ereturn scalars.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    IVW (with fixed effect standard errors)
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw fe
+


+    MR-Egger (with SEs using an unconstrained residual variance)
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1
+


+    MR-Egger reporting I^2_GX statistic and heterogeneity Q-test
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse) heterogi
+


+    MR-Egger using a t-distribution for inference & CI limits
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, tdist
+


+    MR-Egger using the radial formulation
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial
+


+    MR-Egger using the radial formulation and reporting heterogeneity Q-test
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, radial heterogi
+


+


+Stored results
+


+    mregger stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(k)                number of instruments
+      e(I2GX)             I^2_GX (with gxse() option)
+      e(QGX)              Q_GX (with gxse() option)
+      e(phi)              Scale parameter (root mean squared error)
+


+    Macros         
+      e(cmd)              mregger
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+    If heterogi is specified mregger additionally returns the r-class results of heterogi in the
+    e-class results.
+


+    If unwi2gx is specified mregger additionally returns
+      e(I2GXunw)          Unweighted I^2_GX statistic
+      e(QGXunw)           Unweighted Q_GX statistic
+


+    mregger stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Bowden J, Davey Smith G, Burgess S.  Mendelian randomization with invalid instruments: effect
+        estimation and bias detection through Egger regression. International Journal of
+        Epidemiology, 2015, 44, 2, 512-525.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Bowden J, Del Greco F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR.  Assessing the
+        suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger
+        regression: the role of the I-squared statistic. International Journal of Epidemiology,
+        2016, 45, 6, 1961-1974.  DOI
+


+    Bowden J, Spiller W, Del-Greco F, Sheehan NA, Thompson JR, Minelli C, Davey Smith G.
+        Improving the visualisation, interpretation and analysis of two-sample summary data
+        Mendelian randomization via the radial plot and radial regression.  International Journal
+        of Epidemiology, 2018, 47, 4, 1264-1278. DOI
+


+    Burgess S, Bowden J, Dudbridge F, Thompson SG. Robust instrumental variable methods using
+        candidate instruments with application to Mendelian randomization. arXiv:1606.03729v1,
+        2016.  Link
+


+    Burgess S, Butterworth A, Thompson S. Mendelian randomization analysis with multiple genetic
+        variants using summarized data. Genetic Epidemiology, 2013, 37, 7, 658-665.   DOI
+


+    Del Greco F M, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian
+        randomization studies with summary data and a continuous outcome.  Statistics in Medicine,
+        2015, 34, 21, 2926-2940.  DOI
+


+    Do R et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352. DOI:  DOI
+


+    Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods.
+        Statistics in Medicine, 1999, 18, 20, 2693-2708.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mreggerplot.html b/site/helpfiles/mreggerplot.html new file mode 100644 index 00000000..a509a271 --- /dev/null +++ b/site/helpfiles/mreggerplot.html @@ -0,0 +1,140 @@ + + + + + + + + +
+Title
+


+     mreggerplot -- Scatter plot for MR-Egger type models
+


+Syntax
+


+        mreggerplot varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      egger               MR-Egger estimator for fitted line
+      ellipses            marker confidence intervals as ellipses
+      errorbars           marker confidence intervals as capped lines
+      fe                  fixed effect standard errors for fitted line
+      gpci                CIs around G-P associations
+      ivw                 IVW line (default is MR-Egger)
+      legend(string)      legend options
+      level(#)            set confidence level; default is level(95)
+      linetop             draw fitted line (and CI) on top of points
+      mleglabel(string)   Label for data points in legend; default is Instruments
+      median              median estimator for fitted line
+      mlabel(string)      variable containing marker labels
+      nolci               do not plot confidence interval around fitted line
+      noline              do not plot fitted line
+      nomcis              do not plot confidence intervals around markers
+      penweighted         penalized weighted estimator
+      re                  random effect estimator for fitted line
+      recons              random intercept for fitted line
+      reslope             random slope for fitted line
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted median estimator
+      wmarkers            weighted markers
+      *                   Other options passed to the twoway plot
+


+Description
+


+    mreggerplot plots a scatter plot for MR-Egger type models
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Figure 4, LDL-c
+    "All genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Scatter plot of MR-Egger model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Scatter plot of MR-Egger model labelling outlying genotypes
+        . gen mlabvar = rsid if abs(ldlcbeta) > .3
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, mlab(mlabvar) mlabsize(vsmall)
+            mlabp(7) mlabc(gs0)
+


+    Scatter plot of MR-Egger model with genotype-phenotype CIs
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, gpci
+


+    Scatter plot of MR-Egger model specifying own titles and legend label
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, title(Investigating pleiotropy:
+            MR-Egger model fit) xtitle(Genotype-LDLC associations) ytitle(Genotype-CHD
+            associations) mleglabel(Genotypes)
+


+    Scatter plot of IVW model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ivw
+


+    Scatter plot of unweighted median model
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, median
+


+    Scatter plot of MR-Egger model using ellipses around points
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ellipses
+


+    Scatter plot of MR-Egger model adding an IVW line (with legend entry) for comparison
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+        . mregger chdbeta ldlcbeta [aw=1/(chdse^2)] if sel1==1, ivw
+        . addplot : function _b[ldlcbeta]*x if sel1==1, range(0 0.5) lc(gs0) lp(longdash)
+            lw(vthin) legend(order(5 "Instruments" 4 "95% CIs" 3 "MR-Egger" 2 "MR-Egger 95% CI" 6
+            "IVW") rows(1) size(vsmall))
+


+    Scatter plot of MR-Egger model adding weighted median and modal lines (with legend entries)
+    for comparison
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+        . addplot : function _b[beta]*x if sel1==1, range(0 0.5) lc(gs0) lp(shortdash) lw(vthin)
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, phi(.25)
+        . addplot : function _b[beta]*x if sel1==1, range(0 0.5) lc(gs0) lp(longdash)
+            legend(order(5 "Instruments" 4 "95% CIs" 3 "MR-Egger" 2 "MR-Egger 95% CI" 6 "Weighted
+            median" 7 "Modal") rows(1) si(vsmall) symx(*.5))
+


+    Scatter plot without any fitted lines
+        . mreggerplot chdbeta chdse ldlcbeta ldlcse if sel1==1, ivw noline nolci gpci
+            legend(order(4 "Instruments" 3 "95% CI") size(vsmall) rows(1))
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al.Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mreggersimex.html b/site/helpfiles/mreggersimex.html new file mode 100644 index 00000000..b3613244 --- /dev/null +++ b/site/helpfiles/mreggersimex.html @@ -0,0 +1,96 @@ + + + + + + + + +
+Title
+


+     mreggersimex -- SIMEX for the MR-Egger estimator
+


+Syntax
+


+        mreggersimex varname_gd varname_gp [aweight] [if] [in] [, options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      mreggeropts           Options for mregger
+      bsopts                Options for bootstrap
+      gxse(varname)         Variable containg genotype-phenotype (SNP-exposure) SEs
+      noboot                Do not perform bootstrapping for SEs
+      nodraw                Do not draw SIMEX plot
+      reps(#)               No. bootstrap replications
+      seed(#)               Seed for random number generator, specify for reproducible results
+      simreps(#)            No. simulation replications
+


+Description
+


+    mreggersimex performs the simulation extrapolation (SIMEX) algorithm (Cook et al., 1995,
+    Hardin et al., 2003) on mregger using the commonly applied quadratic extrapolation (Bowden et
+    al., 2016).
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp variable containing the genotype-phenotype (SNP-exposure) association estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    SEs squared, i.e. aw=1/(gdse^2).
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et el. (2016), Figure 4, LDL-c
+    "All genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    SIMEX using default settings
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345)
+


+    Suppressing the bootstrap SEs
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot
+


+    Suppressing the bootstrap SEs and the plot
+        . mreggersimex chdbeta ldlcbeta [aw=1/chdse^2] if sel1==1, gxse(ldlcse) seed(12345) noboot
+            nodraw
+


+References
+


+    Bowden J, Del Greco FM, Minelli C, Davey Smith G, Sheehan NA, Thompson JR.  Assessing the
+        suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger
+        regression: the role of the I2 statistic.  International Journal of Epidemiology, 2016,
+        45, 6, 1961-1974.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Cook J and Stefanski LA. A simulation extrapolation method for parametric measurement error
+        models. Journal of the American Statistical Association, 1995, 85, 652-663.  Link
+


+    Do R et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352. DOI:  DOI
+


+    Hardin JW, Schmiediche H, Carroll RJ. The simulation extrapolation method for fitting linear
+        models with additive measurement error. Stata Journal.  2003, 3, 4, 373-385.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrforest.html b/site/helpfiles/mrforest.html new file mode 100644 index 00000000..9b1b800d --- /dev/null +++ b/site/helpfiles/mrforest.html @@ -0,0 +1,134 @@ + + + + + + + + +
+Title
+


+     mrforest -- Forest plot for MR-Egger type models
+


+Syntax
+


+        mrforest varname_gd varname_gdse varname_gp varname_gpse [varname_cov] [if] [in] [,
+              options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      astext(#)             Percentage of plot taken up by areas for text - some trial and error
+                              may be required
+      effect(string)        Label for statistics column; default is Estimate
+      ivid(varname)         Variable to label genotypes, usually containing RSIDs
+      ividlabel(string)     Label for genotypes; default is Genotypes
+      ivwlabel(string)      Label for IVW model; default is IVW
+      gsort(string)         how to sort the estimates, if specified must be one of; ascending,
+                              descending, or unsorted; default is ascending
+      level(#)              set confidence level; default is level(95)
+      mreggerlabel(string)  Label for MR-Egger model; default is MR-Egger
+      mrmedianlabel(string) Label for median model; default is Median
+      mrmodallabel(string)  Label for modal model; default is Modal
+      models(#)             number of models to show (1 IVW, 2 & MR-Egger, 3 & Median, 4 & Modal);
+                              default is 2
+      modelslabel(string)   Label for models; default is Summary
+      modelsonly            Only show model estimates on plot
+      nonote                Suppress note reporting I^2_GX
+      nostats               Suppress the statistics column
+      textsize(#)           Scaling factor for text on plot. I have tried to use sensible numbers
+                              here but some trial and error may be required
+      zcis                  Use normal distribution CI limits (for IVW and MR-Egger)
+


+      Options passed to other commands:
+      ivwopts(string)       options for IVW estimate from mregger, ivw fe
+      mreggeropts(string)   options for MR-Egger estimate from mregger
+      mrivestopts(string)   options for mrivests used to generate genotype specific ratio
+                              estimates and SEs
+      mrmedianopts(string)  options for median estimate from mrmedian
+      mrmodal(string)       options for modal estimate from mrmodal
+      nofe                  Remove fe option (fixed effect SE) from the IVW fit
+


+      *                     Other options passed to the metan
+


+Description
+


+    mrforest plots a forest plot for MR-Egger type models.  It is really a wrapper program for a
+    call to metan (Harris et al., 2008), which must be installed.
+


+    If you do not already have metan, it can be installed by running: ssc install metan.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+    varname_cov is a variable containing the covariances between the genotype-disease
+    (SNP-outcome) and genotype-phenotype (SNP-exposure) associations.
+


+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016) Figure 4, LDL-c "All
+    genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Forest plot of genotype specific IV estimates and IVW and MR-Egger estimates, labelling the
+    genotypes with their RSID.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid)
+


+    Having seen the first plot we can now define nicer x-axis labels.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid)
+            xlabel(-5,-4,-3,-2,-1,0,1,2,3,4,5)
+


+    Removing the column of estimates from the plot, and sorting in descending order of the IV
+    estimates.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) nostats gsort(descending)
+


+    Showing all 4 models and modifying some labels.
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) models(4) modelslabel(All
+            genotypes)
+


+    If you have data which require more than 2 decimal places, use metan's undocumented dp option,
+    e.g. for 4 decimal places specify dp(4)
+        . gen chdbeta2 = chdbeta / 100
+        . gen chdse2 = chdse / 100
+        . mrforest chdbeta chdse ldlcbeta ldlcse if sel1==1, ivid(rsid) dp(4)
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+    Harris RJ, Bradburn MJ, Deeks JJ, Harbord RM, Altman DG, Sterne JAC. metan: fixed- and
+        random-effects meta-analysis.  Stata Journal, 2008, 8, 1, 3-28. Link
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrfunnel.html b/site/helpfiles/mrfunnel.html new file mode 100644 index 00000000..e8801512 --- /dev/null +++ b/site/helpfiles/mrfunnel.html @@ -0,0 +1,111 @@ + + + + + + + + +
+Title
+


+     mrfunnel -- Funnel plot for two-sample MR analysis
+


+Syntax
+


+        mrfunnel varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      extraplots(string)  extra plots to add to the overall plot
+      metric(metric)      scale of y-axis
+      noivw               do not plot IVW line
+      nomregger           do not plot MR-Egger line
+      mrivestsopts(opts)  options passed to mrivests
+      scatteropts(opts)   options passed to the scatter command
+      xlrange(# #)        the range for the IVW and MR-Egger lines, see twoway_function range()
+                            option
+      *                   other options passed to twoway
+


+Description
+


+    mrfunnel provides a funnel plot for a two-sample Mendelian randomization analysis.
+


+    There are 3 choices of measures of instrument strength to plot on the y-axis, which are
+    specified using the metric option and are described below.
+ 
+    On the plot the MR-Egger estimate is the line with the longer dashes, the IVW estimate is
+    shown with the shorter dashes.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    metric(gpbeta|gpbetastd|invse) specifies the metric for the y-axis. Can be one of:
+    - gpbeta: the absolute value of the genotype-phenotype (SNP-exposure) estimates,
+    - gpbetastd: gpbeta standardised by the genotype-disease (SNP-outcome) standard errors (the
+    default),
+    - invse: the inverse of the standard errors on the genotype specific IV ratio estimates.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016) Web Figure A2
+    (top-right plot, LDL-C with 73 genotypes).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Funnel plot
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Without adding the IVW and MR-Egger estimates
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, noivw nomregger
+


+    Using an unstandardised y-axis
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(gpbeta)
+


+    Using inverse IV SEs on the y-axis
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(invse)
+


+    Remove the legend
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, legend(off)
+


+    Extend the IVW and MR-Egger lines to the y-axis limits (as per the original version of this
+    command)
+        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrivests.html b/site/helpfiles/mrivests.html new file mode 100644 index 00000000..5c2b642c --- /dev/null +++ b/site/helpfiles/mrivests.html @@ -0,0 +1,91 @@ + + + + + + + + +
+Title
+


+     mrivests -- Generate genotype specific instrumental variable ratio (Wald) estimates in a
+dataset
+


+Syntax
+


+        mrivests varname_gd varname_gdse varname_gp [varname_gpse varname_cov] [if] [in] [,
+                options]
+


+    options                      Description
+    ----------------------------------------------------------------------------------------------
+      generate(varlist, replace) Variables to contain IV estimates and SEs or CI limits
+      *                          options passed to mrratio
+


+Description
+


+    mrivests calls mrratio to put the instrumental variable ratio (Wald) estimate and its standard
+    error in the variables specified.
+


+    varname_gd is a variable containing genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the standard errors of the genotype-disease
+    (SNP-outcome) association estimates.
+


+    varname_gp is a variable containing genotype-phenotype (SNP-exposure) association estimates.
+


+    varname_gpse is a variable containing the standard errors of the genotype-phenotype
+    (SNP-exposure) association estimates.
+


+    varname_cov is a variable containing the covariance between the genotype-disease (SNP-outcome)
+    and the genotype-phenotype (SNP-exposure) estimates.
+


+Options
+


+    generate(varlist, replace) specifies the variables (2 or 3) to contain the IV estimates and
+        their standard errors or confidence interval limits. Specifying replace replaces the
+        values in these variables if they already exist in the dataset.
+


+    * options passed through to mrratio, e.g. nome.
+


+Examples
+


+    Using the data provided by Do et al. (2013) generate genotype specific estimates for the LDL-c
+    phenotype.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Generate IV estimates in dataset
+        . mrivests chdbeta chdse ldlcbeta ldlcse if sel1==1, generate(ivest ivse)
+


+    Generate IV estimates with SEs assuming NOME
+        . mrivests chdbeta chdse ldlcbeta if sel1==1, generate(ivest ivse, replace) nome
+


+    Generate IV estimates with CI limits using Fieller's Theorem
+        . drop ivest
+        . mrivests chdbeta chdse ldlcbeta ldlcse if sel1==1, generate(ivest ivcilow ivciupp)
+            fieller
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrleaveoneout.html b/site/helpfiles/mrleaveoneout.html new file mode 100644 index 00000000..b625e8ed --- /dev/null +++ b/site/helpfiles/mrleaveoneout.html @@ -0,0 +1,93 @@ + + + + + + + + +
+Title
+


+     mrleaveoneout -- Leave one (genotype) out (at a time) analysis
+


+Syntax
+


+        mrleaveoneout varname_gd varname_gp [if] [in], gyse(varname) [options]
+


+    options                 Description
+    ----------------------------------------------------------------------------------------------
+      astext(#)             Percentage of plot taken up by areas for text - some trial and error
+                              may be required
+      genotype(varname)     Variable to label genotypes, usually containing RSIDs
+      gxse(varname)         Variable with genotype-phenotype (SNP-exposure) standard errors (if
+                              required by method)
+      gyse(varname)         Variable with genotype-disease (SNP-outcome) standard errors
+      metanopts(string)     Options passed to metan for the plot
+      method(string)        The method fitted for the analysis
+      noplot                Suppresses the plot
+      noprint               Suppresses the display of each leave one out model
+      textsize(#)           Scaling factor for text on plot. I have tried to use sensible numbers
+                              here but some trial and error may be required
+      *                     Other options passed to the analysis method command
+


+Description
+


+    mrleaveoneout performs leave one out analysis, in which each genotype is omitted in turn from
+    the analysis.  A plot of the estimates is shown by default.
+


+    For multiple exposure models such as MVMR and MVMR-Egger the estimate is collected for the
+    first phenotype.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+Options
+


+    method(string) Specifies the method used for the analysis.  Must be one of ivw, egger,
+        mregger, mrivw, median, mrmedian, mrmodal, modal, mode, mvmr, mvivw, mvegger.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel2 = (ldlcp2 < 1e-25)
+


+    Perform leave one out analysis using the IVW estimator
+        . mrleaveoneout chdbeta ldlcbeta if sel2==1, gyse(chdse) genotype(rsid)
+


+    Leave one out analysis using MVMR (collecting the estimate for LDL-c)
+        . mrleaveoneout chdbeta ldlcbeta hdlcbeta tgbeta if sel2==1, method(mvmr) gyse(chdse)
+            genotype(rsid)
+


+    If you have data which require more than 2 decimal places, use metan's undocumented dp option,
+    e.g. for 4 decimal places specify dp(4)
+        . gen chdbeta2 = chdbeta / 100
+        . gen chdse2 = chdse / 100
+        . mrleaveoneout chdbeta2 ldlcbeta if sel2==1, gyse(chdse2) genotype(rsid) metanopts(dp(4))
+


+References
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmedian.html b/site/helpfiles/mrmedian.html new file mode 100644 index 00000000..c2ec2b2d --- /dev/null +++ b/site/helpfiles/mrmedian.html @@ -0,0 +1,123 @@ + + + + + + + + +
+Title
+


+     mrmedian -- Weighted median of instrumental variable estimates
+


+Syntax
+


+        mrmedian varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      level(#)            set confidence level; default is level(95)
+      penweighted         penalized weighted estimator
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted estimator
+


+Description
+


+    mrmedian performs unweighted, weighted, and penalized median IV estimator on summary level
+    data (i.e. reported genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure]
+    association estimates and their standard errors for individual genotypes).
+


+    See Bowden et al. (2016) for more information.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    level(#); see [R] estimation options.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 1000 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted use weights.
+


+    penweighted use penalized weights.
+


+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016), Table 4, LDL-c "All
+    genetic variants" median estimates.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Unweighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Unweighted median estimator with reproducible standard error
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Weighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+


+    Penalized weighted median estimator
+        . mrmedian chdbeta chdse ldlcbeta ldlcse if sel1==1, penweighted
+


+Stored results
+


+    mrmedian stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmedian
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmedianobs.html b/site/helpfiles/mrmedianobs.html new file mode 100644 index 00000000..da0495fb --- /dev/null +++ b/site/helpfiles/mrmedianobs.html @@ -0,0 +1,116 @@ + + + + + + + + +
+Title
+


+     mrmedianobs -- Weighted median of instrumental variable estimates
+


+Syntax
+


+        mrmedianobs depvar [varlist1] (varname_endog = varlist_ivs) [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      all                 report percentile and bias corrected confidence intervals
+      level(#)            set confidence level; default is level(95)
+      obsboot             obtain standard error by bootstrapping at observation level
+      penweighted         penalized weighted estimator
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted estimator
+


+Description
+


+    mrmedianobs performs unweighted, weighted, and penalized IV estimator on observation level
+    data (Bowden et al., 2016).
+


+         depvar          outcome variable
+         varlist1        covariates to adjust for
+         varname_endog   exposure/treatment received/endogenous variable
+         varlist_ivs     instrumental variables
+


+Options
+


+    all report percentile and bias corrected bootstrap confidence interval limits (only applies to
+        observation level bootstrapping with obsboot).
+


+    level(#); see [R] estimation options.
+


+    obsboot obtain bootstrap standard error by bootstrapping at the observation level.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 50 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted use weights.
+


+    penweighted use penalized weights.
+


+


+Examples
+


+    Simulated test dataset.
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/mrmedianobs_testdata, clear
+


+    Unweighted median estimator
+        . mrmedianobs y (x = z1-z20)
+


+    Weighted median estimator
+        . mrmedianobs y (x = z1-z20), weighted
+


+    Penalized weighted median estimator
+        . mrmedianobs y (x = z1-z20), penweighted
+


+    Unweighted median estimator with percentile CI limits of observation level bootstrapping
+        . mrmedianobs y (x = z1-z20), obsboot all
+


+


+Stored results
+


+    mrmedian stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(N)                number of observations
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmedianobs
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S.  Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmodal.html b/site/helpfiles/mrmodal.html new file mode 100644 index 00000000..e9c1445d --- /dev/null +++ b/site/helpfiles/mrmodal.html @@ -0,0 +1,138 @@ + + + + + + + + +
+Title
+


+     mrmodal -- Modal estimator for summary data
+


+Syntax
+


+        mrmodal varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      level(#)            set confidence level; default is level(95)
+      nome                NOME assumption
+      nosave              Do not save density and vector of IV estimates in Mata
+      phi(#)              value of phi (for bandwidth)
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted IV estimates
+


+Description
+


+    mrmodal implements the zero modal estimator of Hartwig et al. (2017) for use with summary
+    level data (i.e. reported genotype-disease [SNP-outcome] and genotype-phenotype [SNP-exposure]
+    association estimates and their standard errors for individual genotypes).
+


+    Standard errors are obtained by parametric bootstrapping.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Options
+


+    level(#); see [R] estimation options.
+


+    nome specifies the NOME (no measurement error in the genotype-phenotype [SNP-exposure]
+        associations) assumption.
+


+    nosave specifies that the density of the IV estimates and column vector of IV estimates should
+        not be saved in Mata. If not specified these are saved in Mata as mrmodal_densityiv and
+        mrmodal_g respectively.
+


+    phi(#) specifies the parameter phi which is used in the calculation of the bandwidth for the
+        density estimation. Default is phi = 1, other values commonly chosen are 0.25 and 0.5.
+


+    reps(#) specifies the number of bootstrap replications for obtaining the standard error. The
+        default is 1000 replications.
+


+    seed(#) specifies the initial value of the random-number seed.  The default is the current
+        random-number seed. Specifying seed(#) is the same as typing set seed # before issuing the
+        command; see set_seed.
+


+    weighted weight the instrumental variable estimates.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Investigate what is a good value of phi to use (we want a smooth density plot)
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Simple mode estimator
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1
+


+    Simple mode estimator with reproducible standard error
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Weighted mode estimator
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted
+


+    Simple mode estimator with NOME assumption
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, nome
+


+    Weighted mode estimator with NOME assumption
+        . mrmodal chdbeta chdse ldlcbeta ldlcse if sel1==1, weighted nome
+


+Stored results
+


+    mrmodal stores the following in e():
+


+    Scalars        
+      e(k)                number of instruments
+      e(phi)              value of phi
+      e(reps)             number of (bootstrap) replications
+


+    Macros         
+      e(cmd)              mrmodal
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+References
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+    Hartwig FP, Davey Smith G, Bowden J.  Robust inference in two-sample Mendelian randomisation
+        via the zero modal pleiotropy assumption. International Journal of Epidemiology, 2017, 46,
+        6, 1985-1998.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmodalplot.html b/site/helpfiles/mrmodalplot.html new file mode 100644 index 00000000..18520e4a --- /dev/null +++ b/site/helpfiles/mrmodalplot.html @@ -0,0 +1,89 @@ + + + + + + + + +
+Title
+


+     mrmodalplot -- Density plot to investigate values of phi in mrmodal
+


+Syntax
+


+        mrmodalplot varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      lc(optlist)         list of line colors
+      lp(optlist)         list of line patterns
+      lw(optlist)         list of line widths
+      nome                NOME assumption
+      phi(numlist)        value/s of phi (for bandwidth), default is .25 .5 1
+      reps(#)             number of bootstrap replications to obtain standard error
+      seed(#)             seed for random number generator for bootstrapping to obtain standard
+                            error
+      weighted            weighted IV estimates
+      *                   Other options passed to the twoway plot
+


+Description
+


+    mrmodalplot plots the density of the IV estimates used in the mrmodal estimator.
+


+    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
+    standard errors.
+


+    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
+    estimate standard errors.
+


+Examples
+


+    Using the data provided by Do et al. (2013) recreate Bowden et (2016), Figure 4, LDL-c "All
+    genetic variants" (plot in row 2, column 1).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with exposure < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Densities with phi=.25, .5, 1 and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, seed(12345)
+


+    Densities with phi=.4, .6, .8, 1 and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, phi(.4(.2)1) seed(12345)
+


+    Lines in grayscale and reproducible standard error
+        . mrmodalplot chdbeta chdse ldlcbeta ldlcse if sel1==1, lc(gs10 gs5 gs0) seed(12345)
+


+


+References
+


+    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
+        randomization with some invalid instruments using a weighted median estimator. Genetic
+        Epidemiology, 2016, 40, 4, 304-314.  DOI
+


+    Do et al. Common variants associated with plasma triglycerides and risk for coronary artery
+        disease. Nature Genetics, 2013, 45, 1345-1352.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmvegger.html b/site/helpfiles/mrmvegger.html new file mode 100644 index 00000000..1b26be00 --- /dev/null +++ b/site/helpfiles/mrmvegger.html @@ -0,0 +1,119 @@ + + + + + + + + +
+Title
+


+     mrmvegger -- Multivariable MR-Egger regression (MVMR-Egger)
+


+Syntax
+


+        mrmvegger varname_gd varname_gp1 [varname_gp2 ...] [aweight] [if] [in] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      orient(#)           orient the data wrt to the phenotype which corresponds with the #th
+                            genotype-phenotype (SNP-exposure) association variable in the varlist
+                            (default is 1)
+      level(#)            set confidence level; default is level(95)
+      tdist               use t-distribution for Wald test and CI limits
+


+Description
+


+    mrmvegger performs multivariable MR-Egger regression.  For further information see Rees et al.
+    (2017).
+


+    By default multiplicative random effect standard errors are reported.  However, if the
+    residual variance is found to be less than 1 the model is refitted with this constrained to 1.
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp# variable containing the #th genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    orient(#) specifies which phenotype to orient the data to. The default is 1, i.e. the
+        phenotype in the first genotype-phenotype (SNP-exposure) variable in the varlist.
+


+    level(#); see [R] estimation options.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with LDL-C < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    MVMR-Egger regression
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+    Orient wrt HDL-C instead of LDL-C
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(2)
+


+    Orient wrt triglycerides instead of LDL-C
+        . mrmvegger chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, orient(3)
+


+Stored results
+


+    mrmvegger stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(N)                Number of genotypes
+      e(Np)               Number of phenotypes
+      e(phi)              Scale parameter (root mean squared error)
+


+    Macros         
+      e(cmd)              Command name
+      e(cmdline)          Command issued
+      e(orientvar)        Genotype-phenotype (SNP-exposure) association variable the model is
+                            oriented wrt
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+


+    mrmvegger stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Rees J, Wood A, Burgess S. Extending the MR-Egger method for multivariable Mendelian
+        randomization to correct for both measured and unmeasured pleiotropy.  Statistics in
+        Medicine, 2017, 36, 29, 4705-4718.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrmvivw.html b/site/helpfiles/mrmvivw.html new file mode 100644 index 00000000..bc40e62e --- /dev/null +++ b/site/helpfiles/mrmvivw.html @@ -0,0 +1,143 @@ + + + + + + + + +
+Title
+


+     mrmvivw/mvivw/mvmr -- Multivariable inverse variance weighted regression (MVIVW)
+


+Syntax
+


+        mrmvivw/mvivw/mvmr varname_gd varname_gp1 [varname_gp2 ...] [aweight] [if] [in] [,
+                options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      fe                  fixed effect standard errors (default is multiplicative random effect)
+      gxse(varlist)       varlist of genotype-phenotype (SNP-exposure) standard errors
+      level(#)            set confidence level; default is level(95)
+      tdist               use t-distribution for Wald test and CI limits
+


+Description
+


+    mrmvivw/mvivw/mvmr performs multivariable inverse-variance weighted (IVW) regression using
+    summary level data.  See  Burgess et al. (2015) for more information.
+


+    varname_gd variable containing the genotype-disease (SNP-outcome) association estimates.
+


+    varname_gp# variable containing the #th genotype-phenotype (SNP-exposure) association
+    estimates.
+


+    For the analytic weights you need to specify the inverse of the genotype-disease (SNP-outcome)
+    standard errors squared, i.e. aw=1/(gdse^2).
+


+Options
+


+    fe specifies fixed effect standard errors (i.e. variance of residuals constrained to 1 as in
+        fixed effect meta-analysis models). The default is multiplicative random effect standard
+        errors in which case the variance of the residuals is unconstrained and the square root of
+        the estimated residual variance is displayed (Residual standard error). If the residual
+        variance is found to be less than 1 an error message is shown and the model is refitted
+        with it constrained to 1.
+


+    gxse(varlist) specifies a varlist of genotype-phenotype (SNP-exposure) standard errors.  These
+        should be in the same order as the genotype-phenotype (SNP-exposure) variables in the main
+        varlist.  When this option is specified the Q_A statistic for instrument validity is
+        calculated.  When this is specified and there are two or more phenotypes conditional F
+        statistics for instrument strength are calculated.  See Sanderson et al. (2019) and
+        Sanderson et al. (2021) for more information.
+


+    level(#); see [R] estimation options.
+


+    tdist specifies using the t-distribution, instead of the normal distribution, for calculating
+        the Wald test and the confidence interval limits.
+


+Examples
+


+    Using the data provided by Do et al. (2013).
+


+    Setup
+        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear
+


+    Select observations (p-value with LDL-C < 10^-8)
+        . gen byte sel1 = (ldlcp2 < 1e-8)
+


+    Fit MVMR/MVIVW
+        . mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+        . mvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+        . mvmr chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1
+


+    Report Q_A statistic and conditional F-statistics
+        . mrmvivw chdbeta ldlcbeta hdlcbeta tgbeta [aw=1/(chdse^2)] if sel1==1, gxse(ldlcse hdlcse
+            tgse)
+


+Stored results
+


+    mrmvivw/mvivw/mvmr stores the following in e():
+


+    Scalars        
+      e(df_r)             residual degrees of freedom (with tdist option)
+      e(N)                Number of genotypes
+      e(Np)               Number of phenotypes
+      e(phi)              Scale parameter (root mean squared error)
+    If gxse() specified
+      e(Qa)               Q_A statistic
+      e(Qadf)             Degrees of freedom of Q_A statistic
+      e(Qap)              P-value for Q_A chi-squared test
+


+    Macros         
+      e(cmd)              Command name
+      e(cmdline)          Command issued
+      e(setype)           Standard error type
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimates
+    If gxse() specified
+      e(Fx)               vector of conditional F-statistics
+      e(Qx)               vector of Q_x statistics
+


+    mrmvivw/mvivw/mvmr stores the following in r():
+


+    Matrices       
+      r(table)            Coefficient table with rownames: b, se, z, pvalue, ll, ul, df, crit,
+                            eform
+


+References
+


+    Burgess S, Dudbridge F, Thompson SG. Multivariable Mendelian randomization:  the use of
+        pleiotropic genetic variants to estimate causal effects.  American Journal of
+        Epidemiology, 2015, 181, 4, 251-260.  DOI
+


+    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
+        artery disease. Nature Genetics. 45, 1345-1352.  DOI
+


+    Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian
+        randomization in the single-sample and two-sample summary data settings. International
+        Journal of Epidemiology, 2019, 48, 3, 713-727.  DOI
+


+    Sanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments
+        in two-sample multivariable Mendelian randomization.  Statistics in Medicine, 2021, 40,
+        25, 5434-5452.  DOI
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrratio.html b/site/helpfiles/mrratio.html new file mode 100644 index 00000000..425e0da9 --- /dev/null +++ b/site/helpfiles/mrratio.html @@ -0,0 +1,114 @@ + + + + + + + + +
+Title
+


+     mrratio -- Instrumental variable ratio (Wald) estimator
+


+Syntax
+


+        mrratio #gd #gdse #gp [#gpse #cov] [, options]
+


+    options               Description
+    ----------------------------------------------------------------------------------------------
+      eform               exp() estimate and CI limits
+      fieller             CI using Fieller's Theorem
+      level(#)            set confidence level; default is level(95)
+      nome                NOME assumption
+


+Description
+


+    mrratio implements the standard instrumental variable ratio (Wald) estimator.
+


+    mrratio can generate confidence interval limits using either a standard error from a Taylor
+    series expansion (the default), a standard error using the NOME (no measurement error) in the
+    genotype-phenotype (SNP-exposure) association assumption, or using Fieller's Theorem.
+


+    The user needs to have summary level genotype-disease (SNP-outcome) and genotype-phenotype
+    (SNP-exposure) association estimates and their standard errors. If assuming NOME the standard
+    error of the genotype-phenotype (SNP-exposure) association is not required.
+


+    It is also optional to provide the covariance between the genotype-disease (SNP-outcome) and
+    genotype-phenotype (SNP-exposure) estimates. If these are from independent samples then this
+    covariance is zero.
+


+    #gd genotype-disease (SNP-outcome) association estimate.
+


+    #gdse standard error of the genotype-disease (SNP-outcome) association estimate.
+


+    #gp genotype-phenotype (SNP-exposure) association estimate.
+


+    #gpse standard error of the genotype-phenotype (SNP-exposure) association estimate.
+


+    #cov covariance between the genotype-disease (SNP-outcome) and the genotype-phenotype
+    (SNP-exposure) estimates.
+


+Options
+


+    eform specifies reporting the exponentiated ratio estimate and confidence interval limits.
+


+    fieller specifies deriving the confidence interval using Fieller's Theorem.
+


+    level(#); see [R] estimation options.
+


+    nome specifies the NOME (no measurement error in the genotype-phenotype [SNP-exposure]
+        association) assumption.
+


+Examples
+


+    Hypothetical example
+        . mrratio 1 .5 1 .25
+


+    With NOME assumption
+        . mrratio 1 .5 1 .25, nome
+


+    With NOME assumption
+        . mrratio 1 .5 1, nome
+


+    Exponentiate estimate and CI limits
+        . mrratio 1 .5 1 .25, eform
+


+    CI using Fieller's Theorem
+        . mrratio 1 .5 1 .25, fieller
+


+Stored results
+


+    mrratio stores the following in e():
+


+    Scalars        
+      e(fiellerres)       Result code from the Fieller CI: . fieller not specified; 1 closed CI; 2
+                            union of two intervals; 3 CI is whole real line
+      e(level)            level specified in first call to mrratio.
+


+    When fieller specified additional scalars:
+      e(lowerci)          lower CI limit
+      e(upperci)          upper CI limit
+


+    Macros         
+      e(cmd)              mrratio
+      e(cmdline)          command as typed
+


+    Matrices       
+      e(b)                coefficient vector
+      e(V)                variance-covariance matrix of the estimate
+


+Author
+


+INCLUDE help mrrobust-author
+
+ + diff --git a/site/helpfiles/mrrobust.html b/site/helpfiles/mrrobust.html new file mode 100644 index 00000000..2e484a6b --- /dev/null +++ b/site/helpfiles/mrrobust.html @@ -0,0 +1,81 @@ + + + + + + + + +
+Title
+


+    mrrobust -- commands for two-sample Mendelian randomization (MR) analyses.
+


+    https://remlapmot.github.io/mrrobust/
+


+Commands
+


+      mr            Primary command syntax, i.e. use mr egger ... syntax instead of mregger ...
+


+      mrdeps        Install dependencies for the package
+


+      mregger       MR-Egger and inverse-variance weighted (IVW) estimators
+


+      mreggersimex  Simulation extrapolation algorithm for the MR-Egger model
+


+      mreggerplot   Scatter plot showing instrument specific estimates with IVW, MR-Egger, or
+                      median fitted line and confidence interval
+


+      mrmedian      Unweighted, weighted, and penalized weighted median estimators for summary
+                      level data
+


+      mrmedianobs   Unweighted, weighted, and penalized weighted median estimators for individual
+                      level data
+


+      mrmodal       Modal estimator for summary level data
+


+      mrmodalplot   Plot of density used in the modal estimator
+


+      mrratio       Ratio (Wald) estimator for summary level data for a single genotype
+


+      mrivests      Generate ratio (Wald) estimates for summary level data in dataset
+


+      mrforest      Forest plot of genotype specific and model (IVW, MR-Egger, Median, Modal) IV
+                      estimates
+


+      mrfunnel      Funnel plot of the genotype specific IV estimates
+


+      mrmvivw       Multivariable inverse variance weighted estimator (also mvivw and mvmr)
+


+      mrmvegger     Multivariable MR-Egger regression
+


+      mrleaveoneout Leave one out analysis
+


+Description
+


+    mrrobust is a suite of programs implementing recently developed estimators which are robust to
+    certain proportions of invalid instrumental variables.
+


+    Most of the commands are designed to use summary level data as provided by repositories such
+    as MR-Base.
+


+    The estimators were developed in the context of MR studies but could be used for other
+    applications of instrumental variables.
+


+    There is a website showing the examples from the helpfiles here: 
+    https://remlapmot.github.io/mrrobust/
+


+Author
+


+INCLUDE help mrrobust-author
+
+ +

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