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

Xavier Robin1*, Natacha Turck1, Alexandre Hainard1, Natalia Tiberti1, Frédérique Lisacek2, Jean-Charles Sanchez1 and Markus Müller2*

Author Affiliations

1 Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland

2 Swiss Institute of Bioinformatics, Medical University Centre, Geneva, Switzerland

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BMC Bioinformatics 2011, 12:77  doi:10.1186/1471-2105-12-77

Published: 17 March 2011

Additional files

Additional file 1:

Assessment of the ROC comparison tests. We evaluate the uniformity of the tests under the null hypothesis (ROC curves are not different), and the correlation between the different tests.

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Additional file 2:

Histograms of the frequency of 600 test p-values under the null hypothesis (ROC curves are not different). A: DeLong's paired test, B: DeLong's unpaired test, C: bootstrap paired test (with 10000 replicates), D: bootstrap unpaired test (with 10000 replicates) and E: Venkatraman's test (with 10000 permutations).

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Additional file 3:

Correlations between DeLong and bootstrap paired tests. X axis: DeLong's test; Y-axis: bootstrap test with number of bootstrap replicates. A: 10, B: 100, C: 1000 and D: 10000.

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Additional file 4:

Correlation between DeLong and Venkatraman's test. X axis: DeLong's test; Y-axis: Venkatraman's test with 10000 permutations.

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Additional file 5:

Binormal smoothing. Binormal smoothing with pcvsuite (green, solid) and pROC (black, dashed).

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