pROC 1.4 is here

After several months without any new version, here is finally pROC version 1.4!

This long-awaited version introduces three new notable features:

Multiclass ROC curves

Multi-class AUC is a precious generalization of ROC curves for tests with more than one outcome. We used the approach by Hand and Till (2001)1 that averages all the ROC curves than can be built with the given levels. Here is a basic example:

multiclass.roc(aSAH$gos6, aSAH$s100b)

See ?multiclass.roc for more details.

Unpaired ROC tests

The second feature is the unpaired DeLong2 and bootstrap ROC tests. Until now, only paired ROC curves (when measurements were done on the same sample) were available. Now even unpaired ROC curves are supported.

rocs100 <- roc(aSAH$outcome, aSAH$s100b)
rocwfns <- roc(aSAH$outcome, aSAH$wfns)
roc.test(rocs100, rocwfns, paired=FALSE)

Or with a truly unpaired context:

rocwfns.subset <- roc(aSAH$outcome[1:100], aSAH$wfns[1:100])
roc.test(rocs100, rocwfns.subset)

Specificity and sensitivity tests

The last feature introduced in pROC 1.4 is the specificity and sensitivity tests in roc.test. Instead of testing the AUC, it will test if the ROC curves have the same sensitivity/specificity at the given level of specificity/sensitivity, as described by Pepe et al. (2009)3. Here are two examples:

roc.test(roc1, roc2, method="specificity", specificity=0.9)
roc.test(roc1, roc2, method="sensitivity", sensitivity=0.9)


Here is the full change log:

As usual, you can find the new version on ExPASy and on the CRAN. To update, type update.packages() or install.packages("pROC") if you want to update pROC only.

Xavier Robin
Published Friday, January 21, 2011 20:38 CET
Permalink: /blog/2011/01/21/proc-1.4-released
Tags: pROC
Comments: 0


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