Skip to content
This repository

Display and analyze ROC curves in R and S+

branch: master

Fetching latest commit…

Octocat-spinner-32-eaf2f5

Cannot retrieve the latest commit at this time

Octocat-spinner-32 R
Octocat-spinner-32 data
Octocat-spinner-32 inst
Octocat-spinner-32 man
Octocat-spinner-32 src
Octocat-spinner-32 .gitattributes
Octocat-spinner-32 .gitignore
Octocat-spinner-32 DESCRIPTION
Octocat-spinner-32 NAMESPACE
Octocat-spinner-32 NEWS
Octocat-spinner-32 README.md
Octocat-spinner-32 default.Rproj
README.md

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

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

Download the source code from git, unzip it if necessary, and then type R CMD INSTALL pROC. Alternatively, you can use the devtool package by Hadley Wickham to automate the process (make sure you follow the full instructions to get started):

install.packages("devtools")
library("devtools")
install_github(repo = "pROC", username = "xrobin", ref = "master")
Something went wrong with that request. Please try again.