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An R package to display and analyze ROC curves.

For more information, see:

  1. 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
  2. The official web page on ExPaSy
  3. The CRAN page
  4. My blog
  5. The FAQ


The latest stable version is best installed from the CRAN:


Getting started

If you don't want to read the manual first, try the following:



Basic ROC / AUC analysis

roc(aSAH$outcome, aSAH$s100b)
roc(outcome ~ s100b, aSAH)


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,
            # 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

# Of the curve <-, specificities=seq(0, 100, 5))
plot(, type="shape", col="lightblue")
plot(, type="bars")

# need to re-add roc2 over the shape
plot(roc2, add=TRUE)

# CI of thresholds


    # 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)

Getting Help

If you still can't find an answer, you can:


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")


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.gz


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

To run the checks upon R CMD check, set environment variable NOT_CRAN=1:


Release steps

  1. Get new version to release: VERSION=$(grep Version pROC/DESCRIPTION | sed "s/.\+ //") && echo $VERSION
  2. Build & check package: R CMD build pROC && R CMD check --as-cran pROC_$VERSION.tar.gz
  3. Check with slow tests: NOT_CRAN=1 RUN_SLOW_TESTS=true R CMD check pROC_$VERSION.tar.gz
  4. Check with R-devel: rhub::check_for_cran()
  5. Check reverse dependencies: revdepcheck::revdep_check(num_workers=8, timeout = as.difftime(60, units = "mins"))
  6. Update Version and Date in DESCRIPTION
  7. Update version and date in NEWS
  8. Create a tag: git tag v$VERSION && git push --tags
  9. Submit to CRAN

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pROC documentation built on Sept. 5, 2021, 5:34 p.m.