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pROC

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
  3. The CRAN page
  4. My blog
  5. The FAQ

Stable

The latest stable version is best installed from the CRAN:

install.packages("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)

Getting Help

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

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@develop")

Check

To run all automated tests and R checks, 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

Or from an R command prompt with devtools:

devtools::check()

Tests

To run automated tests only from an R command prompt:

run_slow_tests <- TRUE  # Optional, include slow tests
devtools::test()

vdiffr

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
devtools::test() # Must run the new tests
testthat::snapshot_review()

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.gz

AppVeyor Build Cache

By default, AppVeyor stores a build cache containing installed dependencies. Sometimes you want to clean the cache, for instance if a Graphics API versionmismatch occurs on R-devel, indicating that ggplot2 was built with a previous version of R. For this you need the AppVeyor API key to make a DELETE request:

export APPVEYOR_TOKEN="<your-api-token>"
curl -H "Authorization: Bearer $APPVEYOR_TOKEN" -H "Content-Type: application/json" -X DELETE https://ci.appveyor.com/api/projects/xrobin/pROC/buildcache

Release steps

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

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Version

Install

install.packages('pROC')

Monthly Downloads

110,917

Version

1.19.0.1

License

GPL (>= 3)

Maintainer

Xavier Robin

Last Published

July 31st, 2025

Functions in pROC (1.19.0.1)

ci.thresholds

Compute the confidence interval of thresholds
aSAH

Subarachnoid hemorrhage data
ci.se

Compute the confidence interval of sensitivities at given specificities
auc

Compute the area under the ROC curve
coords

Coordinates of a ROC curve
are.paired

Are two ROC curves paired?
ci

Compute the confidence interval of a ROC curve
ggroc.roc

Plot a ROC curve with ggplot2
plot.ci

Plot confidence intervals
ci.auc

Compute the confidence interval of the AUC
coords_transpose

Transposing the output of coords
ci.sp

Compute the confidence interval of specificities at given sensitivities
ci.coords

Compute the confidence interval of arbitrary coordinates
geom_polygon_auc

Add an AUC polygon to a ggroc plot
has.partial.auc

Does the ROC curve have a partial AUC?
groupGeneric

pROC Group Generic Functions
cov.roc

Covariance of two paired ROC curves
pROC-package

pROC
lines.roc

Add a ROC line to a ROC plot
multiclass.roc

Multi-class AUC
print

Print a ROC curve object
var.roc

Variance of a ROC curve
smooth

Smooth a ROC curve
roc

Build a ROC curve
plot.roc

Plot a ROC curve
roc.test

Compare two ROC curves
power.roc.test

Sample size and power computation for ROC curves