Originally invented for the detection of radar signals, they were soon applied to psychology and medical fields such as radiology. ROC curves do not depend on class probabilities, facilitating their interpretation and comparison across different data sets. It shows the sensitivity (the proportion of correctly classified positive observations) and specificity (the proportion of correctly classified negative observations) as the output threshold is moved over the range of all possible values. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.Ī ROC plot displays the performance of a binary classification method with continuous or discrete ordinal output. It is accessible at under the GNU General Public License.
pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. PROC is a package for R and S+ specifically dedicated to ROC analysis. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Intermediary and final results are visualised in user-friendly interfaces.
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. 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. However, conclusions are often reached through inconsistent use or insufficient statistical analysis.
Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications.
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