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
The latest stable version is best installed from the CRAN:
install.packages("pROC")
Once the library is loaded with library(pROC), you can get help on pROC by typing ?pROC.
If you don't want to read the manual first, try the following:
library(pROC)
data(aSAH)roc(aSAH$outcome, aSAH$s100b)
roc(outcome ~ s100b, aSAH)roc(outcome ~ s100b, aSAH, smooth=TRUE) 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) coords(roc1, "best", ret=c("threshold", "specificity", "1-npv"))
coords(roc2, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv"))# 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)) # 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) # 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)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):
install.packages("devtools")
devtools::install_github("xrobin/pROC")