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Comparative Study
. 2011 Mar 17:12:77.
doi: 10.1186/1471-2105-12-77.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin  1 , Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez, Markus Müller
Affiliations
Comparative Study

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin et al. BMC Bioinformatics. .

Abstract

Background: Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.

Results: With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.

Conclusions: pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Figures

Figure 1
Figure 1
ROC curves of WFNS and S100β. ROC curves of WFNS (blue) and S100β (green). The black bars are the confidence intervals of WFNS for the threshold 4.5 and the light green area is the confidence interval shape of S100β. The vertical light grey shape corresponds to the pAUC region. The pAUC of both empirical curves is printed in the middle of the plot, with the p-value of the difference computed by a bootstrap test on the right.
Figure 2
Figure 2
ROC curve of WFNS and smoothing. Empirical ROC curve of WFNS is shown in grey with three smoothing methods: binormal (blue), density (green) and normal distribution fit (red).
Figure 3
Figure 3
Screenshot of pROC in S+ for smoothing WFNS ROC curve. Top left: the General tab, where data is entered. Top right: the details about smoothing. Bottom left: the details for the plot. Checking the box "Add to existing plot" allows drawing several curves on a plot. Bottom right: the result in the standard S+ plot device.
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