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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 on ExPaSy
  3. The CRAN page
  4. My blog
  5. 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.gz

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
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.gz

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_$VERSION.tar.gz
  4. Check with R-devel: rhub::check_with_rdevel()
  5. Chec reverse dependencies: devtools::revdep_check(libpath = rappdirs::user_cache_dir("revdep_lib"), srcpath = rappdirs::user_cache_dir("revdep_src"))
  6. Update Version and Date in DESCRIPTION
  7. Update version and date in NEWS
  8. Create a tag: git tag v$VERSION
  9. Submit to CRAN

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Version

Install

install.packages('pROC')

Monthly Downloads

160,346

Version

1.14.0

License

GPL (>= 3)

Maintainer

Last Published

March 12th, 2019

Functions in pROC (1.14.0)

coords

Coordinates of a ROC curve
print

Print a ROC curve object
cov.roc

Covariance of two paired ROC curves
roc

Build a ROC curve
power.roc.test

Sample size and power computation for ROC curves
has.partial.auc

Does the ROC curve have a partial AUC?
plot.roc

Plot a ROC curve
ci.auc

Compute the confidence interval of the AUC
roc.test

Compare the AUC of two ROC curves
ci.coords

Compute the confidence interval of arbitrary coordinates
ggroc.roc

Plot a ROC curve with ggplot2
lines.roc

Add a ROC line to a ROC plot
pROC-package

pROC
multiclass.roc

Multi-class AUC
var.roc

Variance of a ROC curve
smooth

Smooth a ROC curve
plot.ci

Plot confidence intervals
auc

Compute the area under the ROC curve
ci

Compute the confidence interval of a ROC curve
ci.se

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

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

Compute the confidence interval of thresholds
groupGeneric

pROC Group Generic Functions
aSAH

Subarachnoid hemorrhage data
are.paired

Are two ROC curves paired?