From d415f87b0d382e230ef000148e4055a55dc6e08c Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Sun, 8 Dec 2019 07:43:50 -0500 Subject: [PATCH 1/5] updates to curve_boot --- NEWS.md | 11 ++++ R/curve_boot.R | 134 +++++++++++++++++++++++++++++++++++++++----- R/curve_meta.R | 6 +- README.Rmd | 34 +++++------ README.md | 37 ++++++------ codemeta.json | 4 +- docs/index.html | 34 +++++------ examples/boot.R | 73 ------------------------ examples/examples.R | 25 +++++++++ inst/WORDLIST | 8 --- tests/spelling.R | 9 +-- usethis.R | 2 + 12 files changed, 220 insertions(+), 157 deletions(-) delete mode 100644 examples/boot.R diff --git a/NEWS.md b/NEWS.md index 7e75748..d54d02c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,14 @@ +# concurve 2.3.0.1 + +## Major changes +* `curve_boot()` can utilize parametric BCa bootstrap methods to compute functions +* Corrected error where order of labels in columns for `curve_boot()` tables was incorrect +* Corrected error where order of labels in columns for `curve_meta()` tables was incorrect +* Set minimum version of R to 3.5.0 +* included `install.packages("concurve", dep = TRUE)` as solution to installation problems for some individuals +* Removed `MASS`, `compiler`, and `Rlang` from Description Imports, since these weren't used + + # concurve 2.3.0 ## Major changes diff --git a/R/curve_boot.R b/R/curve_boot.R index afdbe97..313c9f1 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -7,7 +7,8 @@ #' @param func Custom function that is used to create parameters of interest that #' will be bootstrapped. #' @param method The boostrap method that will be used to generate the functions. -#' Methods include "bca" which is the default and "t". +#' Methods include "bca" which is the default, "bcapar", which is parametric +#' bootstrapping using the bca method and "t", for the t-bootstrap/percentile method. #' @param replicates Indicates how many bootstrap replicates are to be performed. #' The defaultis currently 20000 but more may be desirable, especially to make #' the functions more smooth. @@ -43,10 +44,10 @@ #' plot_compare(x[[1]], x[[3]]) #' } #' -curve_boot <- function(data = data, func = func, method = "bca", replicates = 2000, steps = 1000, table = TRUE) { +curve_boot <- function(data = data, func = func, method = "bca", ..., replicates = 2000, steps = 1000, table = TRUE) { - # BCA Bootstrap Method --------------------------------------------------- + # BCA Non-Parametric Bootstrap Method --------------------------------------------------- if (method == "bca") { intrvls <- 0.5 / steps @@ -61,6 +62,17 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 z$alphaperc <- as.numeric(z$alphaperc) 1:length(alpha) + + # Bootstrap Statistics ---------------------------------------------------- + + bootstats <- result[["stats"]] + bootstats <- as.data.frame(bootstats) + class(bootstats) <- c("data.frame", "concurve") + + bcastats <- result[["ustats"]] + bcastats <- as.data.frame(bcastats) + class(bcastats) <- c("data.frame", "concurve") + # Data Frame with BCA Intervals ------------------------------ bca <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), mc.cores = getOption("mc.cores", 1L)) @@ -74,12 +86,12 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" - df_bca <- data.frame(bcaintervals$lower.limit, bcaintervals$upper.limit, levels$intrvl.level, width$intrvl.width) - df_names <- c("lower.limit", "upper.limit", "intrvl.level", "intrvl.width") + df_bca <- data.frame(bcaintervals$lower.limit, bcaintervals$upper.limit, width$intrvl.width, levels$intrvl.level) + df_names <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level") colnames(df_bca) <- df_names + df_bca$cdf <- (abs(df_bca$intrvl.level / 2)) + 0.5 df_bca$pvalue <- 1 - df_bca$intrvl.level df_bca$svalue <- -log2(df_bca$pvalue) - df_bca$cdf <- (abs(df_bca$intrvl.level / 2)) + 0.5 df_bca <- head(df_bca, -1) df_bca <- df_bca[-1, ] class(df_bca) <- c("data.frame", "concurve") @@ -97,12 +109,12 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" - df_std <- data.frame(stdintervals$lower.limit, stdintervals$upper.limit, levels$intrvl.level, width$intrvl.width) - df_names <- c("lower.limit", "upper.limit", "intrvl.level", "intrvl.width") + df_std <- data.frame(stdintervals$lower.limit, stdintervals$upper.limit, width$intrvl.width, levels$intrvl.level) + df_names <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level") colnames(df_std) <- df_names + df_std$cdf <- (abs(df_std$intrvl.level / 2)) + 0.5 df_std$pvalue <- 1 - df_std$intrvl.level df_std$svalue <- -log2(df_std$pvalue) - df_std$cdf <- (abs(df_std$intrvl.level / 2)) + 0.5 df_std <- head(df_std, -1) df_std <- df_std[-1, ] class(df_std) <- c("data.frame", "concurve") @@ -116,13 +128,109 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 class(bca_subintervals) <- c("data.frame", "concurve") (std_subintervals <- (curve_table(df_std, levels, type = "c", format = "data.frame"))) class(std_subintervals) <- c("data.frame", "concurve") - dataframes <- list(df_std, std_subintervals, df_bca, bca_subintervals) - names(dataframes) <- c("Standard Intervals", "Standard Table", "BCA Intervals", "BCA Table") + dataframes <- list(df_std, std_subintervals, df_bca, bca_subintervals, bootstats, bcastats) + names(dataframes) <- c("Standard Intervals", "Standard Table", "BCA Intervals", "BCA Table", "Bootstrap Statistics", "BCA Statistics") + class(dataframes) <- "concurve" + return(dataframes) + } else if (table == FALSE) { + dataframes <- list(df_std, df_bca, bootstats, bcastats) + names(dataframes) <- c("Standard", "BCA", "Bootstrap Statistics", "BCA Statistics") + class(dataframes) <- "concurve" + return(dataframes) + } + + + # Parametric BCA Bootstrap Method ----------------------------------------- + } else if (method == "bcapar") { + intrvls <- 0.5 / steps + alpha <- seq(0.00, 0.50, intrvls) + + result <- bcapar(t0 = t0, tt = tt, bb = b.star, alpha = alpha, cd = 1) # > $call + + # Parametric Bootstrap Statistics ----------------------------------------- + + bootstats <- result[["stats"]] + bootstats <- as.data.frame(bootstats) + class(bootstats) <- c("data.frame", "concurve") + + bcastats <- result[["ustats"]] + bcastats <- as.data.frame(bcastats) + class(bcastats) <- c("data.frame", "concurve") + + + # Parametric BCA Bootstrap Density ---------------------------------------- + + densdf <- result[["w"]] + densdf <- as.data.frame(densdf) + class(densdf) <- c("data.frame", "concurve") + colnames(densdf) <- "x" + + z <- result[["lims"]] + z <- as.data.frame(z) + z <- as_tibble(rownames_to_column(z)) + colnames(z)[1] <- "alphaperc" + z$alphaperc <- as.numeric(z$alphaperc) + 1:length(alpha) + + bca <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), mc.cores = getOption("mc.cores", 1L)) + bcaintervals <- data.frame(do.call(rbind, bca)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(bcaintervals) <- intrvl.limit + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), mc.cores = getOption("mc.cores", 1L)) + width <- data.frame(do.call(rbind, news)) + colnames(width) <- "intrvl.width" + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = getOption("mc.cores", 1L)) + levels <- data.frame(do.call(rbind, bews)) + colnames(levels) <- "intrvl.level" + + df_bca <- data.frame(bcaintervals$lower.limit, bcaintervals$upper.limit, width$intrvl.width, levels$intrvl.level) + df_names <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level") + colnames(df_bca) <- df_names + df_bca$cdf <- (abs(df_bca$intrvl.level / 2)) + 0.5 + df_bca$pvalue <- 1 - df_bca$intrvl.level + df_bca$svalue <- -log2(df_bca$pvalue) + df_bca <- head(df_bca, -1) + df_bca <- df_bca[-1, ] + class(df_bca) <- c("data.frame", "concurve") + + # Data Frame with Standard Intervals ------------------------------ + + std <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), mc.cores = getOption("mc.cores", 1L)) + stdintervals <- data.frame(do.call(rbind, std)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(stdintervals) <- intrvl.limit + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), mc.cores = getOption("mc.cores", 1L)) + width <- data.frame(do.call(rbind, news)) + colnames(width) <- "intrvl.width" + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = getOption("mc.cores", 1L)) + levels <- data.frame(do.call(rbind, bews)) + colnames(levels) <- "intrvl.level" + + df_std <- data.frame(stdintervals$lower.limit, stdintervals$upper.limit, width$intrvl.width, levels$intrvl.level) + df_names <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level") + colnames(df_std) <- df_names + df_std$cdf <- (abs(df_std$intrvl.level / 2)) + 0.5 + df_std$pvalue <- 1 - df_std$intrvl.level + df_std$svalue <- -log2(df_std$pvalue) + df_std <- head(df_std, -1) + df_std <- df_std[-1, ] + class(df_std) <- c("data.frame", "concurve") + + # Combine Data Frames ----------------------------------------------------- + + if (table == TRUE) { + levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) + (bca_subintervals <- (curve_table(df_bca, levels, type = "c", format = "data.frame"))) + class(bca_subintervals) <- c("data.frame", "concurve") + (std_subintervals <- (curve_table(df_std, levels, type = "c", format = "data.frame"))) + class(std_subintervals) <- c("data.frame", "concurve") + dataframes <- list(df_std, std_subintervals, df_bca, bca_subintervals, densdf, bootstats, bcastats) + names(dataframes) <- c("Standard Intervals", "Standard Table", "BCA Intervals", "BCA Table", "BCA Density", "Bootstrap Statistics", "BCA Statistics") class(dataframes) <- "concurve" return(dataframes) } else if (table == FALSE) { - dataframes <- list(df_std, df_bca) - names(dataframes) <- c("Standard", "BCA") + dataframes <- list(df_std, df_bca, densdf, bootstats, bcastats) + names(dataframes) <- c("Standard", "BCA", "BCA Density", "Bootstrap Statistics", "BCA Statistics") class(dataframes) <- "concurve" return(dataframes) } diff --git a/R/curve_meta.R b/R/curve_meta.R index f560d50..8fdbb53 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -94,9 +94,6 @@ curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit - df$intrvl.level <- intrvls - df$pvalue <- 1 - intrvls - df$svalue <- -log2(df$pvalue) if (measure == "default") { df$lower.limit <- df$lower.limit df$upper.limit <- df$upper.limit @@ -105,7 +102,10 @@ curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { df$upper.limit <- exp(df$upper.limit) } df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) + df$intrvl.level <- intrvls df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 + df$pvalue <- 1 - intrvls + df$svalue <- -log2(df$pvalue) df <- head(df, -1) class(df) <- c("data.frame", "concurve") densdf <- data.frame(c(df$lower.limit, df$upper.limit)) diff --git a/README.Rmd b/README.Rmd index 43fbdb4..2af0aca 100644 --- a/README.Rmd +++ b/README.Rmd @@ -54,23 +54,23 @@ install.packages("concurve", dep = TRUE) ## Dependencies * `R` ≥ 3.5.0 -* `ggplot2` -* `metafor` -* `parallel` -* `boot` -* `bcaboot` -* `ProfileLikelihood` -* `pbmcapply` -* `dplyr` -* `tidyr` -* `knitr` -* `flextable` -* `officer` -* `tibble` -* `survival` -* `survminer` -* `scales` -* `methods` +* [`ggplot2`](https://ggplot2.tidyverse.org/) +* [`metafor`](http://www.metafor-project.org/doku.php/metafor) +* [`parallel`](https://cran.r-project.org/web/views/HighPerformanceComputing.html) +* [`boot`](https://cran.r-project.org/package=boot) +* [`bcaboot`](https://cran.r-project.org/package=bcaboot) +* [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) +* [`pbmcapply`](https://cran.r-project.org/package=pbmcapply) +* [`dplyr`](https://dplyr.tidyverse.org/) +* [`tidyr`](https://tidyr.tidyverse.org/) +* [`knitr`](https://yihui.org/knitr/) +* [`flextable`](https://davidgohel.github.io/flextable/) +* [`officer`](https://davidgohel.github.io/officer/) +* [`tibble`](https://tibble.tidyverse.org/) +* [`survival`](https://cran.r-project.org/package=survival) +* [`survminer`](https://cran.r-project.org/package=survminer) +* [`scales`](https://cran.r-project.org/package=scales) +* [`methods`]() * * * diff --git a/README.md b/README.md index 333522d..d86aa4a 100644 --- a/README.md +++ b/README.md @@ -62,24 +62,25 @@ issues: ## Dependencies - - `R` ≥ 3.5.0 - - `ggplot2` - - `metafor` - - `parallel` - - `boot` - - `bcaboot` - - `ProfileLikelihood` - - `pbmcapply` - - `dplyr` - - `tidyr` - - `knitr` - - `flextable` - - `officer` - - `tibble` - - `survival` - - `survminer` - - `scales` - - `methods` + - `R` ≥ + 3.5.0 + - [`ggplot2`](https://ggplot2.tidyverse.org/) + - [`metafor`](http://www.metafor-project.org/doku.php/metafor) + - [`parallel`](https://cran.r-project.org/web/views/HighPerformanceComputing.html) + - [`boot`](https://cran.r-project.org/package=boot) + - [`bcaboot`](https://cran.r-project.org/package=bcaboot) + - [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) + - [`pbmcapply`](https://cran.r-project.org/package=pbmcapply) + - [`dplyr`](https://dplyr.tidyverse.org/) + - [`tidyr`](https://tidyr.tidyverse.org/) + - [`knitr`](https://yihui.org/knitr/) + - [`flextable`](https://davidgohel.github.io/flextable/) + - [`officer`](https://davidgohel.github.io/officer/) + - [`tibble`](https://tibble.tidyverse.org/) + - [`survival`](https://cran.r-project.org/package=survival) + - [`survminer`](https://cran.r-project.org/package=survminer) + - [`scales`](https://cran.r-project.org/package=scales) + - [`methods`]() ----- diff --git a/codemeta.json b/codemeta.json index f23648c..51588a8 100644 --- a/codemeta.json +++ b/codemeta.json @@ -327,7 +327,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve", "confidence-intervals", "cargo-cult-statistics", "compatibility-intervals", "assumptions", "stata", "confidence-distribution", "likelihood-functions", "bootstrap-method", "r", "frequentist-methods", "fiducial-distribution"], - "fileSize": "4719.963KB", + "fileSize": "4720.662KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", @@ -337,7 +337,7 @@ "contributor": {}, "copyrightHolder": {}, "funder": {}, - "releaseNotes": "https://github.com/Zadchow/concurve/blob/master/NEWS.md", + "releaseNotes": "ssh://git@github.com/Zadchow/concurve/blob/master/NEWS.md", "citation": [ { "@type": "SoftwareSourceCode", diff --git a/docs/index.html b/docs/index.html index b9ac2a9..c348d37 100644 --- a/docs/index.html +++ b/docs/index.html @@ -152,23 +152,23 @@


diff --git a/examples/boot.R b/examples/boot.R deleted file mode 100644 index defa202..0000000 --- a/examples/boot.R +++ /dev/null @@ -1,73 +0,0 @@ -library(boot) -library(boot) -library(Lock5Data) -dataz <- data(CommuteAtlanta) - -func = function(data, index) { - x <- as.numeric(unlist(data[1])) - y <- as.numeric(unlist(data[2])) - return(mean(x[index]) - mean(y[index])) -} - -library(parallel) -detectCores() - -intervals <- 1:1000/1000 - -set.seed(1031) - -GroupA <- runif(100, min = 0, max = 100) -GroupB <- runif(100, min = 0, max = 100) - -RandomData <- data.frame(GroupA, GroupB) - -replicates <- 20000 - - -t.boot = boot(data = CommuteAtlanta, func, replicates, parallel = "multicore", ncpus = (detectCores())) - -intrvls <- 1:1000/1000 - -library(parallel) -library(pbapply) -library(microbenchmark) -library(boot) -library(concurve) - -microbenchmark( -t = mclapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], - mc.cores = detectCores()), -a = pblapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], - cl = detectCores())) - -df <- data.frame(do.call(rbind, t)) -intrvl.limit <- c("lower.limit", "upper.limit") -colnames(df) <- intrvl.limit -df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) -df$intrvl.level <- intrvls -df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 -df$pvalue <- 1 - intrvls -df$svalue <- -log2(df$pvalue) -df <- head(df, -1) -class(df) <- c("data.frame","concurve") - - -densdf <- data.frame(c(df$lower.limit, df$upper.limit)) -colnames(densdf) <- "x" -densdf <- head(densdf, -1) -class(densdf) <- c("data.frame","concurve") -plot(density(densdf$x)) - -if (table == TRUE) { - levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) - class(df_subintervals) <- c("data.frame","concurve") - dataframes <- list(df, densdf, df_subintervals) - names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") - class(dataframes) <- "concurve" - return(dataframes) - -} else if (table == FALSE) { - return(list(df, densdf)) -} - diff --git a/examples/examples.R b/examples/examples.R index 7b7aa36..679c380 100644 --- a/examples/examples.R +++ b/examples/examples.R @@ -116,6 +116,8 @@ plot_compare(data1 = lik1[[1]], data2 = lik2[[1]], type = "d", measure = "ratio" # Bootstrapping ----------------------------------------------------------- +# Nonparametric BCA Bootstrapping --------------------------------------------- + options(mc.cores = 8L) getOption("mc.cores", 2L) @@ -166,6 +168,29 @@ ggcurve(data = y[[3]]) plot_compare(y[[1]], y[[3]]) +# Parametric BCA Bootstrapping -------------------------------------------- + +data(diabetes, package = "bcaboot") +X <- diabetes$x +y <- scale(diabetes$y, center = TRUE, scale = FALSE) +lm.model <- lm(y ~ X - 1) +mu.hat <- lm.model$fitted.values +sigma.hat <- stats::sd(lm.model$residuals) +t0 <- summary(lm.model)$adj.r.squared +y.star <- sapply(mu.hat, rnorm, n = 1000, sd = sigma.hat) +tt <- apply(y.star, 1, function(y) summary(lm(y ~ X - 1))$adj.r.squared) +b.star <- y.star %*% X +set.seed(1234) + +replicates = 2000 +steps = 1000 +intrvls <- 0.5 / steps +alpha <- seq(0.00, 0.50, intrvls) + +df <- curve_boot(method = "bcapar", t0 = t0, tt = tt, bb = b.star) + + +# Percentile Bootstrapping ------------------------------------------------ library(Lock5Data) diff --git a/inst/WORDLIST b/inst/WORDLIST index fee2d03..1fc1352 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -17,10 +17,8 @@ dichotomization distrbutions docx doi -dplyr estiate eval -flextable forvalues frac functons @@ -31,7 +29,6 @@ Hjort insertedinto kendall KJ -knitr leq levelinto Lifecycle @@ -39,7 +36,6 @@ likelihoodm lm logarithmically lointerval -magrittr mathbf mathST metafor @@ -48,7 +44,6 @@ NL nullvalue ORCID paranthesis -pbmcapply pearson postfile powerpoint @@ -61,7 +56,6 @@ Pvalue Rdoc README relace -rlang Rothman Saltelli Scand @@ -73,11 +67,9 @@ Strawderman summarise surprisal Surprisal -survminer svalue Svalue tibble -tidyr topost Trafimow Twoway diff --git a/tests/spelling.R b/tests/spelling.R index 13f77d9..6713838 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,6 +1,3 @@ -if (requireNamespace("spelling", quietly = TRUE)) { - spelling::spell_check_test( - vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE - ) -} +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE) diff --git a/usethis.R b/usethis.R index 557b2ff..f4aa858 100644 --- a/usethis.R +++ b/usethis.R @@ -34,6 +34,8 @@ use_spell_check(vignettes = TRUE, lang = "en-US", error = FALSE) use_cran_comments(open = interactive()) use_tidy_style() use_revdep() +codemetar::write_codemeta() + check_rhub(pkg = ".", platforms = NULL, email = NULL, interactive = TRUE, build_args = NULL) From b90de4c97b4ae491b589d6e5e58ded93b6364de5 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Sun, 8 Dec 2019 07:53:37 -0500 Subject: [PATCH 2/5] update to curve_boot and NEWS --- codemeta.json | 2 +- docs/articles/examples.html | 62 +++++++++++++++++----------------- docs/news/index.html | 47 ++++++++++++++++++-------- docs/reference/curve_meta.html | 26 +++++++------- tests/spelling.R | 9 +++-- 5 files changed, 84 insertions(+), 62 deletions(-) diff --git a/codemeta.json b/codemeta.json index 51588a8..79d5be8 100644 --- a/codemeta.json +++ b/codemeta.json @@ -327,7 +327,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve", "confidence-intervals", "cargo-cult-statistics", "compatibility-intervals", "assumptions", "stata", "confidence-distribution", "likelihood-functions", "bootstrap-method", "r", "frequentist-methods", "fiducial-distribution"], - "fileSize": "4720.662KB", + "fileSize": "4720.65KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/docs/articles/examples.html b/docs/articles/examples.html index e0d8a29..1d028db 100644 --- a/docs/articles/examples.html +++ b/docs/articles/examples.html @@ -354,7 +354,7 @@

Upper Limit

Interval Width

Interval Level (%)

-

CDF

+

CDF

P-value

S-value (bits)

@@ -362,56 +362,56 @@

-0.142

-0.093

-

25

-

0.048

-

0.75

-

0.415

-

0.625

+

0.048

+

25

+

0.625

+

0.75

+

0.415

-0.169

-0.067

-

50

-

0.102

-

0.50

-

1.000

-

0.750

+

0.102

+

50

+

0.750

+

0.50

+

1.000

-0.205

-0.031

-

75

-

0.174

-

0.25

-

2.000

-

0.875

+

0.174

+

75

+

0.875

+

0.25

+

2.000

-0.214

-0.021

-

80

-

0.194

-

0.20

-

2.322

-

0.900

+

0.194

+

80

+

0.900

+

0.20

+

2.322

-0.266

0.031

-

95

-

0.296

-

0.05

-

4.322

-

0.975

+

0.296

+

95

+

0.975

+

0.05

+

4.322

-0.312

0.077

-

99

-

0.389

-

0.01

-

6.644

-

0.995

+

0.389

+

99

+

0.995

+

0.01

+

6.644

diff --git a/docs/news/index.html b/docs/news/index.html index 2eabf9b..585c231 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -136,15 +136,33 @@

Changelog

Source: NEWS.md -
+

-concurve 2.3.0 2019-12-04 +concurve 2.3.0.1 Unreleased

Major changes

  • +curve_boot() can utilize parametric BCa bootstrap methods to compute functions
  • +
  • Corrected error where order of labels in columns for curve_boot() tables was incorrect
  • +
  • Corrected error where order of labels in columns for curve_meta() tables was incorrect
  • +
  • Set minimum version of R to 3.5.0
  • +
  • included install.packages("concurve", dep = TRUE) as solution to installation problems for some individuals
  • +
  • Removed MASS, compiler, and Rlang from Description Imports, since these weren’t used
  • +
+
+
+
+

+concurve 2.3.0 2019-12-04 +

+
+

+Major changes

+
    +
  • ggconcurve() is now ggcurve().
  • ggcurve() plots confidence (consonance) distributions, densities, likelihood, and deviance functions.
  • @@ -175,9 +193,9 @@

    concurve 2.1.0 2019-09-19

    -
    +

    -Major changes

    +Major changes

diff --git a/docs/news/index.html b/docs/news/index.html index 585c231..6535929 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -78,7 +78,7 @@ concurve - 2.3.0.1 + 2.4.0 @@ -103,6 +103,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -136,21 +139,31 @@

    Changelog

    Source: NEWS.md -
    +

    -concurve 2.3.0.1 Unreleased +concurve 2.4.0 Unreleased

    Major changes

    • -curve_boot() can utilize parametric BCa bootstrap methods to compute functions
    • -
    • Corrected error where order of labels in columns for curve_boot() tables was incorrect
    • -
    • Corrected error where order of labels in columns for curve_meta() tables was incorrect
    • -
    • Set minimum version of R to 3.5.0
    • -
    • included install.packages("concurve", dep = TRUE) as solution to installation problems for some individuals
    • -
    • Removed MASS, compiler, and Rlang from Description Imports, since these weren’t used
    • +curve_boot() can utilize parametric Bca bootstrap methods to compute functions. +
    • Corrected error where order of labels in columns for curve_boot() tables was incorrect.
    • +
    • Corrected error where order of labels in columns for curve_meta() tables was incorrect.
    • +
    • Set minimum version of R to 3.5.0.
    • +
    • included install.packages("concurve", dep = TRUE) as solution to installation problems for some individuals.
    • +
    • Removed MASS, compiler, and Rlang from DESCRIPTION IMPORTS, since these weren’t used.
    • +
    • Wrote new unit tests examing the class of each of the objects created from the functions.
    • +
    +
    +
    +

    +Minor changes

    +
      +
    • +ggcurve() theme has been changed from theme_bw() to theme_minimal().
    • +
    • Several new examples in the “Examples in R” article.
    @@ -180,9 +193,9 @@

    curve_lik() produces likelihood functions by transforming the objects from the ProfileLikelihood package.

    -
    +

    -Minor changes

    +Minor changes
    • All functions now provide progress on how long it will take to complete the task.
    • Interval widths are now provided as measures of precision.
    • @@ -272,9 +285,9 @@

    • Now contains documentation for producing interval functons in Stata.
    -
    +

    -Minor changes

    +Minor changes
    • Default plots now contain grids, title, subtitle, and a caption.
    • Updated figures in README and the ‘Examples in R’ vignette/article.
    • @@ -287,7 +300,7 @@

      Contents

      @@ -123,6 +123,9 @@
    • Examples in R
    • +
    • + Troubleshooting +
    • Using Stata
    • diff --git a/docs/reference/curve_boot-1.png b/docs/reference/curve_boot-1.png deleted file mode 100644 index 80865c14ae7baec55c5e6a82fb173cf03c4c6cd4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119992 zcmeFZbyQUE`ZkV8BO)N6AgP3;sB}w7gXEAR%@EQdAs`|mC0zn4NDLq`bO_QdF?2UW z!w^Hg59fQ%_k27b&-(rQTkC!QV69mbo4ucXKlgpz*LB@HL`_AGh=7U!0|SFd{;Bjc z3=C`!42-KU@NmF8<8c|=7#LW$t)-;Ypi**D4z>y3 z^lG!xatnKgE-SqykRLhjY%s5${CVBi{t4Q{rUksMD|;<|twEsS>)w^{1s8 zUm1@MO`fOam&?+S(>v0=C|SxTd>3(L`buzu^<(A2Lnq7lc z(4O7TZ@r0WVsExg3{Kd2nU-hOn-p_Drpcb-%|Y#a%I?gJ$0bLRh~q>~OeGa&k!(pD zz{YWN>(ajF!{0ObF2CX1KhRqvI(H*uV}rL$xUH$Mx~cb$#x6L%Xb%Ud3ya{DX9g6iBrHv-At;l2E7Dg%F%P^$%gHW1N+1 z$Yb^99!l>t?;yTBf4@4S5`1jdga^U31j@QsNz7O!aHc6nta1Xc<2<~XfAjWx;|vQ* z>g69IyF#Uj-3@h(G3RY<#(~EbJ*8}B!D0av#Z;%^Fp2^5oxC6eQ!;U14t`8==G*k7 zqk+9f59JS{93Ng`^n5R(d=P!7n9OVoJ)&SMVa=vh{>%%osxb^7y*M{G(4 z8pAM->t%26cHTe1Hmp5uF)lJ#<=|H@o4cJ_rPFh4e2hGOc))&Y^L55=L1(Ajsa3D5 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zev^|lsCnO?6nKQ>WWy~OC=;(AS6CPR%kI-F%SZaenKN)+U|L2thFLv-Y>H-`MXuO# zl6~dkkICX(r_CgNsXhiDvax&ZwU&%u9}asnR~5Q$(SBlF@=B3{cjyO>fk>j|YDJ>s z@6czO1cmU{{wBDXg#hlF2@&$wRHaM0ni{4xzLUvPQ*f%It;#hXVa+bHE;@~QF%8!D zSNgnbc;?S*iRyteHPUZ-Xe|*u46WEFmp^r=LdmtO)0TkQpMiJA5Nv`DpW^@d3C1|8 zRmfpefwkY3-vHfNRG{Xu3NlE1pM@i!LC10sQ#dkC7i?9h9*CV%7wO@{IDd~OVMg*} z;W|kf&dq - @@ -80,7 +80,7 @@ concurve - 2.3.0.1 + 2.4.0

    @@ -105,6 +105,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -140,18 +143,12 @@

    Generate Consonance Functions via Bootstrapping

    -

    Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages +

    Use the Bca bootstrap method and the t-boostrap method from the bcaboot and boot packages to generate consonance distrbutions.

    -
    curve_boot(
    -  data = data,
    -  func = func,
    -  method = "bca",
    -  replicates = 2000,
    -  steps = 1000,
    -  table = TRUE
    -)
    +
    curve_boot(data = data, func = func, method = "bca", t0, tt, bb,
    +  replicates = 2000, steps = 1000, table = TRUE)

    Arguments

    @@ -168,7 +165,25 @@

    Arg

    +Methods include "bca" which is the default, "bcapar", which is parametric +bootstrapping using the bca method and "t", for the t-bootstrap/percentile method.

    + + + + + + + + + + + + @@ -195,12 +210,23 @@

    Arg

    Value

    -

    A list with the dataframe of values in the first list and the table -in the second if table = TRUE.

    +

    A list with 7 items where the dataframe of standard values is in the first +list and the table for it in the second if table = TRUE. The Bca intervals and table +are found in the third and fourth list. The values for the density function are in +the fifth object, while the Bca stats are in the sixth and seventh objects.

    +

    References

    + +

    Efron, B., and Tibshirani, R. J. (1994), An Introduction to the Bootstrap, CRC Press.

    +

    Efron, B., and Narasimhan, B. (2018), “The automatic construction of bootstrap confidence intervals,” 17.

    +

    Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: +Statistical Inference with Confidence Distributions, Cambridge University Press.

    +

    Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: +A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000.

    Examples

    # \donttest{ +if (FALSE) { data(diabetes, package = "bcaboot") Xy <- cbind(diabetes$x, diabetes$y) rfun <- function(Xy) { @@ -209,17 +235,21 @@

    Examp return(summary(lm(y ~ X))$adj.r.squared) } -x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 200, steps = 1000)

    #> | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 10% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 30% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 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    -ggcurve(data = x[[1]])
    ggcurve(data = x[[3]])
    -plot_compare(x[[1]], x[[3]])
    # } +x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 200, steps = 1000) -
    +ggcurve(data=x[[1]]) +ggcurve(data=x[[3]]) + +plot_compare(x[[1]], x[[3]]) +} +# } @@ -104,6 +104,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -133,7 +136,7 @@
    @@ -177,6 +180,14 @@

    Arg

    method

    The boostrap method that will be used to generate the functions. -Methods include "bca" which is the default and "t".

    t0

    Only used for the "bcapar" method. +Observed estimate of theta, usually by maximum likelihood.

    tt

    Only used for the "bcapar" method. +A vector of parametric bootstrap replications of theta of length B, +usually large, say B = 2000

    bb

    Only used for the "bcapar" method. +A B by p matrix of natural sufficient vectors, +where p is the dimension of the exponential family.

    replicates
    +

    Value

    + +

    Computes an AUC score and returns a plot that graphs two functions.

    +

    See also

    + +

    Examples

    # \donttest{ @@ -190,19 +201,19 @@

    Examp RandomData2 <- data.frame(GroupA2, GroupB2) model <- lm(GroupA2 ~ GroupB2, data = RandomData2) randomframe <- curve_gen(model, "GroupB2") -(curve_compare(intervalsdf[[1]], randomframe[[1]]))

    #> [1] "AUC = Area Under the Curve"
    #> [[1]] +curve_compare(intervalsdf[[1]], randomframe[[1]])
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] #> #> #> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| #> |-----:|-----:|----------:|---------------:|-----------------------------:| -#> | 0.317| 0.204| 0.043| 0.089| 0.098| +#> | 0.335| 0.211| 0.089| 19.496| 0.242| #> -#> [[2]]
    #>
    (curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] +#> [[2]]
    #>
    curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] #> #> #> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| #> |------:|-----:|----------:|---------------:|-----------------------------:| -#> | 12.935| 4.309| 4.265| 0.329| 0.489| +#> | 13.683| 4.462| 4.462| 32.611| 0.484| #> #> [[2]]
    #>
    # } @@ -212,6 +223,8 @@

    Examp

    Contents

    diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html index e790c94..4e346b6 100644 --- a/docs/reference/curve_corr.html +++ b/docs/reference/curve_corr.html @@ -6,7 +6,7 @@ -Computes Consonance Intervals for Correlations — curve_corr • concurve +Consonance Functions for Correlations — curve_corr • concurve @@ -44,7 +44,7 @@ - + concurve - 2.3.0.1 + 2.4.0
    @@ -107,6 +107,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -136,7 +139,7 @@
    @@ -192,6 +195,11 @@

    Arg +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    Examples

    @@ -201,22 +209,23 @@

    Examp tibble::tibble(joe[[1]])

    #> # A tibble: 10,000 x 1 #> `joe[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.0900 -0.0900 0 0 0.5 1 -#> 2 -0.0900 -0.0900 0.0000363 0.0001 0.500 1.000 -#> 3 -0.0900 -0.0900 0.0000725 0.0002 0.500 1.000 -#> 4 -0.0900 -0.0899 0.000109 0.000300 0.500 1.000 -#> 5 -0.0901 -0.0899 0.000145 0.0004 0.500 1.000 -#> 6 -0.0901 -0.0899 0.000181 0.0005 0.500 1.000 -#> 7 -0.0901 -0.0899 0.000218 0.000600 0.500 0.999 -#> 8 -0.0901 -0.0899 0.000254 0.0007 0.500 0.999 -#> 9 -0.0901 -0.0898 0.000290 0.0008 0.500 0.999 -#> 10 -0.0902 -0.0898 0.000326 0.0009 0.500 0.999 +#> 1 0.106 0.106 0 0 0.5 1 +#> 2 0.106 0.106 0.0000362 0.0001 0.500 1.000 +#> 3 0.106 0.106 0.0000723 0.0002 0.500 1.000 +#> 4 0.106 0.106 0.000108 0.000300 0.500 1.000 +#> 5 0.106 0.106 0.000145 0.0004 0.500 1.000 +#> 6 0.106 0.106 0.000181 0.0005 0.500 1.000 +#> 7 0.106 0.106 0.000217 0.000600 0.500 0.999 +#> 8 0.106 0.106 0.000253 0.0007 0.500 0.999 +#> 9 0.106 0.106 0.000289 0.0008 0.500 0.999 +#> 10 0.106 0.106 0.000325 0.0009 0.500 0.999 #> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    @@ -110,6 +108,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -139,8 +140,7 @@
    @@ -197,6 +197,11 @@

    Arg +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    Examples

    @@ -210,16 +215,16 @@

    Examp tibble::tibble(bob[[1]])

    #> # A tibble: 998 x 1 #> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.0312 0.0315 0.000336 0.001 0.500 0.999 -#> 2 0.0310 0.0317 0.000672 0.002 0.501 0.998 -#> 3 0.0309 0.0319 0.00101 0.003 0.501 0.997 -#> 4 0.0307 0.0320 0.00134 0.004 0.502 0.996 -#> 5 0.0305 0.0322 0.00168 0.005 0.502 0.995 -#> 6 0.0304 0.0324 0.00202 0.006 0.503 0.994 -#> 7 0.0302 0.0325 0.00235 0.007 0.503 0.993 -#> 8 0.0300 0.0327 0.00269 0.008 0.504 0.992 -#> 9 0.0299 0.0329 0.00302 0.009 0.504 0.991 -#> 10 0.0297 0.0331 0.00336 0.01 0.505 0.99 +#> 1 -0.00612 -0.00570 0.000421 0.001 0.500 0.999 +#> 2 -0.00633 -0.00549 0.000841 0.002 0.501 0.998 +#> 3 -0.00654 -0.00528 0.00126 0.003 0.501 0.997 +#> 4 -0.00675 -0.00507 0.00168 0.004 0.502 0.996 +#> 5 -0.00696 -0.00486 0.00210 0.005 0.502 0.995 +#> 6 -0.00717 -0.00465 0.00252 0.006 0.503 0.994 +#> 7 -0.00738 -0.00444 0.00294 0.007 0.503 0.993 +#> 8 -0.00759 -0.00423 0.00337 0.008 0.504 0.992 +#> 9 -0.00780 -0.00402 0.00379 0.009 0.504 0.991 +#> 10 -0.00801 -0.00381 0.00421 0.01 0.505 0.99 #> # … with 988 more rows, and 1 more variable: $svalue <dbl>
    # }
    @@ -228,6 +233,7 @@

    Examp

    Contents

    diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html index d4a8356..1734286 100644 --- a/docs/reference/curve_lik.html +++ b/docs/reference/curve_lik.html @@ -6,7 +6,7 @@ -Compute the Profile Likelihood Functions — curve_lik • concurve +Compute Profile Likelihood Functions — curve_lik • concurve @@ -44,8 +44,8 @@ - - + + @@ -79,7 +79,7 @@ concurve - 2.3.0.1 + 2.4.0
    @@ -104,6 +104,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -133,13 +136,13 @@
    -

    Compute the Profile Likelihood Functions

    +

    Compute Profile Likelihood Functions

    curve_lik(likobject, data, table = TRUE)
    @@ -164,6 +167,10 @@

    Arg +

    Value

    + +

    A list with 2 items where the dataframe of values is in the first +object, and the table for the values in the second if table = TRUE.

    Examples

    @@ -188,6 +195,7 @@

    Examp

    Contents

    diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html index d585dad..ec1427e 100644 --- a/docs/reference/curve_mean.html +++ b/docs/reference/curve_mean.html @@ -6,7 +6,7 @@ -Mean Interval Consonance Function — curve_mean • concurve +Consonance Functions For Mean Differences — curve_mean • concurve @@ -44,7 +44,7 @@ - + concurve - 2.3.0.1 + 2.4.0
    @@ -107,6 +107,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -136,7 +139,7 @@
    @@ -148,16 +151,8 @@

    Mean Interval Consonance Function

    p-values and s-values.

    -
    curve_mean(
    -  x,
    -  y,
    -  data,
    -  paired = F,
    -  method = "default",
    -  replicates = 1000,
    -  steps = 10000,
    -  table = TRUE
    -)
    +
    curve_mean(x, y, data, paired = F, method = "default", replicates = 1000,
    +  steps = 10000, table = TRUE)

    Arguments

    @@ -209,6 +204,11 @@

    Arg

    +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    Examples

    @@ -220,22 +220,23 @@

    Examp tibble::tibble(bob[[1]])

    #> # A tibble: 10,000 x 1 #> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 5.15 5.15 0 0 0.5 1 -#> 2 5.15 5.15 0.00103 0.0001 0.500 1.000 -#> 3 5.15 5.15 0.00206 0.0002 0.500 1.000 -#> 4 5.15 5.15 0.00308 0.000300 0.500 1.000 -#> 5 5.15 5.15 0.00411 0.0004 0.500 1.000 -#> 6 5.15 5.15 0.00514 0.0005 0.500 1.000 -#> 7 5.15 5.15 0.00617 0.000600 0.500 0.999 -#> 8 5.15 5.15 0.00719 0.0007 0.500 0.999 -#> 9 5.15 5.15 0.00822 0.0008 0.500 0.999 -#> 10 5.15 5.15 0.00925 0.0009 0.500 0.999 +#> 1 -1.11 -1.11 0 0 0.5 1 +#> 2 -1.11 -1.11 0.00104 0.0001 0.500 1.000 +#> 3 -1.11 -1.11 0.00208 0.0002 0.500 1.000 +#> 4 -1.11 -1.11 0.00312 0.000300 0.500 1.000 +#> 5 -1.11 -1.11 0.00417 0.0004 0.500 1.000 +#> 6 -1.11 -1.11 0.00521 0.0005 0.500 1.000 +#> 7 -1.11 -1.11 0.00625 0.000600 0.500 0.999 +#> 8 -1.11 -1.11 0.00729 0.0007 0.500 0.999 +#> 9 -1.11 -1.11 0.00833 0.0008 0.500 0.999 +#> 10 -1.11 -1.11 0.00937 0.0009 0.500 0.999 #> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    @@ -107,6 +107,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -136,7 +139,7 @@
    @@ -183,6 +186,11 @@

    Arg +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    Examples

    @@ -242,22 +250,23 @@

    Examp tibble::tibble(metaf[[1]])

    #> # A tibble: 10,000 x 1 #> `metaf[[1]]`$lo… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.0617 0.0617 0 0 0.5 1 -#> 2 0.0616 0.0617 0.0000562 0.0001 0.500 1.000 -#> 3 0.0616 0.0617 0.000112 0.0002 0.500 1.000 -#> 4 0.0616 0.0617 0.000169 0.000300 0.500 1.000 -#> 5 0.0615 0.0618 0.000225 0.0004 0.500 1.000 -#> 6 0.0615 0.0618 0.000281 0.0005 0.500 1.000 -#> 7 0.0615 0.0618 0.000337 0.000600 0.500 0.999 -#> 8 0.0615 0.0619 0.000394 0.0007 0.500 0.999 -#> 9 0.0614 0.0619 0.000450 0.0008 0.500 0.999 -#> 10 0.0614 0.0619 0.000506 0.0009 0.500 0.999 +#> 1 -0.249 -0.249 0 0 0.5 1 +#> 2 -0.249 -0.249 0.0000565 0.0001 0.500 1.000 +#> 3 -0.249 -0.249 0.000113 0.0002 0.500 1.000 +#> 4 -0.249 -0.249 0.000170 0.000300 0.500 1.000 +#> 5 -0.249 -0.249 0.000226 0.0004 0.500 1.000 +#> 6 -0.249 -0.249 0.000283 0.0005 0.500 1.000 +#> 7 -0.249 -0.249 0.000339 0.000600 0.500 0.999 +#> 8 -0.249 -0.249 0.000396 0.0007 0.500 0.999 +#> 9 -0.249 -0.249 0.000452 0.0008 0.500 0.999 +#> 10 -0.249 -0.249 0.000509 0.0009 0.500 0.999 #> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    @@ -108,6 +109,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -146,18 +150,12 @@

    Reverse Engineer Consonance / Likelihood Functions Using the Point

    Using the confidence limits and point estimates from a dataset, one can use these estimates to compute thousands of consonance intervals and graph the -intervals to form a consonance and surprisal function.

    +intervals to form a consonance and surprisal function. The intervals are +calculated from the approximated normal distribution.

    -
    curve_rev(
    -  point,
    -  LL,
    -  UL,
    -  type = "c",
    -  measure = "default",
    -  steps = 10000,
    -  table = TRUE
    -)
    +
    curve_rev(point, LL, UL, type = "c", measure = "default", steps = 10000,
    +  table = TRUE)

    Arguments

    @@ -201,6 +199,16 @@

    Arg

    +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    +

    See also

    + +

    Examples

    @@ -228,6 +236,8 @@

    Examp

    Contents

    diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html index 23e0009..b3aeaac 100644 --- a/docs/reference/curve_surv.html +++ b/docs/reference/curve_surv.html @@ -6,7 +6,7 @@ -Produce Consonance Intervals for Survival Data — curve_surv • concurve +Consonance Functions For Survival Data — curve_surv • concurve @@ -44,7 +44,7 @@ - + concurve - 2.3.0.1 + 2.4.0
    @@ -107,6 +107,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -136,7 +139,7 @@
    @@ -180,12 +183,49 @@

    Arg - +

    Value

    + +

    A list with 3 items where the dataframe of values is in the first +object, the values needed to calculate the density function in the second, +and the table for the values in the third if table = TRUE.

    + +

    Examples

    +
    + +library(carData) +Rossi[1:5, 1:10]
    #> week arrest fin age race wexp mar paro prio educ +#> 1 20 1 no 27 black no not married yes 3 3 +#> 2 17 1 no 18 black no not married yes 8 4 +#> 3 25 1 no 19 other yes not married yes 13 3 +#> 4 52 0 yes 23 black yes married yes 1 5 +#> 5 52 0 no 19 other yes not married yes 3 3
    library(survival) + +mod.allison <- coxph(Surv(week, arrest) ~ fin + age + race + wexp + mar + paro + prio, + data = Rossi +) +mod.allison
    #> Call: +#> coxph(formula = Surv(week, arrest) ~ fin + age + race + wexp + +#> mar + paro + prio, data = Rossi) +#> +#> coef exp(coef) se(coef) z p +#> finyes -0.37942 0.68426 0.19138 -1.983 0.04742 +#> age -0.05744 0.94418 0.02200 -2.611 0.00903 +#> raceother -0.31390 0.73059 0.30799 -1.019 0.30812 +#> wexpyes -0.14980 0.86088 0.21222 -0.706 0.48029 +#> marnot married 0.43370 1.54296 0.38187 1.136 0.25606 +#> paroyes -0.08487 0.91863 0.19576 -0.434 0.66461 +#> prio 0.09150 1.09581 0.02865 3.194 0.00140 +#> +#> Likelihood ratio test=33.27 on 7 df, p=2.362e-05 +#> n= 432, number of events= 114
    +z <- curve_surv(mod.allison, "prio")
    diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html index c08aad7..eeeb6d7 100644 --- a/docs/reference/curve_table.html +++ b/docs/reference/curve_table.html @@ -80,7 +80,7 @@ concurve - 2.3.0.1 + 2.4.0
    @@ -105,6 +105,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -172,6 +175,11 @@

    Arg +

    See also

    + +

    Examples

    @@ -185,15 +193,15 @@

    Examp intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (z <- curve_table(intervalsdf[[1]], format = "data.frame"))

    #> Lower Limit Upper Limit Interval Width Interval Level (%) CDF P-value -#> 2501 0.131 0.172 0.041 25.0 0.625 0.750 -#> 5001 0.108 0.195 0.088 50.0 0.750 0.500 -#> 7501 0.077 0.226 0.149 75.0 0.875 0.250 -#> 8001 0.068 0.235 0.166 80.0 0.900 0.200 -#> 8501 0.058 0.245 0.187 85.0 0.925 0.150 -#> 9001 0.045 0.258 0.214 90.0 0.950 0.100 -#> 9501 0.024 0.279 0.255 95.0 0.975 0.050 -#> 9751 0.006 0.297 0.291 97.5 0.988 0.025 -#> 9901 -0.016 0.319 0.335 99.0 0.995 0.010 +#> 2501 -0.015 0.025 0.039 25.0 0.625 0.750 +#> 5001 -0.037 0.046 0.083 50.0 0.750 0.500 +#> 7501 -0.066 0.076 0.142 75.0 0.875 0.250 +#> 8001 -0.074 0.084 0.158 80.0 0.900 0.200 +#> 8501 -0.084 0.094 0.177 85.0 0.925 0.150 +#> 9001 -0.096 0.106 0.203 90.0 0.950 0.100 +#> 9501 -0.116 0.126 0.241 95.0 0.975 0.050 +#> 9751 -0.133 0.143 0.276 97.5 0.988 0.025 +#> 9901 -0.154 0.164 0.318 99.0 0.995 0.010 #> S-value (bits) #> 2501 0.415 #> 5001 1.000 @@ -206,33 +214,50 @@

    Examp #> 9901 6.644

    (z <- curve_table(intervalsdf[[1]], format = "tibble"))
    #> # A tibble: 9 x 1 #> subdf$`Lower Li… $`Upper Limit` $`Interval Widt… $`Interval Leve… $CDF #> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.131 0.172 0.041 25 0.625 -#> 2 0.108 0.195 0.088 50 0.75 -#> 3 0.077 0.226 0.149 75 0.875 -#> 4 0.068 0.235 0.166 80 0.9 -#> 5 0.058 0.245 0.187 85 0.925 -#> 6 0.045 0.258 0.214 90 0.95 -#> 7 0.024 0.279 0.255 95 0.975 -#> 8 0.006 0.297 0.291 97.5 0.988 -#> 9 -0.016 0.319 0.335 99 0.995 +#> 1 -0.015 0.025 0.039 25 0.625 +#> 2 -0.037 0.046 0.083 50 0.75 +#> 3 -0.066 0.076 0.142 75 0.875 +#> 4 -0.074 0.084 0.158 80 0.9 +#> 5 -0.084 0.094 0.177 85 0.925 +#> 6 -0.096 0.106 0.203 90 0.95 +#> 7 -0.116 0.126 0.241 95 0.975 +#> 8 -0.133 0.143 0.276 97.5 0.988 +#> 9 -0.154 0.164 0.318 99 0.995 #> # … with 2 more variables: $`P-value` <dbl>, $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf[[1]], format = "latex"))
    #> #> #> | | Lower Limit| Upper Limit| Interval Width| Interval Level (%)| CDF| P-value| S-value (bits)| #> |:----|-----------:|-----------:|--------------:|------------------:|-----:|-------:|--------------:| -#> |2501 | 0.131| 0.172| 0.041| 25.0| 0.625| 0.750| 0.415| -#> |5001 | 0.108| 0.195| 0.088| 50.0| 0.750| 0.500| 1.000| -#> |7501 | 0.077| 0.226| 0.149| 75.0| 0.875| 0.250| 2.000| -#> |8001 | 0.068| 0.235| 0.166| 80.0| 0.900| 0.200| 2.322| -#> |8501 | 0.058| 0.245| 0.187| 85.0| 0.925| 0.150| 2.737| -#> |9001 | 0.045| 0.258| 0.214| 90.0| 0.950| 0.100| 3.322| -#> |9501 | 0.024| 0.279| 0.255| 95.0| 0.975| 0.050| 4.322| -#> |9751 | 0.006| 0.297| 0.291| 97.5| 0.988| 0.025| 5.322| -#> |9901 | -0.016| 0.319| 0.335| 99.0| 0.995| 0.010| 6.644|
    +#> |2501 | -0.015| 0.025| 0.039| 25.0| 0.625| 0.750| 0.415| +#> |5001 | -0.037| 0.046| 0.083| 50.0| 0.750| 0.500| 1.000| +#> |7501 | -0.066| 0.076| 0.142| 75.0| 0.875| 0.250| 2.000| +#> |8001 | -0.074| 0.084| 0.158| 80.0| 0.900| 0.200| 2.322| +#> |8501 | -0.084| 0.094| 0.177| 85.0| 0.925| 0.150| 2.737| +#> |9001 | -0.096| 0.106| 0.203| 90.0| 0.950| 0.100| 3.322| +#> |9501 | -0.116| 0.126| 0.241| 95.0| 0.975| 0.050| 4.322| +#> |9751 | -0.133| 0.143| 0.276| 97.5| 0.988| 0.025| 5.322| +#> |9901 | -0.154| 0.164| 0.318| 99.0| 0.995| 0.010| 6.644|
    (z <- curve_table(intervalsdf[[1]], format = "image"))
    #> a flextable object. +#> col_keys: `Lower Limit`, `Upper Limit`, `Interval Width`, `Interval Level (%)`, `CDF`, `P-value`, `S-value (bits)` +#> header has 1 row(s) +#> body has 9 row(s) +#> original dataset sample: +#> Lower Limit Upper Limit Interval Width Interval Level (%) CDF P-value +#> 2501 -0.015 0.025 0.039 25 0.625 0.75 +#> 5001 -0.037 0.046 0.083 50 0.750 0.50 +#> 7501 -0.066 0.076 0.142 75 0.875 0.25 +#> 8001 -0.074 0.084 0.158 80 0.900 0.20 +#> 8501 -0.084 0.094 0.177 85 0.925 0.15 +#> S-value (bits) +#> 2501 0.415 +#> 5001 1.000 +#> 7501 2.000 +#> 8001 2.322 +#> 8501 2.737
    @@ -104,6 +104,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
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zKQi~9j>0-pcv}s6$bvuZ?;-v=KLx(MVBRJXC!Aj?8yTW zJj}8!07bF<$wu7TGN`C`F}BvZ;SGL6TWp6=!Gxwfar~3lc)(?Q`)$Hm6w^;ngj6I9 zH>mG7zV~~(L29n3GW5nZ9tqFw{(n!1&Yre_(w#A`9Ng%z@u6;i%%~2r!os3txZS8F zRE3lcUV#1@zfS=UJD7imPRgD$`CdPR8sR_YObBv#28~yas)a9vYj_Xs);n$J?F_?t zl;9$4CN_*TEn7-#&qilkV-S`aL93JCN@-@JHfN>zkte6<(tkz+U37>Z1)@h{k&>O33LoI;?8m zDsK5(Uq?k9gLtca9)Wq}-=F>GPf_tyq1m|awO#-D;qMn125zZPyyhJDm;D)&@;?Jb z1{?SSIcep#f14=2D=sc{_P4KL;9q6A;*6^!T*a}xb4e_^-KRYlTAMkS2#xf oPWx -Plots the P-Value (Consonance), S-value (Surprisal), -and Likelihood Function via ggplot2 — ggcurve • concurve +Plots Consonance, Surprisal, and Likelihood Functions — ggcurve • concurve @@ -45,8 +44,7 @@ - + concurve - 2.3.0.1 + 2.4.0
    @@ -109,6 +107,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -138,8 +139,7 @@
    @@ -151,20 +151,12 @@

    Plots the P-Value (Consonance), S-value (Surprisal), using ggplot2 graphics.

    -
    ggcurve(
    -  data,
    -  type = "c",
    -  measure = "default",
    -  levels = 0.95,
    -  nullvalue = FALSE,
    -  position = "pyramid",
    -  title = "Interval Function",
    +    
    ggcurve(data, type = "c", measure = "default", levels = 0.95,
    +  nullvalue = FALSE, position = "pyramid", title = "Interval Function",
       subtitle = "The function displays intervals at every level.",
       xaxis = expression(Theta ~ "Range of Values"),
    -  yaxis = "P-value",
    -  color = "#000000",
    -  fill = "#239a98"
    -)
    + yaxis = expression(paste(italic(p), "-value")), color = "#000000", + fill = "#239a98")

    Arguments

    @@ -264,8 +256,27 @@

    Arg

    Value

    -

    Plot with intervals at every consonance level graphed with their corresponding +

    A plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels.

    +

    References

    + +

    Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. https://doi.org/10.1214/aoms/1177705145.

    +

    Chow, Z. R., and Greenland, S. (2019), “Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise,” arXiv:1909.08579.

    +

    Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. https://doi.org/10.1146/annurev-statistics-060116-054139.

    +

    Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. https://doi.org/10.1080/00031305.2018.1556735.

    +

    Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. https://doi.org/10.1016/j.jspi.2017.09.017.

    +

    Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. https://doi.org/10.1002/sim.8293.

    +

    Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. https://doi.org/10.2105/AJPH.77.2.195.

    +

    Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. https://doi.org/10.2105/ajph.77.4.492.

    +

    Rothman, K. J., Greenland, S., and Lash, T. L. (2008a), Modern Epidemiology, Lippincott Williams & Wilkins.

    +

    Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. Lash, Lippincott Williams & Wilkins, pp. 148–167.

    +

    Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. https://doi.org/10.1111/1467-9469.00285.

    +

    Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions, Cambridge University Press.

    +

    Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. https://doi.org/10.1097/00001648-199001000-00009.

    +

    Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000.

    +

    See also

    + +

    Examples

    @@ -279,13 +290,15 @@

    Examp RandomData <- data.frame(GroupA, GroupB) intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") -(function1 <- ggcurve(type = "c", intervalsdf[[1]]))

    +ggcurve(type="c", intervalsdf[[1]], nullvalue=TRUE)
    @@ -103,6 +103,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -161,38 +164,37 @@

    curve_compare()

    -

    + - + - + - + - + - + - + - + - +

    Compares two functions and produces an AUC score to show the amount of consonance.

    Compare Two Functions and Produces An AUC Score

    curve_corr()

    Computes Consonance Intervals for Correlations

    Consonance Functions for Correlations

    curve_gen()

    General Consonance Functions Using Profile Likelihood, Wald, -or the bootstrap method for linear models.

    Consonance Functions For Linear Models.

    curve_lik()

    Compute the Profile Likelihood Functions

    Compute Profile Likelihood Functions

    curve_mean()

    Mean Interval Consonance Function

    Consonance Functions For Mean Differences

    curve_meta()

    Meta-analytic Consonance Function

    Consonance Functions For Meta-Analytic Data

    @@ -205,7 +207,7 @@

    curve_surv()

    Produce Consonance Intervals for Survival Data

    Consonance Functions For Survival Data

    @@ -217,14 +219,13 @@

    ggcurve()

    Plots the P-Value (Consonance), S-value (Surprisal), -and Likelihood Function via ggplot2

    Plots Consonance, Surprisal, and Likelihood Functions

    plot_compare()

    Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2

    Graph and Compare Consonance, Surprisal, and Likelihood Functions

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zg<-@BtM4CB;^ccbL#t9SphBVwT~8b{773H9Cb48of2Ea5_7~b1MZy3OHkHiskem%^ z?lv-DJ{em(`s)LAVz&gvE$^LFL;Lm45n#$z>lY?_&w;*-Ataia0A}_We2n@TfOyOI zeyxckSnL`W-%hQ9rfR3p3Hm4aIOG?3$i3t)pGJ3p_iB6bJN`ro>($ZfrYcCaw ztoL50P2oRl2fZk>OVDmQ0eOMCgBJMW=@@)Hif5^v_9eP~*m^RopVlJRS<1MoM#6g+ zC!i{%6{e!t;DgTGTmE=`W9AY0MH#9{w4vg0UFgzl2iy6(R5bE&QBE+sgYuyl~3j!K;)oI3zOV;>RD#5LOdC>8r-u}PSS+z}r_m67~8c`&FH zl`e-J_u4p%iAg1Gk%(Qi8D`Oqdb~?=&z-VnBSwcz#l%xiLAQaML5P z$Z6nIV#n9#>}36653kN-R;0q#N}%C5EMG{fyJ46a47jGBKQKBZ!ZaS`fT~G#MG(Y# zRo8OO{4}Dbw){>DNIVaMZ@?YrZ!rZ~oIn2|wi##F)o@U9I{M{b2$+OP2vd2y1H#>r zqrkefyw0bZkYaA*<7}?nbEC^gYAfr|n;}764QcEVGrj6&jzb_4arM@}dikifz;-%^ zdp!11m)*eTb&rC=(2opTvS(c&Su|3V>310YLvRoZ(oJ!aZ}{;49^?`}P=+fCL8AX; zCrlbo;^>G~OHMW_0_Qgo1bCDV0Zz+djtSS3Xb1tdB3u2&-+z04Lpa!o=nPL?G*G5e zM@(QsI)qi4?RjPcsau(U%N%eG2y!1@xa|Mulw_B&Se4VWA<>G!Dg|IZ+j*_81DeSm zF-BPjMOjo*Di!gxsGZ!A{)u(?h3<4n3HB>*K-_^1`*W~^2*`jCBA|bLV1J(kyn3zz z5A?)=c z_hE$G4b`_hTf0^(|Kr;eC|KK8@U5aCa^-{o%aeH|I7Oi;QgCVmMHlD zEW&@e&mp -Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 — plot_compare • concurve +Graph and Compare Consonance, Surprisal, and Likelihood Functions — plot_compare • concurve @@ -44,7 +44,7 @@ - + @@ -79,7 +79,7 @@ concurve - 2.3.0.1 + 2.4.0

    @@ -104,6 +104,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • @@ -133,7 +136,7 @@
    @@ -142,21 +145,12 @@

    Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Funct

    Compares the p-value/s-value, and likelihood functions using ggplot2 graphics.

    -
    plot_compare(
    -  data1,
    -  data2,
    -  type = "c",
    -  measure = "default",
    -  nullvalue = FALSE,
    -  position = "pyramid",
    -  title = "Interval Functions",
    +    
    plot_compare(data1, data2, type = "c", measure = "default",
    +  nullvalue = FALSE, position = "pyramid", title = "Interval Functions",
       subtitle = "The function displays intervals at every level.",
       xaxis = expression(Theta ~ "Range of Values"),
    -  yaxis = "P-value",
    -  color = "#000000",
    -  fill1 = "#239a98",
    -  fill2 = "#d46c5b"
    -)
    + yaxis = expression(paste(italic(p), "-value")), color = "#000000", + fill1 = "#239a98", fill2 = "#EE6A50")

    Arguments

    @@ -261,6 +255,26 @@

    Arg

    Value

    A plot that compares two functions.

    +

    References

    + +

    Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. https://doi.org/10.1214/aoms/1177705145.

    +

    Chow, Z. R., and Greenland, S. (2019), “Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise,” arXiv:1909.08579.

    +

    Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. https://doi.org/10.1146/annurev-statistics-060116-054139.

    +

    Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. https://doi.org/10.1080/00031305.2018.1556735.

    +

    Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. https://doi.org/10.1016/j.jspi.2017.09.017.

    +

    Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. https://doi.org/10.1002/sim.8293.

    +

    Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. https://doi.org/10.2105/AJPH.77.2.195.

    +

    Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. https://doi.org/10.2105/ajph.77.4.492.

    +

    Rothman, K. J., Greenland, S., and Lash, T. L. (2008a), Modern Epidemiology, Lippincott Williams & Wilkins.

    +

    Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. Lash, Lippincott Williams & Wilkins, pp. 148–167.

    +

    Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. https://doi.org/10.1111/1467-9469.00285.

    +

    Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions, Cambridge University Press.

    +

    Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. https://doi.org/10.1097/00001648-199001000-00009.

    +

    Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000.

    +

    See also

    + +

    Examples

    # \donttest{ @@ -277,7 +291,7 @@

    Examp randomframe <- curve_gen(model, "GroupB2") -(plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))

    # } +plot_compare(intervalsdf[[1]], randomframe[[1]], type = "c")
    # }
    @@ -286,6 +300,8 @@

    Contents

    diff --git a/docs/reference/tidyeval.html b/docs/reference/tidyeval.html index 13d785d..90aaca6 100644 --- a/docs/reference/tidyeval.html +++ b/docs/reference/tidyeval.html @@ -111,7 +111,7 @@ concurve - 2.3.0.1 + 2.4.0 @@ -136,6 +136,9 @@
  • Examples in R
  • +
  • + Troubleshooting +
  • Using Stata
  • diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 9c86710..29624ed 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -54,4 +54,7 @@ https://data.lesslikely.com/concurve//articles/stata.html + + https://data.lesslikely.com/concurve//articles/troubleshooting.html + diff --git a/examples/examples.R b/examples/examples.R index 679c380..c577a3b 100644 --- a/examples/examples.R +++ b/examples/examples.R @@ -5,9 +5,8 @@ GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -(intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -)) +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default") tibble::tibble(intervalsdf[[1]]) @@ -19,9 +18,10 @@ tibble::tibble(intervalsdf[[1]]) (z <- curve_table(intervalsdf[[1]], format = "latex")) (z <- curve_table(intervalsdf[[1]], format = "image")) -(function1 <- ggcurve(data = intervalsdf[[1]], type = "c")) +(function1 <- ggcurve(data = intervalsdf[[1]], type = "c", nullvalue = TRUE)) + (function1 <- ggcurve(data = intervalsdf[[1]], type = "s")) -(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf")) +(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf", nullvalue = TRUE)) options(mc.cores = 8L) @@ -32,9 +32,9 @@ GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, data = RandomData2) +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) -system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)) +system.time(randomframe <- curve_gen(model, "GroupB2")) options(mc.cores = 2L) @@ -43,9 +43,9 @@ GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, data = RandomData2) +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) -system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)) +system.time(randomframe <- curve_gen(model, "GroupB2")) (function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) @@ -60,7 +60,7 @@ system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1 plot = TRUE, measure = "default", nullvalue = FALSE )) -(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) +(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", measure = "default", nullvalue = TRUE)) (df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)) ## may take some time @@ -88,11 +88,11 @@ system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1 lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000) -(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) +(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000) -(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) +(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) (plot_compare( @@ -132,6 +132,10 @@ rfun <- function(Xy) { system.time(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 20000, steps = 1000)) +x[[5]] +x[[6]] + + options(mc.cores = 2L) getOption("mc.cores", 2L) @@ -189,6 +193,9 @@ alpha <- seq(0.00, 0.50, intrvls) df <- curve_boot(method = "bcapar", t0 = t0, tt = tt, bb = b.star) +ggcurve(df[[1]]) +ggcurve(df[[3]]) +ggcurve(df[[5]], type = "cd") # Percentile Bootstrapping ------------------------------------------------ diff --git a/inst/CITATION b/inst/CITATION index 949e0ff..97e0bc8 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,11 +1,11 @@ -citHeader("To cite concurve in publications use:") +year <- sub("-.*", "", meta$Date) +note <- sprintf("R package version %s", meta$Version) -citEntry( - entry = "Manual", +bibentry( + bibtype = "Manual", title = paste("{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions"), author = c(person("Zad R.", "Chow"), person("Andrew D.", "Vigotsky")), - year = 2019, - url = "https://CRAN.R-project.org/package=concurve", - textVersion = paste( - ) + year = year, + note = note, + url = "https://CRAN.R-project.org/package=concurve" ) diff --git a/inst/WORDLIST b/inst/WORDLIST index 1fc1352..a391507 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,15 +1,25 @@ +ai +ajph AJPH +al Amrhein analysesusing ANCOVA +annurev +aoms arXiv bca -BCa +Bca bcaboot +bcapar +Birnbaum boostrap cd -cdf +ci +confidence +CRC DAS +dat databrowser defaultis df @@ -17,22 +27,34 @@ dichotomization distrbutions docx doi +Efron +emp +epidemiologic estiate +et eval +examing forvalues frac functons ggcurve ggplot glm +hine Hjort +https +Infanger insertedinto +insr +jacknife +jspi kendall KJ leq levelinto +lidocaine Lifecycle -likelihoodm +Lippincott lm logarithmically lointerval @@ -40,22 +62,29 @@ mathbf mathST metafor Metaprogramming +Narasimhan NL nullvalue ORCID +parallelization paranthesis +paro pearson postfile powerpoint Powerpoint ppt pptx +preloaded +prio ProfileLikelihood pvalue Pvalue Rdoc README relace +resinstalling +Rossi Rothman Saltelli Scand @@ -70,14 +99,19 @@ Surprisal svalue Svalue tibble +Tibshirani topost Trafimow +Trucksäss Twoway typicallywhat upinterval wald +Weisberg +wexp widehat xaxis Xie +yi yxis Zad diff --git a/man/.DS_Store b/man/.DS_Store index dba8243b5dab548d83a0c6985347a19df319498a..1ba2d91c4d0b7d042f1a62fc7d2da2a29081667a 100644 GIT binary patch delta 90 zcmZp1XmQx!Ajr)0Rd%wXpz!7}K^s=~? ```{r setup, include = FALSE} knitr::opts_chunk$set( - message = TRUE, - warning = TRUE, - collapse = TRUE, - comment = "#>" + message = TRUE, + warning = TRUE, + collapse = TRUE, + comment = "#>" ) ``` @@ -27,6 +27,8 @@ Here I show how to produce _P_-value, _S_-value, likelihood, and deviance functi For a more extensive discussion of these concepts, see the following references. [@birnbaum1961ams; @chow2019asb; @fraser2017arsa; @fraser2019as; @Poole1987-nb; @poole1987ajph; @Schweder2002-vh; @schweder2016; @Singh2007-zr; @Sullivan1990-ha; @whitehead1993sm; @xie2013isr; @rothman2008me] +# Simple Models + To get started, we could generate some normal data and combine two vectors in a dataframe ```{r echo=TRUE, fig.height=4.5, fig.width=6} @@ -51,30 +53,30 @@ Each of the functions within `concurve` will generally produce a list with three tibble::tibble(intervalsdf[[1]]) ``` -We can view the function using the `ggcurve()` function. The two basic arguments that must be provided are the data argument and the "type" argument. To plot a consonance function, we would write "c". +We can view the function using the `ggcurve()` function. The two basic arguments that must be provided are the data argument and the "type" argument. To plot a consonance function, we would write "`c`". ```{r echo=TRUE, fig.height=4.5, fig.width=6} -(function1 <- ggcurve(data = intervalsdf[[1]], type = "c")) +(function1 <- ggcurve(data = intervalsdf[[1]], type = "c", nullvalue = TRUE)) ``` -We can see that the consonance "curve" is every interval estimate plotted, and provides the _P_-values, CIs, along with the median unbiased estimate It can be defined as such, +We can see that the consonance "curve" is every interval estimate plotted, and provides the _P_-values, CIs, along with the **median unbiased estimate** It can be defined as such, $$C V_{n}(\theta)=1-2\left|H_{n}(\theta)-0.5\right|=2 \min \left\{H_{n}(\theta), 1-H_{n}(\theta)\right\}$$ Its information counterpart, the surprisal function, can be constructed by taking the $-log_{2}$ of the _P_-value.[@chow2019asb; @greenland2019as; @Shannon1948-uq] -To view the surprisal function, we simply change the type to "s". +To view the surprisal function, we simply change the type to "`s`". ```{r echo=TRUE, fig.height=4.5, fig.width=6} (function1 <- ggcurve(data = intervalsdf[[1]], type = "s")) ``` -We can also view the consonance distribution by changing the type to "cdf", which is a cumulative probability distribution. The point at which the curve reaches 50% is known as the "median unbiased estimate". It is the same estimate that is typically at the peak of the _P_-value curve from above. +We can also view the consonance distribution by changing the type to "`cdf`", which is a cumulative probability distribution. The point at which the curve reaches 50% is known as the "**median unbiased estimate**". It is the same estimate that is typically at the peak of the _P_-value curve from above. ```{r echo=TRUE, fig.height=4.5, fig.width=6} -(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf")) +(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf", nullvalue = TRUE)) ``` We can also get relevant statistics that show the range of values by using the `curve_table()` function. There are several formats that can be exported such as .docx, .ppt, and TeX. @@ -97,7 +99,7 @@ model <- lm(GroupA2 ~ GroupB2, data = RandomData2) randomframe <- curve_gen(model, "GroupB2") ``` -Once again, we'll plot this data with `ggcurve()`. We can also indicate whether we want certain interval estimates to be plotted in the function with the "levels" argument. If we wanted to plot the 50%, 75%, and 95% intervals, we'd provide the argument this way: +Once again, we'll plot this data with `ggcurve()`. We can also indicate whether we want certain interval estimates to be plotted in the function with the "`levels`" argument. If we wanted to plot the **50**%, **75**%, and **95**% intervals, we'd provide the argument this way: ```{r echo=TRUE, fig.height=4.5, fig.width=6} (function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) @@ -114,7 +116,7 @@ Now that we have two datasets and two functions, we can compare them using the ` This function will provide us with the area that is shared between the curve, along with a ratio of overlap to non-overlap. -We can also do this for the surprisal function simply by changing type to "s". +We can also do this for the surprisal function simply by changing `type` to "`s`". ```{r echo=TRUE, fig.height=4.5, fig.width=6} (curve_compare( @@ -125,9 +127,152 @@ We can also do this for the surprisal function simply by changing type to "s". It's clear that the outputs have changed and indicate far more overlap than before. +# Survival Modeling + +Here, we'll look at how to create consonance functions from the coefficients of predictors of interest in a Cox regression model. + +We'll use the `carData` package for this. [Fox & Weisberg, 2018](https://socialsciences.mcmaster.ca/jfox/Books/Companion/appendices/Appendix-Cox-Regression.pdf) describe the dataset elegantly in their paper, + +> The Rossi data set in the `carData` package contains data from an experimental study of recidivism of 432 male prisoners, who were observed for a year after being released from prison (Rossi et al., 1980). The following variables are included in the data; the variable names are those used by Allison (1995), from whom this example and variable descriptions are adapted: +> +> **week**: week of first arrest after release, or censoring time. +> +> **arrest**: the event indicator, equal to 1 for those arrested during the period of the study and 0 for those who were not arrested. +> +> **fin**: a factor, with levels “yes” if the individual received financial aid after release from prison, and “no” if he did not; financial aid was a randomly assigned factor manipulated by the researchers. +> +> **age**: in years at the time of release. +> +> **race**: a factor with levels “black” and “other”. +> +> **wexp**: a factor with levels “yes” if the individual had full-time work experience prior to incarceration and “no” if he did not. +> +> **mar**: a factor with levels “married” if the individual was married at the time of release and “not married” if he was not. +> +> **paro**: a factor coded “yes” if the individual was released on parole and “no” if he was not. +> +> **prio**: number of prior convictions. +> +> **educ**: education, a categorical variable coded numerically, with codes 2 (grade 6 or less), 3 (grades 6 through 9), 4 (grades 10 and 11), 5 (grade 12), or 6 (some post-secondary). +> +> **emp1–emp52**: factors coded “yes” if the individual was employed in the corresponding week of the study and “no” otherwise. +> +> We read the data file into a data frame, and print the first few cases (omitting the variables **emp1 – emp52**, which are in columns 11–62 of the data frame): + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(carData) +Rossi[1:5, 1:10] +``` + +> Thus, for example, the first individual was arrested in week 20 of the study, while the fourth individual was never rearrested, and hence has a censoring time of 52. Following Allison, a Cox regression of time to rearrest on the time-constant covariates is specified as follows: + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(survival) +mod.allison <- coxph(Surv(week, arrest) ~ +fin + age + race + wexp + mar + paro + prio, +data = Rossi +) +mod.allison +``` + +Now that we have our Cox model object, we can use the `curve_surv()` function to create the function. + +If we wanted to create a function for the coefficient of prior convictions, then we'd do so like this: + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +z <- curve_surv(mod.allison, "prio") +``` + +Then we could plot our consonance curve and density and also produce a table of relevant statistics. Because we're working with ratios, we'll set the `measure` argument in `ggcurve()` to "`ratio`". + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(z[[1]], measure = "ratio", nullvalue = TRUE) +ggcurve(z[[2]], type = "cd", measure = "ratio", nullvalue = TRUE) +curve_table(z[[1]], format = "image") +``` + +We could also construct a function for another predictor such as age + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +x <- curve_surv(mod.allison, "age") +ggcurve(x[[1]], measure = "ratio") +ggcurve(x[[2]], type = "cd", measure = "ratio") +curve_table(x[[1]], format = "image") +``` + +That's a very quick look at creating functions from Cox regression models. + +# Meta-Analysis + +Here, we'll use an example dataset taken from the [`metafor`](http://www.metafor-project.org/doku.php/analyses:normand1999) website, which also comes preloaded with the `metafor` package. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(metafor) +dat.hine1989 +``` + +I will quote Wolfgang here, since he explains it best, + +>"As described under help(dat.hine1989), variables **n1i** and **n2i** are the number of patients in the lidocaine and control group, respectively, and **ai** and **ci** are the corresponding number of deaths in the two groups. Since these are 2×2 table data, a variety of different outcome measures could be used for the meta-analysis, including the risk difference, the risk ratio (relative risk), and the odds ratio (see Table III). Normand (1999) uses risk differences for the meta-analysis, so we will proceed accordingly. We can calculate the risk differences and corresponding sampling variances with: + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +dat <- escalc(measure = "RD", n1i = n1i, n2i = n2i, ai = ai, ci = ci, data = dat.hine1989) +dat +``` + +>"Note that the **yi** values are the risk differences in terms of proportions. Since Normand (1999) provides the results in terms of percentages, we can make the results directly comparable by multiplying the risk differences by 100 (and the sampling variances by $100^{2}$): + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +dat$yi <- dat$yi * 100 +dat$vi <- dat$vi * 100^2 +``` + +We can fit a fixed-effects model with the following + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +fe <- rma(yi, vi, data = dat, method = "FE") +``` + +Now that we have our metafor object, we can compute the consonance function using the `curve_meta()` function. +```{r echo=TRUE, fig.height=4.5, fig.width=6} +fecurve <- curve_meta(fe) +``` + +Now we can graph our function. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(fecurve[[1]], nullvalue = TRUE) +``` + +We used a fixed-effects model here, but if we wanted to use a random-effects model, we could do so with the following, which will use a restricted maximum likelihood estimator for the random-effects model + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +re <- rma(yi, vi, data = dat, method = "REML") +``` + +And then we could use `curve_meta()` to get the relevant list + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +recurve <- curve_meta(re) +``` + +Now we can plot our object. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(recurve[[1]], nullvalue = TRUE) +``` + +We could also compare our two models to see how much consonance/overlap there is + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +curve_compare(fecurve[[1]], recurve[[1]], plot = TRUE) +``` + +The results are practically the same and we cannot actually see any difference, and the AUC % overlap also indicates this. + # Constructing Functions From Single Intervals -We can also take a set of confidence limits and use them to construct a consonance, surprisal, likelihood or deviance function using the `curve_rev()` function. +We can also take a set of confidence limits and use them to construct a consonance, surprisal, likelihood or deviance function using the `curve_rev()` function. This method is computed from the approximate normal distribution. Here, we'll use two epidemiological studies[@brown2017j; @brown2017jcp] that studied the impact of SSRI exposure in pregnant mothers, and the rate of autism in children. @@ -136,7 +281,7 @@ Both of these studies suggested a null effect of SSRI exposure on autism rates i ```{r echo=TRUE, fig.height=4.5, fig.width=6} curve1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "c", measure = "ratio", steps = 10000) (ggcurve(data = curve1[[1]], type = "c", measure = "ratio", nullvalue = TRUE)) -curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "c", measure = "ratio", steps = 10000) +curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "c", measure = "ratio", steps = 10000) (ggcurve(data = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE)) ``` @@ -147,7 +292,7 @@ We can also see this by plotting the likelihood functions via the `curve_rev()` ```{r echo=TRUE, fig.height=4.5, fig.width=6} lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000) (ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) -lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000) +lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "l", measure = "ratio", steps = 10000) (ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) ``` @@ -175,13 +320,15 @@ Some authors have shown that the bootstrap distribution is equal to the confiden $$H_{n}(\theta)=1-P\left(\hat{\theta}-\hat{\theta}^{*} \leq \hat{\theta}-\theta | \mathbf{x}\right)=P\left(\hat{\theta}^{*} \leq \theta | \mathbf{x}\right)$$ -Certain bootstrap methods such as the BCa method and _t_-bootstrap method also yield second order accuracy of consonance distributions. +Certain bootstrap methods such as the `BCa` method and `t`-bootstrap method also yield second order accuracy of consonance distributions. $$H_{n}(\theta)=1-P\left(\frac{\hat{\theta}^{*}-\hat{\theta}}{\widehat{S E}^{*}\left(\hat{\theta}^{*}\right)} \leq \frac{\hat{\theta}-\theta}{\widehat{S E}(\hat{\theta})} | \mathbf{x}\right)$$ Here, I demonstrate how to use these particular bootstrap methods to arrive at consonance curves and densities. -We'll use the Iris dataset and construct a function that'll yield a parameter of interest. +We'll use the `Iris` dataset and construct a function that'll yield a parameter of interest. + +## The Nonparametric Bootstrap ```{r echo=TRUE, fig.height=4.5, fig.width=6} iris <- datasets::iris @@ -193,7 +340,7 @@ foo <- function(data, indices) { } ``` -We can now use the `curve_boot()` method to construct a function. The default method used for this function is the "BCa" method provided by the [`bcaboot`](https://cran.r-project.org/package=bcaboot) package.[@efron2018] +We can now use the `curve_boot()` method to construct a function. The default method used for this function is the "`Bca`" method provided by the [`bcaboot`](https://cran.r-project.org/package=bcaboot) package.[@efron2018] ```{r include=FALSE} y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 2000, steps = 1000) @@ -204,8 +351,8 @@ I will suppress the output of the function because it is unnecessarily long. But The first item in the list will be the consonance distribution constructed by typical means, while the third item will be the bootstrap approximation to the consonance distribution. ```{r echo=TRUE, fig.height=4.5, fig.width=6} -ggcurve(data = y[[1]]) -ggcurve(data = y[[3]]) +ggcurve(data = y[[1]], nullvalue = TRUE) +ggcurve(data = y[[3]], nullvalue = TRUE) ``` We can also print out a table for TeX documents @@ -220,24 +367,35 @@ More bootstrap replications will lead to a smoother function. But for now, we ca plot_compare(y[[1]], y[[3]]) ``` -The densities can also be calculated accurately using the _t_-bootstrap method. Here we use a different dataset to show this +If we wanted to look at the bootstrap standard errors, we could do so by loading the fifth item in the list +```{r echo=TRUE, fig.height=4.5, fig.width=6} +knitr::kable(y[[5]]) +``` + +where in the top row, `theta` is the point estimate, and `sdboot` is the bootstrap estimate of the standard error, `sdjack` is the jacknife estimate of the standard error. `z0` is the bias correction value and `a` is the acceleration constant. + +The values in the second row are essentially the internal standard errors of the estimates in the top row. + +* * * + +The densities can also be calculated accurately using the `t`-bootstrap method. Here we use a different dataset to show this ```{r echo=TRUE, fig.height=4.5, fig.width=6} library(Lock5Data) dataz <- data(CommuteAtlanta) -func = function(data, index) { +func <- function(data, index) { x <- as.numeric(unlist(data[1])) y <- as.numeric(unlist(data[2])) return(mean(x[index]) - mean(y[index])) } ``` -Our function is a simple mean difference. This time, we'll set the method to "t" for the _t_-bootstrap method +Our function is a simple mean difference. This time, we'll set the method to "`t`" for the `t`-bootstrap method ```{r echo=TRUE, fig.height=4.5, fig.width=6} z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) -ggcurve(data = z[[1]]) -ggcurve(data = z[[2]], type = "cd") +ggcurve(data = z[[1]], nullvalue = FALSE) +ggcurve(data = z[[2]], type = "cd", nullvalue = FALSE) ``` The consonance curve and density are nearly identical. With more bootstrap replications, they are very likely to converge. @@ -246,6 +404,51 @@ The consonance curve and density are nearly identical. With more bootstrap repli (zz <- curve_table(data = z[[1]], format = "image")) ``` +## The Parametric Bootstrap + +For the examples above, we mainly used nonparametric bootstrap methods. Here I show an example using the parametric `Bca` bootstrap and the results it yields. + +First, we'll load our data again and set our function. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +data(diabetes, package = "bcaboot") +X <- diabetes$x +y <- scale(diabetes$y, center = TRUE, scale = FALSE) +lm.model <- lm(y ~ X - 1) +mu.hat <- lm.model$fitted.values +sigma.hat <- stats::sd(lm.model$residuals) +t0 <- summary(lm.model)$adj.r.squared +y.star <- sapply(mu.hat, rnorm, n = 1000, sd = sigma.hat) +tt <- apply(y.star, 1, function(y) summary(lm(y ~ X - 1))$adj.r.squared) +b.star <- y.star %*% X +``` + +Now, we'll use the same function, but set the method to "`bcapar`" for the parametric method. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +df <- curve_boot(method = "bcapar", t0 = t0, tt = tt, bb = b.star) +``` + +Now we can look at our outputs. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(df[[1]], nullvalue = FALSE) +ggcurve(df[[3]], nullvalue = FALSE) +``` + +We can compare the functions to see how well the bootstrap approximations match up +```{r echo=TRUE, fig.height=4.5, fig.width=6} +plot_compare(df[[1]], df[[3]]) +``` + +We can also look at the density function + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(df[[5]], type = "cd", nullvalue = FALSE) +``` + +That concludes our demonstration of the bootstrap method to approximate consonance functions. + ## Using Profile Likelihoods For this last example, we'll explore the `curve_lik()` function, which can help generate profile likelihood functions, and deviance statistics with the help of the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) package. @@ -258,8 +461,10 @@ We'll use a simple example taken directly from the [`ProfileLikelihood`](https:/ ```{r echo=TRUE, fig.height=4.5, fig.width=6} data(dataglm) -xx <- profilelike.glm(y ~ x1 + x2, data=dataglm, profile.theta="group", -family=binomial(link="logit"), length=500, round=2) +xx <- profilelike.glm(y ~ x1 + x2, + data = dataglm, profile.theta = "group", + family = binomial(link = "logit"), length = 500, round = 2 +) ``` Then, we’ll use `curve_lik()` on the object that the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) package created. @@ -269,10 +474,10 @@ lik <- curve_lik(xx, dataglm) tibble::tibble(lik[[1]]) ``` -Next, we’ll plot three functions, the relative likelihood, the log likelihood, the likelihoodm, and the deviance function. +Next, we’ll plot three functions, the relative likelihood, the log-likelihood, the likelihood, and the deviance function. ```{r echo=TRUE, fig.height=4.5, fig.width=6} -ggcurve(lik[[1]], type = "l1") +ggcurve(lik[[1]], type = "l1", nullvalue = TRUE) ggcurve(lik[[1]], type = "l2") ggcurve(lik[[1]], type = "l3") ggcurve(lik[[1]], type = "d") diff --git a/vignettes/stata.R b/vignettes/stata.R index dd873ab..528acb5 100644 --- a/vignettes/stata.R +++ b/vignettes/stata.R @@ -1,3 +1,2 @@ ## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) - diff --git a/vignettes/troubleshooting.Rmd b/vignettes/troubleshooting.Rmd new file mode 100644 index 0000000..2fb0de2 --- /dev/null +++ b/vignettes/troubleshooting.Rmd @@ -0,0 +1,78 @@ +--- +title: "Troubleshooting" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Troubleshooting} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +
    + +
    + +```{r setup, include=FALSE} +knitr::opts_chunk$set( + message = TRUE, + warning = TRUE, + collapse = TRUE, + comment = "#>" +) +library(concurve) +``` + +# Installation + +If you encounter any issues with installation, try the following script to see if it resolves your issue, as this will also install the other packages that are necessary for `concurve` to function. + +```{r eval=FALSE} +install.packages("concurve", dep = TRUE) +``` + +If that doesn't work, please try installing, and resinstalling `R`, and then installing the package again. + +You can also try installing the developer version with + +```{r eval=FALSE} +library(devtools) +install_github("zadchow/concurve") +``` + +# Distorted Plots + +If you encounter issues when plotting the functions, it is because there are a large number of points being plotted, which could lead to the graph being slightly distorted or not loading at all. The simplest solution to this is to refresh the plot and try the function again. + +This applies to the `ggcurve()`, `curve_compare()`, and `plot_compare()` functions. + +# Slow Performance + +Because this package is computing thousands of interval estimates via iterations and bootstrapping, it requires a lot of computational power. Luckily, `concurve` supports parallelization, although it is disabled by default because some users, such as those who use Windows, are unable to use it. + +However, if you are able to use parallelization, you can enable it with the following script + +```{r eval=FALSE} +library(parallel) +options(mc.cores = detectCores()) +``` + +The script will detect the number of cores on your machine via the `parallel` package and use them to speed up the computations, especially for bootstrapping. + +However, if you would like to speed up the computations and are unable to use parallelization, then you can reduce the number of `steps` in the each of the `concurve` functions, which will drastically reduce the time it takes to complete the operation. By default, most of the `steps` arguments are set to 10000. + +For example, here I changed the number of steps to 100, which is the minimum needed to plot a function, and the process is now much quicker. + +```{r} +system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 100)) + +system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)) +``` + +# Bugs + +If you encounter any other bugs, please report them at +https://github.com/zadchow/concurve/issues + +# Session info + +```{r session_info, include=TRUE, echo=FALSE} +sessionInfo() +``` From 40b2b8736e76b2453e13f58d3bd0f62b1a3011d5 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 10 Dec 2019 01:30:38 -0500 Subject: [PATCH 4/5] v. 2.4.0 --- .DS_Store | Bin 26628 -> 28676 bytes R/curve_boot.R | 25 +- R/curve_compare.R | 6 +- R/curve_rev.R | 10 +- R/curve_table.R | 6 +- R/ggcurve.R | 25 +- R/plot_compare.R | 28 +- R/utils-tidy-eval.R | 47 --- README.Rmd | 4 +- README.md | 10 +- codemeta.json | 4 +- concurve.Rproj | 3 +- docs/ISSUE_TEMPLATE.html | 2 +- docs/articles/examples.html | 455 +++++++++++---------- docs/articles/stata.html | 148 +++---- docs/articles/troubleshooting.html | 26 +- docs/index.html | 42 +- docs/pkgdown.yml | 2 +- docs/reference/curve_boot.html | 25 +- docs/reference/curve_compare.html | 6 +- docs/reference/curve_rev.html | 10 +- docs/reference/curve_table.html | 6 +- docs/reference/defunct.html | 26 +- docs/reference/ggcurve.html | 25 +- docs/reference/plot_compare.html | 27 +- docs/reference/tidyeval.html | 243 ----------- docs/sitemap.xml | 3 - inst/WORDLIST | 49 +-- man/.DS_Store | Bin 8196 -> 8196 bytes man/curve_boot.Rd | 24 +- man/curve_compare.Rd | 6 +- man/curve_rev.Rd | 10 +- man/curve_table.Rd | 6 +- man/defunct.Rd | 22 +- man/ggcurve.Rd | 25 +- man/plot_compare.Rd | 27 +- man/tidyeval.Rd | 51 --- pkgdown/favicon/android-chrome-512x512.png | Bin 25420 -> 0 bytes pkgdown/favicon/safari-pinned-tab.svg | 44 -- tests/spelling.R | 9 +- usethis.R | 29 +- vignettes/examples.R | 84 +++- vignettes/figures/.DS_Store | Bin 6148 -> 6148 bytes vignettes/figures/function1.png | Bin 55806 -> 0 bytes vignettes/figures/function2.png | Bin 55655 -> 0 bytes vignettes/figures/function3.png | Bin 58415 -> 0 bytes vignettes/figures/function4.png | Bin 57436 -> 0 bytes vignettes/figures/function5.png | Bin 60338 -> 0 bytes vignettes/figures/function6.png | Bin 60868 -> 0 bytes vignettes/figures/likelihood.png | Bin 35645 -> 0 bytes vignettes/stata.R | 1 + vignettes/troubleshooting.Rmd | 2 +- 52 files changed, 598 insertions(+), 1005 deletions(-) delete mode 100644 R/utils-tidy-eval.R delete mode 100644 docs/reference/tidyeval.html delete mode 100644 man/tidyeval.Rd delete mode 100644 pkgdown/favicon/android-chrome-512x512.png delete mode 100644 pkgdown/favicon/safari-pinned-tab.svg delete mode 100644 vignettes/figures/function1.png delete mode 100644 vignettes/figures/function2.png delete mode 100644 vignettes/figures/function3.png delete mode 100644 vignettes/figures/function4.png delete mode 100644 vignettes/figures/function5.png delete mode 100644 vignettes/figures/function6.png delete mode 100644 vignettes/figures/likelihood.png diff --git a/.DS_Store b/.DS_Store index c8b18e536087c73985c28f0d15432de7b7cf498a..73041eff1e95eec7e36283beb8b4a9071a4ebcea 100644 GIT binary patch delta 1174 zcmb_cO-vI(6n?MWEvrB%5Ejr@YHNsyfwn}&N@&ZE(jcK0qYy9@x&^9jx6o?+Nu)85 zaKI2UF=)JC)RXu-;ot>r;>|=f9+Y??(Zrh=6ApU8S&C312M=ztvv1zF?|t8XGdm-1 zabyv70DyJpB@4g+3k7y7Pi+=}H*-=~Rd9vPG%Nw&gQzPg`+9?NFU=K1jv9pPYMLP! z1^h_&X@6?ey07^pSzJu*g?o76$HXtUYd+~DPF+!+)S#3p1ncQ!u}B{1veo)&U_~N* zr?aWiL~0EpH5!^wJ#ptX4ec)B>kYGyNhNEiyD$q$ zcmtnd8CEbGMKoeL?#Bao2p#Ce`g&|a7kX0VDqL%$ta93goovE^1hTTI!Tt@e01OHa6KfyVrOtCZ`0fq7Y7}{~O&z&Y9`l@flQE9W=sCspzHJ z7;YhUr5X_oa*m(25I#$zZ;g{xnk0TDRIcAMf-9KxI0<1}6N*io3{nRWyZjT{?O z(O0L2RfbQc#^9Q42Bb?)A=K9~sv$?qO2{?M=+?T7#4K?9mVl#Nj->I?SDauBGk*@~5`{0-}W-ajbQV-aIj4gq!z fWU`_0&sjiq-WrayW(geIR05Ht8$|TP|2zM0_YOUR delta 1122 zcmah}Ur3Wt6hG(Nw>eYUs<}3uWllu0%xRSQZ(CWp?9YcA<(~)VwokX(_RY;d5tJH6 z6cOmnu;{@Ge5s(iL_I_#6g>#mQ$Y|35rSA6X3$gJx!DY?F5G*6_nvcp=bU>Ew|^4* zU!ws4a95|t1mI*)$!2s{rvmV+1`H+Xz3il57XVf`1Y4jE_P~Mr?YCs4$j{4pCgsUo zVL8f4pV>&hxU}?A?HP_77c$j9s$bP9R9gDqLz@tG*__lw;Kxz zlLVEMHgCC%16{?aA^BT(lC{o!sw&!!_>}B5<;ivWj0^*5H))gWeQqge_nj2URg;j_ z)E4%64!5>O#Eyn$wJPEcOMbr~MSLATDM%E>2YaHoUa8y0tc0-G2dF~f)4l6|f diff --git a/R/curve_boot.R b/R/curve_boot.R index fa78cc9..a64dcd3 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -35,27 +35,6 @@ #' are found in the third and fourth list. The values for the density function are in #' the fifth object, while the Bca stats are in the sixth and seventh objects. #' -#' @examples -#' -#' \donttest{ -#' \dontrun{ -#' data(diabetes, package = "bcaboot") -#' Xy <- cbind(diabetes$x, diabetes$y) -#' rfun <- function(Xy) { -#' y <- Xy[, 11] -#' X <- Xy[, 1:10] -#' return(summary(lm(y ~ X))$adj.r.squared) -#' } -#' -#' x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 200, steps = 1000) -#' -#' ggcurve(data = x[[1]]) -#' ggcurve(data = x[[3]]) -#' -#' plot_compare(x[[1]], x[[3]]) -#' } -#' } -#' #' @references #' #' Efron, B., and Tibshirani, R. J. (1994), An Introduction to the Bootstrap, CRC Press. @@ -66,7 +45,7 @@ #' Statistical Inference with Confidence Distributions, Cambridge University Press. #' #' Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: -#' A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000. +#' A Review,” International Statistical Review, 81, 3–39. #' #' @@ -171,7 +150,7 @@ curve_boot <- function(data = data, func = func, method = "bca", t0, tt, bb, rep intrvls <- 0.5 / steps alpha <- seq(0.00, 0.50, intrvls) - result <- bcapar(t0 = t0, tt = tt, bb = bb, alpha = alpha, cd = 1) # > $call + result <- bcapar(t0 = t0, tt = tt, bb = bb, alpha = alpha, cd = 1) # Parametric Bootstrap Statistics ----------------------------------------- diff --git a/R/curve_compare.R b/R/curve_compare.R index 7329392..fa91334 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -32,9 +32,9 @@ #' curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s") #' } #' -#' @seealso \code{\link{plot_compare}} -#' @seealso \code{\link{ggcurve}} -#' @seealso \code{\link{curve_table}} +#' @seealso [plot_compare()] +#' @seealso [ggcurve()] +#' @seealso [curve_table()] #' curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { diff --git a/R/curve_rev.R b/R/curve_rev.R index 234d02c..669d9d3 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -13,8 +13,8 @@ #' function or a likelihood function. The default is "c" for consonance and #' likelihood can be set via "l". #' @param measure The type of data being used. If they involve mean differences, -# then the "default" option should be used, which is also the default setting. -# If the data are ratios, then the "ratio" option should be used. +#' then the "default" option should be used, which is also the default setting. +#' If the data are ratios, then the "ratio" option should be used. #' @param steps Indicates how many consonance intervals are to be calculated at #' various levels. For example, setting this to 100 will produce 100 consonance #' intervals from 0 to 100. Setting this to 10000 will produce more consonance @@ -37,9 +37,9 @@ #' df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") #' #' tibble::tibble(df[[1]]) -#' @seealso \code{\link{ggcurve}} -#' @seealso \code{\link{curve_compare}} -#' @seealso \code{\link{plot_compare}} +#' @seealso [ggcurve()] +#' @seealso [curve_compare()] +#' @seealso [plot_compare()] #' curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10000, table = TRUE) { diff --git a/R/curve_table.R b/R/curve_table.R index 0b4ea22..394bd28 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -28,9 +28,9 @@ #' (z <- curve_table(intervalsdf[[1]], format = "tibble")) #' (z <- curve_table(intervalsdf[[1]], format = "latex")) #' (z <- curve_table(intervalsdf[[1]], format = "image")) -#' @seealso \code{\link{ggcurve}} -#' @seealso \code{\link{curve_compare}} -#' @seealso \code{\link{plot_compare}} +#' @seealso [ggcurve()] +#' @seealso [curve_compare()] +#' @seealso [plot_compare()] #' curve_table <- function(data, levels, type = "c", format = "data.frame") { if (type == "c") { diff --git a/R/ggcurve.R b/R/ggcurve.R index dbdb06b..bf68e0a 100644 --- a/R/ggcurve.R +++ b/R/ggcurve.R @@ -79,34 +79,35 @@ #' ggcurve(type = "c", intervalsdf[[1]], nullvalue = TRUE) #' @references #' -#' Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. https://doi.org/10.1214/aoms/1177705145. +#' Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. #' #' Chow, Z. R., and Greenland, S. (2019), “Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise,” arXiv:1909.08579. #' -#' Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. https://doi.org/10.1146/annurev-statistics-060116-054139. +#' Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. #' -#' Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. https://doi.org/10.1080/00031305.2018.1556735. +#' Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. #' -#' Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. https://doi.org/10.1016/j.jspi.2017.09.017. +#' Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. #' -#' Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. https://doi.org/10.1002/sim.8293. +#' Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. #' -#' Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. https://doi.org/10.2105/AJPH.77.2.195. +#' Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. #' -#' Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. https://doi.org/10.2105/ajph.77.4.492. +#' Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. #' #' Rothman, K. J., Greenland, S., and Lash, T. L. (2008a), Modern Epidemiology, Lippincott Williams & Wilkins. #' -#' Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. Lash, Lippincott Williams & Wilkins, pp. 148–167. +#' Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. +#' Lash, Lippincott Williams & Wilkins, pp. 148–167. #' -#' Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. https://doi.org/10.1111/1467-9469.00285. +#' Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. #' #' Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions, Cambridge University Press. #' -#' Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. https://doi.org/10.1097/00001648-199001000-00009. +#' Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. #' -#' Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000. -#' @seealso \code{\link{plot_compare}} +#' Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. +#' @seealso [plot_compare()] #' ggcurve <- function(data, type = "c", measure = "default", levels = 0.95, nullvalue = FALSE, position = "pyramid", diff --git a/R/plot_compare.R b/R/plot_compare.R index 2939f65..82d1acd 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -79,37 +79,37 @@ #' #' @references #' -#' Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. https://doi.org/10.1214/aoms/1177705145. +#' Birnbaum, A. (1961), “A unified theory of estimation, I,” The Annals of Mathematical Statistics, 32, 112–135. #' #' Chow, Z. R., and Greenland, S. (2019), “Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise,” arXiv:1909.08579. #' -#' Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. https://doi.org/10.1146/annurev-statistics-060116-054139. +#' Fraser, D. A. S. (2017), “p-Values: The Insight to Modern Statistical Inference,” Annual Review of Statistics and Its Application, 4, 1–14. #' -#' Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. https://doi.org/10.1080/00031305.2018.1556735. +#' Fraser, D. A. S. (2019), “The P-value function and statistical inference,” The American Statistician, 73, 135–147. #' -#' Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. https://doi.org/10.1016/j.jspi.2017.09.017. +#' Hjort, N. L., and Schweder, T. (2018), “Confidence distributions and related themes,” Journal of Statistical Planning and Inference, 195, 1–13. #' -#' Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. https://doi.org/10.1002/sim.8293. +#' Infanger, D., and Schmidt‐Trucksäss, A. (2019), “P value functions: An underused method to present research results and to promote quantitative reasoning,” Statistics in Medicine. #' -#' Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. https://doi.org/10.2105/AJPH.77.2.195. +#' Poole, C. (1987a), “Beyond the confidence interval,” American Journal of Public Health, 77, 195–199. #' -#' Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. https://doi.org/10.2105/ajph.77.4.492. +#' Poole, C. (1987b), “Confidence intervals exclude nothing.,” American Journal of Public Health, 77, 492–493. #' #' Rothman, K. J., Greenland, S., and Lash, T. L. (2008a), Modern Epidemiology, Lippincott Williams & Wilkins. #' -#' Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. Lash, Lippincott Williams & Wilkins, pp. 148–167. +#' Rothman, K. J., Greenland, S., and Lash, T. L. (2008b), “Precision and statistics in epidemiologic studies,” in Modern Epidemiology, eds. K. J. Rothman, S. Greenland, and T. L. +#' Lash, Lippincott Williams & Wilkins, pp. 148–167. #' -#' Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. https://doi.org/10.1111/1467-9469.00285. +#' Schweder, T., and Hjort, N. L. (2002), “Confidence and Likelihood*,” Scandinavian Journal of Statistics, 29, 309–332. #' #' Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions, Cambridge University Press. #' -#' Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. https://doi.org/10.1097/00001648-199001000-00009. +#' Sullivan, K. M., and Foster, D. A. (1990), “Use of the confidence interval function,” Epidemiology, 1, 39–42. #' -#' Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000. +#' Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review,” International Statistical Review, 81, 3–39. #' -#' -#' @seealso \code{\link{ggcurve}} -#' @seealso \code{\link{curve_compare}} +#' @seealso [ggcurve()] +#' @seealso [curve_compare()] #' plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalue = FALSE, position = "pyramid", title = "Interval Functions", diff --git a/R/utils-tidy-eval.R b/R/utils-tidy-eval.R deleted file mode 100644 index af7c474..0000000 --- a/R/utils-tidy-eval.R +++ /dev/null @@ -1,47 +0,0 @@ -#' Tidy eval helpers -#' -#' @description -#' -#' * \code{\link[rlang:quotation]{sym}()} creates a symbol from a string and -#' \code{\link[rlang:quotation]{syms}()} creates a list of symbols from a -#' character vector. -#' -#' * \code{\link[rlang:quotation]{enquo}()} and -#' \code{\link[rlang:quotation]{enquos}()} delay the execution of one or -#' several function arguments. \code{enquo()} returns a single quoted -#' expression, which is like a blueprint for the delayed computation. -#' \code{enquos()} returns a list of such quoted expressions. -#' -#' * \code{\link[rlang:quotation]{expr}()} quotes a new expression _locally_. It -#' is mostly useful to build new expressions around arguments -#' captured with [enquo()] or [enquos()]: -#' \code{expr(mean(!!enquo(arg), na.rm = TRUE))}. -#' -#' * \code{\link[rlang]{as_name}()} transforms a quoted variable name -#' into a string. Supplying something else than a quoted variable -#' name is an error. -#' -#' That's unlike \code{\link[rlang]{as_label}()} which also returns -#' a single string but supports any kind of R object as input, -#' including quoted function calls and vectors. Its purpose is to -#' summarise that object into a single label. That label is often -#' suitable as a default name. -#' -#' If you don't know what a quoted expression contains (for instance -#' expressions captured with \code{enquo()} could be a variable -#' name, a call to a function, or an unquoted constant), then use -#' \code{as_label()}. If you know you have quoted a simple variable -#' name, or would like to enforce this, use \code{as_name()}. -#' -#' To learn more about tidy eval and how to use these tools, visit -#' \url{https://tidyeval.tidyverse.org} and the -#' \href{https://adv-r.hadley.nz/metaprogramming.html}{Metaprogramming -#' section} of \href{https://adv-r.hadley.nz}{Advanced R}. -#' -#' @md -#' @name tidyeval -#' @keywords internal -#' @importFrom rlang expr enquo enquos sym syms .data := as_name as_label -#' @aliases expr enquo enquos sym syms .data := as_name as_label -#' @export expr enquo enquos sym syms .data := as_name as_label -NULL diff --git a/README.Rmd b/README.Rmd index dcbe25e..eec7ef6 100644 --- a/README.Rmd +++ b/README.Rmd @@ -58,7 +58,7 @@ install.packages("concurve", dep = TRUE) * `R` ≥ 3.5.0 * [`ggplot2`](https://ggplot2.tidyverse.org/) * [`metafor`](http://www.metafor-project.org/doku.php/metafor) -* [`parallel`]() +* `parallel` * [`boot`](https://cran.r-project.org/package=boot) * [`bcaboot`](https://cran.r-project.org/package=bcaboot) * [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) @@ -72,7 +72,7 @@ install.packages("concurve", dep = TRUE) * [`survival`](https://cran.r-project.org/package=survival) * [`survminer`](https://cran.r-project.org/package=survminer) * [`scales`](https://cran.r-project.org/package=scales) -* [`methods`]() +* `methods` * * * diff --git a/README.md b/README.md index aad0050..851beb8 100644 --- a/README.md +++ b/README.md @@ -52,8 +52,7 @@ v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org install.packages("concurve") -Try the following if you run into any installation -issues: +Try the following if you run into any installation issues: install.packages("concurve", dep = TRUE) @@ -65,11 +64,10 @@ issues: ## Dependencies - - `R` ≥ - 3.5.0 + - `R` ≥ 3.5.0 - [`ggplot2`](https://ggplot2.tidyverse.org/) - [`metafor`](http://www.metafor-project.org/doku.php/metafor) - - [`parallel`]() + - `parallel` - [`boot`](https://cran.r-project.org/package=boot) - [`bcaboot`](https://cran.r-project.org/package=bcaboot) - [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) @@ -83,7 +81,7 @@ issues: - [`survival`](https://cran.r-project.org/package=survival) - [`survminer`](https://cran.r-project.org/package=survminer) - [`scales`](https://cran.r-project.org/package=scales) - - [`methods`]() + - `methods` ----- diff --git a/codemeta.json b/codemeta.json index 0729780..c2c2f5f 100644 --- a/codemeta.json +++ b/codemeta.json @@ -7,7 +7,7 @@ "identifier": "concurve", "description": "Allows one to compute consonance (confidence)\n intervals for various statistical tests along with their corresponding\n P-values, S-values, and likelihoods. The intervals can be plotted to\n create consonance, surprisal, and likelihood functions allowing one to\n see what effect sizes are compatible with the test model at various\n consonance levels rather than being limited to one interval estimate\n such as 95%. These methods are discussed by Poole C. (1987)\n , Schweder T, Hjort NL. (2002)\n , Singh K, Xie M, Strawderman WE. (2007)\n , Rothman KJ, Greenland S, Lash TL. (2008,\n ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019)\n , Greenland S. (2019)\n , Chow ZR, Greenland S. (2019)\n , and Greenland S, Chow ZR. (2019)\n .", "name": "concurve: Computes and Plots Compatibility (Confidence) Intervals,\n P-Values, S-Values, & Likelihood Intervals to Form Consonance,\n Surprisal, & Likelihood Functions", - "date": "2019-11-24", + "date": "2019-12-09", "codeRepository": "https://github.com/zadchow/concurve", "relatedLink": [ "https://data.lesslikely.com/concurve/", @@ -339,7 +339,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve", "confidence-intervals", "cargo-cult-statistics", "compatibility-intervals", "assumptions", "stata", "confidence-distribution", "likelihood-functions", "bootstrap-method", "r", "frequentist-methods", "fiducial-distribution"], - "fileSize": "2236.004KB", + "fileSize": "1861.564KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/concurve.Rproj b/concurve.Rproj index 5e0a049..6933deb 100644 --- a/concurve.Rproj +++ b/concurve.Rproj @@ -9,7 +9,7 @@ UseSpacesForTab: Yes NumSpacesForTab: 2 Encoding: UTF-8 -RnwWeave: knitr +RnwWeave: Sweave LaTeX: pdfLaTeX AutoAppendNewline: Yes @@ -18,6 +18,7 @@ StripTrailingWhitespace: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source +PackageCheckArgs: --as-cran PackageRoxygenize: rd,collate,namespace,vignette QuitChildProcessesOnExit: Yes diff --git a/docs/ISSUE_TEMPLATE.html b/docs/ISSUE_TEMPLATE.html index 440bad7..9e43b53 100644 --- a/docs/ISSUE_TEMPLATE.html +++ b/docs/ISSUE_TEMPLATE.html @@ -143,7 +143,7 @@

    NA

    Please include a minimal reproducible example (AKA a reprex). If you’ve never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.


    Brief description of the problem

    - +
    # insert reprex here
    diff --git a/docs/articles/examples.html b/docs/articles/examples.html index d0ee56e..b7f8269 100644 --- a/docs/articles/examples.html +++ b/docs/articles/examples.html @@ -109,53 +109,53 @@

    Examples in R

    Introduction

    -

    Here I show how to produce P-value, S-value, likelihood, and deviance functions with the concurve package using fake data and data from real studies. Simply put, these functions are rich sources of information for scientific inference and the image below, taken from Xie & Singh, 20131 displays why.

    +

    Here I show how to produce P-value, S-value, likelihood, and deviance functions with the concurve package using fake data and data from real studies. Simply put, these functions are rich sources of information for scientific inference and the image below, taken from Xie & Singh, 20131 displays why.

    -

    For a more extensive discussion of these concepts, see the following references.113

    +

    For a more extensive discussion of these concepts, see the following references.113

    Simple Models

    To get started, we could generate some normal data and combine two vectors in a dataframe

    -
    library(concurve)
    -set.seed(1031)
    -GroupA <- rnorm(500)
    -GroupB <- rnorm(500)
    -RandomData <- data.frame(GroupA, GroupB)
    +
    library(concurve)
    +set.seed(1031)
    +GroupA <- rnorm(500)
    +GroupB <- rnorm(500)
    +RandomData <- data.frame(GroupA, GroupB)

    and look at the differences between the two vectors. We’ll plug these vectors and the dataframe they’re in inside of the curve_mean() function. Here, the default method involves calculating CIs using the Wald method.

    - +
    intervalsdf <- curve_mean(GroupA, GroupB,
    +  data = RandomData, method = "default"
    +)

    Each of the functions within concurve will generally produce a list with three items, and the first will usually contain the function of interest.

    - +
    tibble::tibble(intervalsdf[[1]])
    +#> # A tibble: 10,000 x 1
    +#>    `intervalsdf[[1… $upper.limit $intrvl.width $intrvl.level  $cdf $pvalue
    +#>               <dbl>        <dbl>         <dbl>         <dbl> <dbl>   <dbl>
    +#>  1           -0.113       -0.113     0              0        0.5     1    
    +#>  2           -0.113       -0.113     0.0000154      0.0001   0.500   1.000
    +#>  3           -0.113       -0.113     0.0000309      0.0002   0.500   1.000
    +#>  4           -0.113       -0.113     0.0000463      0.000300 0.500   1.000
    +#>  5           -0.113       -0.113     0.0000617      0.0004   0.500   1.000
    +#>  6           -0.113       -0.113     0.0000772      0.0005   0.500   1.000
    +#>  7           -0.113       -0.113     0.0000926      0.000600 0.500   0.999
    +#>  8           -0.113       -0.113     0.000108       0.0007   0.500   0.999
    +#>  9           -0.113       -0.112     0.000123       0.0008   0.500   0.999
    +#> 10           -0.113       -0.112     0.000139       0.0009   0.500   0.999
    +#> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>

    We can view the function using the ggcurve() function. The two basic arguments that must be provided are the data argument and the “type” argument. To plot a consonance function, we would write “c”.

    -
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "c", nullvalue = TRUE))
    +
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "c", nullvalue = TRUE))

    We can see that the consonance “curve” is every interval estimate plotted, and provides the P-values, CIs, along with the median unbiased estimate It can be defined as such,

    \[C V_{n}(\theta)=1-2\left|H_{n}(\theta)-0.5\right|=2 \min \left\{H_{n}(\theta), 1-H_{n}(\theta)\right\}\]

    -

    Its information counterpart, the surprisal function, can be constructed by taking the \(-log_{2}\) of the P-value.3,14,15

    +

    Its information counterpart, the surprisal function, can be constructed by taking the \(-log_{2}\) of the P-value.3,14,15

    To view the surprisal function, we simply change the type to “s”.

    -
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "s"))
    +
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "s"))

    We can also view the consonance distribution by changing the type to “cdf”, which is a cumulative probability distribution. The point at which the curve reaches 50% is known as the “median unbiased estimate”. It is the same estimate that is typically at the peak of the P-value curve from above.

    -
    (function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf", nullvalue = TRUE))
    +
    (function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf", nullvalue = TRUE))

    We can also get relevant statistics that show the range of values by using the curve_table() function. There are several formats that can be exported such as .docx, .ppt, and TeX.

    -
    (x <- curve_table(data = intervalsdf[[1]], format = "image"))
    +
    (x <- curve_table(data = intervalsdf[[1]], format = "image"))
    @@ -256,44 +256,44 @@

    Comparing Functions

    If we wanted to compare two studies to see the amount of “consonance”, we could use the curve_compare() function to get a numerical output.

    First, we generate some more fake data

    -
    GroupA2 <- rnorm(500)
    -GroupB2 <- rnorm(500)
    -RandomData2 <- data.frame(GroupA2, GroupB2)
    -model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    -randomframe <- curve_gen(model, "GroupB2")
    +
    GroupA2 <- rnorm(500)
    +GroupB2 <- rnorm(500)
    +RandomData2 <- data.frame(GroupA2, GroupB2)
    +model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    +randomframe <- curve_gen(model, "GroupB2")

    Once again, we’ll plot this data with ggcurve(). We can also indicate whether we want certain interval estimates to be plotted in the function with the “levels” argument. If we wanted to plot the 50%, 75%, and 95% intervals, we’d provide the argument this way:

    -
    (function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE))
    +
    (function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE))

    Now that we have two datasets and two functions, we can compare them using the curve_compare() function.

    - +
    (curve_compare(
    +  data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c",
    +  plot = TRUE, measure = "default", nullvalue = TRUE
    +))
    +#> [1] "AUC = Area Under the Curve"
    +#> [[1]]
    +#> 
    +#> 
    +#>  AUC 1   AUC 2   Shared AUC   AUC Overlap (%)   Overlap:Non-Overlap AUC Ratio
    +#> ------  ------  -----------  ----------------  ------------------------------
    +#>  0.098   0.073        0.024            16.309                           0.195
    +#> 
    +#> [[2]]

    This function will provide us with the area that is shared between the curve, along with a ratio of overlap to non-overlap.

    We can also do this for the surprisal function simply by changing type to “s”.

    - +
    (curve_compare(
    +  data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s",
    +  plot = TRUE, measure = "default", nullvalue = FALSE
    +))
    +#> [1] "AUC = Area Under the Curve"
    +#> [[1]]
    +#> 
    +#> 
    +#>  AUC 1   AUC 2   Shared AUC   AUC Overlap (%)   Overlap:Non-Overlap AUC Ratio
    +#> ------  ------  -----------  ----------------  ------------------------------
    +#>  3.947   1.531        1.531            38.801                           0.634
    +#> 
    +#> [[2]]

    It’s clear that the outputs have changed and indicate far more overlap than before.

    @@ -317,47 +317,47 @@

    emp1–emp52: factors coded “yes” if the individual was employed in the corresponding week of the study and “no” otherwise.

    We read the data file into a data frame, and print the first few cases (omitting the variables emp1 – emp52, which are in columns 11–62 of the data frame):

    - +
    library(carData)
    +Rossi[1:5, 1:10]
    +#>   week arrest fin age  race wexp         mar paro prio educ
    +#> 1   20      1  no  27 black   no not married  yes    3    3
    +#> 2   17      1  no  18 black   no not married  yes    8    4
    +#> 3   25      1  no  19 other  yes not married  yes   13    3
    +#> 4   52      0 yes  23 black  yes     married  yes    1    5
    +#> 5   52      0  no  19 other  yes not married  yes    3    3

    Thus, for example, the first individual was arrested in week 20 of the study, while the fourth individual was never rearrested, and hence has a censoring time of 52. Following Allison, a Cox regression of time to rearrest on the time-constant covariates is specified as follows:

    - +
    library(survival)
    +mod.allison <- coxph(Surv(week, arrest) ~
    +fin + age + race + wexp + mar + paro + prio,
    +data = Rossi
    +)
    +mod.allison
    +#> Call:
    +#> coxph(formula = Surv(week, arrest) ~ fin + age + race + wexp + 
    +#>     mar + paro + prio, data = Rossi)
    +#> 
    +#>                    coef exp(coef) se(coef)      z       p
    +#> finyes         -0.37942   0.68426  0.19138 -1.983 0.04742
    +#> age            -0.05744   0.94418  0.02200 -2.611 0.00903
    +#> raceother      -0.31390   0.73059  0.30799 -1.019 0.30812
    +#> wexpyes        -0.14980   0.86088  0.21222 -0.706 0.48029
    +#> marnot married  0.43370   1.54296  0.38187  1.136 0.25606
    +#> paroyes        -0.08487   0.91863  0.19576 -0.434 0.66461
    +#> prio            0.09150   1.09581  0.02865  3.194 0.00140
    +#> 
    +#> Likelihood ratio test=33.27  on 7 df, p=2.362e-05
    +#> n= 432, number of events= 114

    Now that we have our Cox model object, we can use the curve_surv() function to create the function.

    If we wanted to create a function for the coefficient of prior convictions, then we’d do so like this:

    -
    z <- curve_surv(mod.allison, "prio")
    +
    z <- curve_surv(mod.allison, "prio")

    Then we could plot our consonance curve and density and also produce a table of relevant statistics. Because we’re working with ratios, we’ll set the measure argument in ggcurve() to “ratio”.

    -
    ggcurve(z[[1]], measure = "ratio", nullvalue = TRUE)
    +
    ggcurve(z[[1]], measure = "ratio", nullvalue = TRUE)

    -
    ggcurve(z[[2]], type = "cd", measure = "ratio", nullvalue = TRUE)
    +
    ggcurve(z[[2]], type = "cd", measure = "ratio", nullvalue = TRUE)

    -
    curve_table(z[[1]], format = "image")
    +
    curve_table(z[[1]], format = "image")

    Lower Limit

    @@ -453,12 +453,12 @@

    Lower Limit

    We could also construct a function for another predictor such as age

    -
    x <- curve_surv(mod.allison, "age")
    -ggcurve(x[[1]], measure = "ratio")
    +
    x <- curve_surv(mod.allison, "age")
    +ggcurve(x[[1]], measure = "ratio")

    -
    ggcurve(x[[2]], type = "cd", measure = "ratio")
    +
    ggcurve(x[[2]], type = "cd", measure = "ratio")

    -
    curve_table(x[[1]], format = "image")
    +
    curve_table(x[[1]], format = "image")
    @@ -559,61 +559,61 @@

    Meta-Analysis

    Here, we’ll use an example dataset taken from the metafor website, which also comes preloaded with the metafor package.

    - +
    library(metafor)
    +#> Loading required package: Matrix
    +#> Loading 'metafor' package (version 2.1-0). For an overview 
    +#> and introduction to the package please type: help(metafor).
    +dat.hine1989
    +#>   study         source n1i n2i ai ci
    +#> 1     1  Chopra et al.  39  43  2  1
    +#> 2     2       Mogensen  44  44  4  4
    +#> 3     3    Pitt et al. 107 110  6  4
    +#> 4     4   Darby et al. 103 100  7  5
    +#> 5     5 Bennett et al. 110 106  7  3
    +#> 6     6 O'Brien et al. 154 146 11  4

    I will quote Wolfgang here, since he explains it best,

    "As described under help(dat.hine1989), variables n1i and n2i are the number of patients in the lidocaine and control group, respectively, and ai and ci are the corresponding number of deaths in the two groups. Since these are 2×2 table data, a variety of different outcome measures could be used for the meta-analysis, including the risk difference, the risk ratio (relative risk), and the odds ratio (see Table III). Normand (1999) uses risk differences for the meta-analysis, so we will proceed accordingly. We can calculate the risk differences and corresponding sampling variances with:

    - +
    dat <- escalc(measure = "RD", n1i = n1i, n2i = n2i, ai = ai, ci = ci, data = dat.hine1989)
    +dat
    +#>   study         source n1i n2i ai ci     yi     vi 
    +#> 1     1  Chopra et al.  39  43  2  1 0.0280 0.0018 
    +#> 2     2       Mogensen  44  44  4  4 0.0000 0.0038 
    +#> 3     3    Pitt et al. 107 110  6  4 0.0197 0.0008 
    +#> 4     4   Darby et al. 103 100  7  5 0.0180 0.0011 
    +#> 5     5 Bennett et al. 110 106  7  3 0.0353 0.0008 
    +#> 6     6 O'Brien et al. 154 146 11  4 0.0440 0.0006

    "Note that the yi values are the risk differences in terms of proportions. Since Normand (1999) provides the results in terms of percentages, we can make the results directly comparable by multiplying the risk differences by 100 (and the sampling variances by \(100^{2}\)):

    - +
    dat$yi <- dat$yi * 100
    +dat$vi <- dat$vi * 100^2

    We can fit a fixed-effects model with the following

    -
    fe <- rma(yi, vi, data = dat, method = "FE")
    +
    fe <- rma(yi, vi, data = dat, method = "FE")

    Now that we have our metafor object, we can compute the consonance function using the curve_meta() function.

    - +
    fecurve <- curve_meta(fe)

    Now we can graph our function.

    -
    ggcurve(fecurve[[1]], nullvalue = TRUE)
    +
    ggcurve(fecurve[[1]], nullvalue = TRUE)

    We used a fixed-effects model here, but if we wanted to use a random-effects model, we could do so with the following, which will use a restricted maximum likelihood estimator for the random-effects model

    -
    re <- rma(yi, vi, data = dat, method = "REML")
    +
    re <- rma(yi, vi, data = dat, method = "REML")

    And then we could use curve_meta() to get the relevant list

    - +
    recurve <- curve_meta(re)

    Now we can plot our object.

    -
    ggcurve(recurve[[1]], nullvalue = TRUE)
    +
    ggcurve(recurve[[1]], nullvalue = TRUE)

    We could also compare our two models to see how much consonance/overlap there is

    - +
    curve_compare(fecurve[[1]], recurve[[1]], plot = TRUE)
    +#> [1] "AUC = Area Under the Curve"
    +#> [[1]]
    +#> 
    +#> 
    +#>  AUC 1   AUC 2   Shared AUC   AUC Overlap (%)   Overlap:Non-Overlap AUC Ratio
    +#> ------  ------  -----------  ----------------  ------------------------------
    +#>  2.085   2.085        2.085               100                             Inf
    +#> 
    +#> [[2]]

    The results are practically the same and we cannot actually see any difference, and the AUC % overlap also indicates this.

    @@ -621,39 +621,39 @@

    Constructing Functions From Single Intervals

    We can also take a set of confidence limits and use them to construct a consonance, surprisal, likelihood or deviance function using the curve_rev() function. This method is computed from the approximate normal distribution.

    -

    Here, we’ll use two epidemiological studies16,17 that studied the impact of SSRI exposure in pregnant mothers, and the rate of autism in children.

    +

    Here, we’ll use two epidemiological studies16,17 that studied the impact of SSRI exposure in pregnant mothers, and the rate of autism in children.

    Both of these studies suggested a null effect of SSRI exposure on autism rates in children.

    -
    curve1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "c", measure = "ratio", steps = 10000)
    -(ggcurve(data = curve1[[1]], type = "c", measure = "ratio", nullvalue = TRUE))
    +
    curve1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "c", measure = "ratio", steps = 10000)
    +(ggcurve(data = curve1[[1]], type = "c", measure = "ratio", nullvalue = TRUE))

    -
    curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "c", measure = "ratio", steps = 10000)
    -(ggcurve(data = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE))
    +
    curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "c", measure = "ratio", steps = 10000)
    +(ggcurve(data = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE))

    The null value is shown via the red line and it’s clear that a large mass of the function is away from it.

    We can also see this by plotting the likelihood functions via the curve_rev() function.

    -
    lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000)
    -(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))
    +
    lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000)
    +(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))

    -
    lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "l", measure = "ratio", steps = 10000)
    -(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))
    +
    lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, type = "l", measure = "ratio", steps = 10000)
    +(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))

    We can also view the amount of agreement between the likelihood functions of these two studies.

    - +
    (plot_compare(
    +  data1 = lik1[[1]], data2 = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.",
    +  subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")
    +))

    and the consonance functions

    - +
    (plot_compare(
    +  data1 = curve1[[1]], data2 = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.",
    +  subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")
    +))

    The Bootstrap and Consonance Functions

    -

    Some authors have shown that the bootstrap distribution is equal to the confidence distribution because it meets the definition of a consonance distribution.1,18,19 The bootstrap distribution and the asymptotic consonance distribution would be defined as:

    +

    Some authors have shown that the bootstrap distribution is equal to the confidence distribution because it meets the definition of a consonance distribution.1,18,19 The bootstrap distribution and the asymptotic consonance distribution would be defined as:

    \[H_{n}(\theta)=1-P\left(\hat{\theta}-\hat{\theta}^{*} \leq \hat{\theta}-\theta | \mathbf{x}\right)=P\left(\hat{\theta}^{*} \leq \theta | \mathbf{x}\right)\]

    Certain bootstrap methods such as the BCa method and t-bootstrap method also yield second order accuracy of consonance distributions.

    \[H_{n}(\theta)=1-P\left(\frac{\hat{\theta}^{*}-\hat{\theta}}{\widehat{S E}^{*}\left(\hat{\theta}^{*}\right)} \leq \frac{\hat{\theta}-\theta}{\widehat{S E}(\hat{\theta})} | \mathbf{x}\right)\]

    @@ -662,22 +662,22 @@

    The Nonparametric Bootstrap

    - -

    We can now use the curve_boot() method to construct a function. The default method used for this function is the “Bca” method provided by the bcaboot package.19

    +
    iris <- datasets::iris
    +foo <- function(data, indices) {
    +  dt <- data[indices, ]
    +  c(
    +    cor(dt[, 1], dt[, 2], method = "p")
    +  )
    +}
    +

    We can now use the curve_boot() method to construct a function. The default method used for this function is the “Bca” method provided by the bcaboot package.19

    I will suppress the output of the function because it is unnecessarily long. But we’ve placed all the estimates into a list object called y.

    The first item in the list will be the consonance distribution constructed by typical means, while the third item will be the bootstrap approximation to the consonance distribution.

    -
    ggcurve(data = y[[1]], nullvalue = TRUE)
    +
    ggcurve(data = y[[1]], nullvalue = TRUE)

    -
    ggcurve(data = y[[3]], nullvalue = TRUE)
    +
    ggcurve(data = y[[3]], nullvalue = TRUE)

    We can also print out a table for TeX documents

    -
    (gg <- curve_table(data = y[[1]], format = "image"))
    +
    (gg <- curve_table(data = y[[1]], format = "image"))

    Lower Limit

    @@ -746,10 +746,10 @@

    Lower Limit

    More bootstrap replications will lead to a smoother function. But for now, we can compare these two functions to see how similar they are.

    -
    plot_compare(y[[1]], y[[3]])
    +
    plot_compare(y[[1]], y[[3]])

    If we wanted to look at the bootstrap standard errors, we could do so by loading the fifth item in the list

    -
    knitr::kable(y[[5]])
    +
    knitr::kable(y[[5]])
    @@ -782,22 +782,22 @@

    The values in the second row are essentially the internal standard errors of the estimates in the top row.


    The densities can also be calculated accurately using the t-bootstrap method. Here we use a different dataset to show this

    -
    library(Lock5Data)
    -dataz <- data(CommuteAtlanta)
    -func <- function(data, index) {
    -  x <- as.numeric(unlist(data[1]))
    -  y <- as.numeric(unlist(data[2]))
    -  return(mean(x[index]) - mean(y[index]))
    -}
    +
    library(Lock5Data)
    +dataz <- data(CommuteAtlanta)
    +func <- function(data, index) {
    +  x <- as.numeric(unlist(data[1]))
    +  y <- as.numeric(unlist(data[2]))
    +  return(mean(x[index]) - mean(y[index]))
    +}

    Our function is a simple mean difference. This time, we’ll set the method to “t” for the t-bootstrap method

    -
    z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000)
    -#> Warning in norm.inter(t, alpha): extreme order statistics used as endpoints
    -ggcurve(data = z[[1]], nullvalue = FALSE)
    +
    z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000)
    +#> Warning in norm.inter(t, alpha): extreme order statistics used as endpoints
    +ggcurve(data = z[[1]], nullvalue = FALSE)

    -
    ggcurve(data = z[[2]], type = "cd", nullvalue = FALSE)
    +
    ggcurve(data = z[[2]], type = "cd", nullvalue = FALSE)

    The consonance curve and density are nearly identical. With more bootstrap replications, they are very likely to converge.

    -
    (zz <- curve_table(data = z[[1]], format = "image"))
    +
    (zz <- curve_table(data = z[[1]], format = "image"))

    @@ -898,28 +898,28 @@

    The Parametric Bootstrap

    For the examples above, we mainly used nonparametric bootstrap methods. Here I show an example using the parametric Bca bootstrap and the results it yields.

    First, we’ll load our data again and set our function.

    -
    data(diabetes, package = "bcaboot")
    -X <- diabetes$x
    -y <- scale(diabetes$y, center = TRUE, scale = FALSE)
    -lm.model <- lm(y ~ X - 1)
    -mu.hat <- lm.model$fitted.values
    -sigma.hat <- stats::sd(lm.model$residuals)
    -t0 <- summary(lm.model)$adj.r.squared
    -y.star <- sapply(mu.hat, rnorm, n = 1000, sd = sigma.hat)
    -tt <- apply(y.star, 1, function(y) summary(lm(y ~ X - 1))$adj.r.squared)
    -b.star <- y.star %*% X
    +
    data(diabetes, package = "bcaboot")
    +X <- diabetes$x
    +y <- scale(diabetes$y, center = TRUE, scale = FALSE)
    +lm.model <- lm(y ~ X - 1)
    +mu.hat <- lm.model$fitted.values
    +sigma.hat <- stats::sd(lm.model$residuals)
    +t0 <- summary(lm.model)$adj.r.squared
    +y.star <- sapply(mu.hat, rnorm, n = 1000, sd = sigma.hat)
    +tt <- apply(y.star, 1, function(y) summary(lm(y ~ X - 1))$adj.r.squared)
    +b.star <- y.star %*% X

    Now, we’ll use the same function, but set the method to “bcapar” for the parametric method.

    -
    df <- curve_boot(method = "bcapar", t0 = t0, tt = tt, bb = b.star)
    +
    df <- curve_boot(method = "bcapar", t0 = t0, tt = tt, bb = b.star)

    Now we can look at our outputs.

    -
    ggcurve(df[[1]], nullvalue = FALSE)
    +
    ggcurve(df[[1]], nullvalue = FALSE)

    -
    ggcurve(df[[3]], nullvalue = FALSE)
    +
    ggcurve(df[[3]], nullvalue = FALSE)

    We can compare the functions to see how well the bootstrap approximations match up

    -
    plot_compare(df[[1]], df[[3]])
    +
    plot_compare(df[[1]], df[[3]])

    We can also look at the density function

    -
    ggcurve(df[[5]], type = "cd", nullvalue = FALSE)
    +
    ggcurve(df[[5]], type = "cd", nullvalue = FALSE)

    That concludes our demonstration of the bootstrap method to approximate consonance functions.

    @@ -927,41 +927,41 @@

    Using Profile Likelihoods

    For this last example, we’ll explore the curve_lik() function, which can help generate profile likelihood functions, and deviance statistics with the help of the ProfileLikelihood package.

    - +
    library(ProfileLikelihood)
    +#> Loading required package: nlme
    +#> Loading required package: MASS

    We’ll use a simple example taken directly from the ProfileLikelihood documentation where we’ll calculate the likelihoods from a glm model

    - +
    data(dataglm)
    +xx <- profilelike.glm(y ~ x1 + x2,
    +  data = dataglm, profile.theta = "group",
    +  family = binomial(link = "logit"), length = 500, round = 2
    +)
    +#> Warning message: provide lo.theta and hi.theta

    Then, we’ll use curve_lik() on the object that the ProfileLikelihood package created.

    - +
    lik <- curve_lik(xx, dataglm)
    +tibble::tibble(lik[[1]])
    +#> # A tibble: 500 x 1
    +#>    `lik[[1]]`$values $likelihood $loglikelihood  $support $deviancestat
    +#>                <dbl>       <dbl>          <dbl>     <dbl>         <dbl>
    +#>  1             -1.41    9.26e-21          -9.79 0.0000560          9.79
    +#>  2             -1.40    1.00e-20          -9.71 0.0000606          9.71
    +#>  3             -1.39    1.08e-20          -9.63 0.0000655          9.63
    +#>  4             -1.38    1.17e-20          -9.56 0.0000708          9.56
    +#>  5             -1.37    1.26e-20          -9.48 0.0000765          9.48
    +#>  6             -1.35    1.37e-20          -9.40 0.0000826          9.40
    +#>  7             -1.34    1.47e-20          -9.32 0.0000892          9.32
    +#>  8             -1.33    1.59e-20          -9.25 0.0000963          9.25
    +#>  9             -1.32    1.72e-20          -9.17 0.000104           9.17
    +#> 10             -1.31    1.85e-20          -9.10 0.000112           9.10
    +#> # … with 490 more rows

    Next, we’ll plot three functions, the relative likelihood, the log-likelihood, the likelihood, and the deviance function.

    -
    ggcurve(lik[[1]], type = "l1", nullvalue = TRUE)
    +
    ggcurve(lik[[1]], type = "l1", nullvalue = TRUE)

    -
    ggcurve(lik[[1]], type = "l2")
    +
    ggcurve(lik[[1]], type = "l2")

    -
    ggcurve(lik[[1]], type = "l3")
    +
    ggcurve(lik[[1]], type = "l3")

    -
    ggcurve(lik[[1]], type = "d")
    +
    ggcurve(lik[[1]], type = "d")

    The obvious advantage of using reduced likelihoods is that they are free of nuisance parameters

    \[L_{t_{n}}(\theta)=f_{n}\left(F_{n}^{-1}\left(H_{p i v}(\theta)\right)\right)\left|\frac{\partial}{\partial t} \psi\left(t_{n}, \theta\right)\right|=h_{p i v}(\theta)\left|\frac{\partial}{\partial t} \psi(t, \theta)\right| /\left.\left|\frac{\partial}{\partial \theta} \psi(t, \theta)\right|\right|_{t=t_{n}}\] thus, giving summaries of the data that can be incorporated into combined analyses.

    @@ -983,7 +983,7 @@

    3. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:190908579 [statME]. September 2019. http://arxiv.org/abs/1909.08579.

    -

    4. Fraser D. P-Values: The Insight to Modern Statistical Inference. Annual Review of Statistics and Its Application. 2017;4(1):1-14. doi:10.1146/annurev-statistics-060116-054139

    +

    4. Fraser DAS. P-Values: The Insight to Modern Statistical Inference. Annual Review of Statistics and Its Application. 2017;4(1):1-14. doi:10.1146/annurev-statistics-060116-054139

    5. Fraser DAS. The P-value function and statistical inference. The American Statistician. 2019;73(sup1):135-147. doi:10.1080/00031305.2018.1556735

    @@ -1047,7 +1047,8 @@

  • Meta-Analysis
  • Constructing Functions From Single Intervals
  • -The Bootstrap and Consonance Functions
  • diff --git a/docs/articles/troubleshooting.html b/docs/articles/troubleshooting.html index e57df81..388b868 100644 --- a/docs/articles/troubleshooting.html +++ b/docs/articles/troubleshooting.html @@ -112,11 +112,11 @@

    Troubleshooting

    Installation

    If you encounter any issues with installation, try the following script to see if it resolves your issue, as this will also install the other packages that are necessary for concurve to function.

    -
    install.packages("concurve", dep = TRUE)
    +
    install.packages("concurve", dep = TRUE)

    If that doesn’t work, please try installing, and resinstalling R, and then installing the package again.

    You can also try installing the developer version with

    -
    library(devtools)
    -install_github("zadchow/concurve")
    +
    library(devtools)
    +install_github("zadchow/concurve")

    @@ -129,23 +129,23 @@

    Slow Performance

    Because this package is computing thousands of interval estimates via iterations and bootstrapping, it requires a lot of computational power. Luckily, concurve supports parallelization, although it is disabled by default because some users, such as those who use Windows, are unable to use it.

    However, if you are able to use parallelization, you can enable it with the following script

    -
    library(parallel)
    -options(mc.cores = detectCores())
    +
    library(parallel)
    +options(mc.cores = detectCores())

    The script will detect the number of cores on your machine via the parallel package and use them to speed up the computations, especially for bootstrapping.

    However, if you would like to speed up the computations and are unable to use parallelization, then you can reduce the number of steps in the each of the concurve functions, which will drastically reduce the time it takes to complete the operation. By default, most of the steps arguments are set to 10000.

    For example, here I changed the number of steps to 100, which is the minimum needed to plot a function, and the process is now much quicker.

    -
    system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 100))
    -#>    user  system elapsed 
    -#>   0.183   0.009   0.195
    -
    -system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000))
    -#>    user  system elapsed 
    -#>   0.030   0.002   0.033
    +
    system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 100))
    +#>    user  system elapsed 
    +#>   0.152   0.005   0.158
    +
    +system.time(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000))
    +#>    user  system elapsed 
    +#>   0.023   0.001   0.024

    Bugs

    -

    If you encounter any other bugs, please report them at https://github.com/zadchow/concurve/issues

    +

    If you encounter any other bugs, please report them at https://github.com/zadchow/concurve/issues

    diff --git a/docs/index.html b/docs/index.html index 34ce87b..eea2b49 100644 --- a/docs/index.html +++ b/docs/index.html @@ -139,9 +139,9 @@

    Install the Package From CRAN Below To Follow The Examples.

    (Seriously Recommend Looking at Examples)

    -
    install.packages("concurve")
    +
    install.packages("concurve")

    Try the following if you run into any installation issues:

    -
    install.packages("concurve", dep = TRUE)
    +
    install.packages("concurve", dep = TRUE)

    @@ -157,7 +157,7 @@

    R ≥ 3.5.0
  • ggplot2
  • metafor
  • -
  • parallel
  • +
  • parallel
  • boot
  • bcaboot
  • ProfileLikelihood
  • @@ -171,7 +171,7 @@

  • survival
  • survminer
  • scales
  • -
  • methods
  • +
  • methods

  • @@ -218,23 +218,23 @@

    Session info

    - +
    ## R version 3.6.1 (2019-07-05)
    +## Platform: x86_64-apple-darwin15.6.0 (64-bit)
    +## Running under: macOS Catalina 10.15.1
    +## 
    +## Matrix products: default
    +## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
    +## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
    +## 
    +## locale:
    +## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    +## 
    +## attached base packages:
    +## [1] stats     graphics  grDevices utils     methods   base     
    +## 
    +## loaded via a namespace (and not attached):
    +##  [1] compiler_3.6.1  magrittr_1.5    tools_3.6.1     htmltools_0.4.0 yaml_2.2.0      Rcpp_1.0.3      stringi_1.4.3  
    +##  [8] rmarkdown_1.18  knitr_1.26      stringr_1.4.0   xfun_0.11       digest_0.6.23   rlang_0.4.2     evaluate_0.14
    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 62e535a..466e944 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: '2.6' +pandoc: 2.8.1 pkgdown: 1.4.1 pkgdown_sha: ~ articles: diff --git a/docs/reference/curve_boot.html b/docs/reference/curve_boot.html index 7a02aa6..2dce918 100644 --- a/docs/reference/curve_boot.html +++ b/docs/reference/curve_boot.html @@ -221,28 +221,8 @@

    R

    Schweder, T., and Hjort, N. L. (2016), Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions, Cambridge University Press.

    Xie, M., and Singh, K. (2013), “Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: -A Review,” International Statistical Review, 81, 3–39. https://doi.org/10.1111/insr.12000.

    - -

    Examples

    -
    -# \donttest{ -if (FALSE) { -data(diabetes, package = "bcaboot") -Xy <- cbind(diabetes$x, diabetes$y) -rfun <- function(Xy) { - y <- Xy[, 11] - X <- Xy[, 1:10] - return(summary(lm(y ~ X))$adj.r.squared) -} - -x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 200, steps = 1000) - -ggcurve(data = x[[1]]) -ggcurve(data = x[[3]]) - -plot_compare(x[[1]], x[[3]]) -} -# }
    +A Review,” International Statistical Review, 81, 3–39.

    + diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html index 0004140..1a38b85 100644 --- a/docs/reference/curve_compare.html +++ b/docs/reference/curve_compare.html @@ -185,9 +185,9 @@

    Value

    Computes an AUC score and returns a plot that graphs two functions.

    See also

    - +

    Examples

    # \donttest{ diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html index 93896bd..75d3b25 100644 --- a/docs/reference/curve_rev.html +++ b/docs/reference/curve_rev.html @@ -180,7 +180,9 @@

    Arg

    - + @@ -206,9 +208,9 @@

    Value

    and the table for the values in the third if table = TRUE.

    See also

    - +

    Examples

    diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html index eeeb6d7..6ee559a 100644 --- a/docs/reference/curve_table.html +++ b/docs/reference/curve_table.html @@ -177,9 +177,9 @@

    Arg

    See also

    - +

    Examples

    diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html index a06c4f1..7cb79a0 100644 --- a/docs/reference/defunct.html +++ b/docs/reference/defunct.html @@ -6,7 +6,7 @@ -Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve +Deprecated functions in <span class="pkg">concurve</span> — defunct • concurve @@ -44,7 +44,7 @@ -concurve. — defunct" /> +concurve — defunct" /> @@ -136,7 +136,7 @@
    @@ -170,52 +170,52 @@

    For plotpint, use ggcurve.

    +

    For plotpint, use ggcurve

    plotsint

    -

    For plotsint, use ggcurve.

    +

    For plotsint, use ggcurve

    plot_concurve

    -

    For plot_concurve, use ggcurve.

    +

    For plot_concurve, use ggcurve

    ggconcurve

    -

    For ggconcurve, use ggcurve.

    +

    For ggconcurve, use ggcurve

    meanintervals

    -

    For meanintervals, use curve_mean.

    +

    For meanintervals, use curve_mean

    metaintervals

    -

    For metaintervals, use curve_meta.

    +

    For metaintervals, use curve_meta

    genintervals

    -

    For genintervals, use curve_gen.

    +

    For genintervals, use curve_gen

    corrintervals

    -

    For corrintervals, use curve_corr.

    +

    For corrintervals, use curve_corr

    survintervals

    -

    For survintervals, use curve_surv.

    +

    For survintervals, use curve_surv

    rev_eng

    -

    For rev_eng, use curve_rev.

    +

    For rev_eng, use curve_rev

    diff --git a/inst/WORDLIST b/inst/WORDLIST index 02b5378..5ba450c 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -2,46 +2,76 @@ ai AJPH al Amrhein +analysesusing +ANCOVA arXiv +bca Bca +bcaboot +bcapar +Birnbaum +boostrap +cd ci +confidence +CRC DAS dat databrowser +defaultis +df dichotomization +distrbutions docx doi +Efron emp +epidemiologic estiate et examing forvalues frac functons +ggcurve +ggplot github glm hine Hjort https +Infanger +insertedinto jacknife +kendall KJ leq +levelinto lidocaine Lifecycle +Lippincott +lm logarithmically lointerval mathbf mathST metafor +Narasimhan NL +nullvalue +ORCID parallelization paranthesis paro +pearson postfile +powerpoint Powerpoint ppt +pptx preloaded prio +ProfileLikelihood pvalue Pvalue Rdoc @@ -53,6 +83,7 @@ Rothman Saltelli Scand Schweder +spearman SSRI Stata Strawderman @@ -60,13 +91,21 @@ surprisal Surprisal svalue Svalue +tibble +Tibshirani topost Trafimow +Trucksäss Twoway +typicallywhat upinterval +wald Weisberg wexp widehat +xaxis Xie yi +yxis +Zad zadchow diff --git a/tests/spelling.R b/tests/spelling.R index 13f77d9..6713838 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,6 +1,3 @@ -if (requireNamespace("spelling", quietly = TRUE)) { - spelling::spell_check_test( - vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE - ) -} +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE)

    Lower Limit

    measure

    The type of data being used. If they involve mean differences,

    The type of data being used. If they involve mean differences, +then the "default" option should be used, which is also the default setting. +If the data are ratios, then the "ratio" option should be used.

    steps