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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-03-17

AUTHORS

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

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. More... »

PAGES

77-77

Journal

TITLE

BMC Bioinformatics

ISSUE

1

VOLUME

12

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-12-77

    DOI

    http://dx.doi.org/10.1186/1471-2105-12-77

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1014582441

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21414208


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