pROC v1.15.0

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Display and Analyze ROC Curves

Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

Readme

Build Status AppVeyor build status Codecov coverage CRAN Version Downloads

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

Functions in pROC

Name Description
aSAH Subarachnoid hemorrhage data
ci.auc Compute the confidence interval of the AUC
ci.sp Compute the confidence interval of specificities at given sensitivities
ci.se Compute the confidence interval of sensitivities at given specificities
are.paired Are two ROC curves paired?
coords Coordinates of a ROC curve
auc Compute the area under the ROC curve
ci Compute the confidence interval of a ROC curve
ci.thresholds Compute the confidence interval of thresholds
ci.coords Compute the confidence interval of arbitrary coordinates
has.partial.auc Does the ROC curve have a partial AUC?
lines.roc Add a ROC line to a ROC plot
cov.roc Covariance of two paired ROC curves
coords_transpose Transposing the output of coords
pROC-package pROC
multiclass.roc Multi-class AUC
groupGeneric pROC Group Generic Functions
ggroc.roc Plot a ROC curve with ggplot2
plot.ci Plot confidence intervals
plot.roc Plot a ROC curve
power.roc.test Sample size and power computation for ROC curves
print Print a ROC curve object
smooth Smooth a ROC curve
var.roc Variance of a ROC curve
roc Build a ROC curve
roc.test Compare the AUC of two ROC curves
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Details

Type Package
Date 2019-06-01
Encoding UTF-8
LinkingTo Rcpp
License GPL (>= 3)
URL http://expasy.org/tools/pROC/
BugReports https://github.com/xrobin/pROC/issues
LazyLoad yes
NeedsCompilation yes
Packaged 2019-06-01 07:44:22 UTC; xavier
Repository CRAN
Date/Publication 2019-06-01 09:00:03 UTC

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