pROC
An R package to display and analyze ROC curves.
For more information, see:
- Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: 10.1186/1471-2105-12-77
- The official web page on ExPaSy
- The CRAN page
- My blog
- The FAQ
Stable
The latest stable version is best installed from the CRAN:
install.packages("pROC")Help
Once the library is loaded with library(pROC), you can get help on pROC by typing ?pROC.
Getting started
If you don't want to read the manual first, try the following:
Loading
library(pROC)
data(aSAH)Basic ROC / AUC analysis
roc(aSAH$outcome, aSAH$s100b)
roc(outcome ~ s100b, aSAH)Smoothing
roc(outcome ~ s100b, aSAH, smooth=TRUE) more options, CI and plotting
roc1 <- roc(aSAH$outcome,
aSAH$s100b, percent=TRUE,
# arguments for auc
partial.auc=c(100, 90), partial.auc.correct=TRUE,
partial.auc.focus="sens",
# arguments for ci
ci=TRUE, boot.n=100, ci.alpha=0.9, stratified=FALSE,
# arguments for plot
plot=TRUE, auc.polygon=TRUE, max.auc.polygon=TRUE, grid=TRUE,
print.auc=TRUE, show.thres=TRUE)
# Add to an existing plot. Beware of 'percent' specification!
roc2 <- roc(aSAH$outcome, aSAH$wfns,
plot=TRUE, add=TRUE, percent=roc1$percent) Coordinates of the curve
coords(roc1, "best", ret=c("threshold", "specificity", "1-npv"))
coords(roc2, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv"))Confidence intervals
# Of the AUC
ci(roc2)
# Of the curve
sens.ci <- ci.se(roc1, specificities=seq(0, 100, 5))
plot(sens.ci, type="shape", col="lightblue")
plot(sens.ci, type="bars")
# need to re-add roc2 over the shape
plot(roc2, add=TRUE)
# CI of thresholds
plot(ci.thresholds(roc2))Comparisons
# Test on the whole AUC
roc.test(roc1, roc2, reuse.auc=FALSE)
# Test on a portion of the whole AUC
roc.test(roc1, roc2, reuse.auc=FALSE, partial.auc=c(100, 90),
partial.auc.focus="se", partial.auc.correct=TRUE)
# With modified bootstrap parameters
roc.test(roc1, roc2, reuse.auc=FALSE, partial.auc=c(100, 90),
partial.auc.correct=TRUE, boot.n=1000, boot.stratified=FALSE)Sample size
# Two ROC curves
power.roc.test(roc1, roc2, reuse.auc=FALSE)
power.roc.test(roc1, roc2, power=0.9, reuse.auc=FALSE)
# One ROC curve
power.roc.test(auc=0.8, ncases=41, ncontrols=72)
power.roc.test(auc=0.8, power=0.9)
power.roc.test(auc=0.8, ncases=41, ncontrols=72, sig.level=0.01)
power.roc.test(ncases=41, ncontrols=72, power=0.9)Development
Installing the development version
Download the source code from git, unzip it if necessary, and then type R CMD INSTALL pROC. Alternatively, you can use the devtools package by Hadley Wickham to automate the process (make sure you follow the full instructions to get started):
if (! requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("xrobin/pROC")Check
To run all automated tests, including slow tests:
cd .. # Run from parent directory
VERSION=$(grep Version pROC/DESCRIPTION | sed "s/.\+ //")
R CMD build pROC
RUN_SLOW_TESTS=true R CMD check pROC_$VERSION.tar.gzvdiffr
The vdiffr package is used for visual tests of plots.
To run all the test cases (incl. slow ones) from the command line:
run_slow_tests <- TRUE
vdiffr::manage_cases()To run the checks upon R CMD check, set environment variable NOT_CRAN=1:
NOT_CRAN=1 RUN_SLOW_TESTS=true R CMD check pROC_$VERSION.tar.gzRelease steps
- Get new version to release:
VERSION=$(grep Version pROC/DESCRIPTION | sed "s/.\+ //") && echo $VERSION - Build & check package:
R CMD build pROC && R CMD check --as-cran pROC_$VERSION.tar.gz - Check with slow tests:
NOT_CRAN=1 RUN_SLOW_TESTS=true R CMD check pROC_$VERSION.tar.gz - Check with R-devel:
rhub::check_with_rdevel() - Chec reverse dependencies:
devtools::revdep_check(libpath = rappdirs::user_cache_dir("revdep_lib"), srcpath = rappdirs::user_cache_dir("revdep_src")) - Update
VersionandDateinDESCRIPTION - Update version and date in
NEWS - Create a tag:
git tag v$VERSION - Submit to CRAN