cvAUC-package: Cross-Validated Area Under the ROC Curve Confidence Intervals

In cvAUC: Cross-Validated Area Under the ROC Curve Confidence Intervals

Cross-Validated Area Under the ROC Curve Confidence Intervals

Description

Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which compute confidence intervals for cross-validated AUC estimates based on influence curves of both regular i.i.d and pooled repeated measures data. One benefit to using influence function based confidence intervals is that they require much less computation time than bootstrapping methods. The utility function, cvAUC, which computes cross-validated AUC, is a wrapper for functions from the ROCR package.

Details

Package:	cvAUC
Type:	Package
Version:	1.1.4
Date:	2022-01-17
License:	Apache License (== 2.0)

See the help files for the following functions for more information:

cvAUC, ci.cvAUC, ci.pooled.cvAUC

Note

This work was supported by the Doris Duke Charitable Foundation Grant No. 2011042.

Author(s)

Erin LeDell, Maya Petersen, Mark van der Laan

Maintainer: Erin LeDell <[e-mail]>

References

LeDell, Erin; Petersen, Maya; van der Laan, Mark. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron. J. Statist. 9 (2015), no. 1, 1583–1607. doi:10.1214/15-EJS1035. http://projecteuclid.org/euclid.ejs/1437742107.

M. J. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Series in Statistics. Springer, first edition, 2011.

Tobias Sing, Oliver Sander, Niko Beerenwinkel, and Thomas Lengauer. ROCR: Visualizing classifier performance in R. Bioinformatics, 21(20):3940-3941, 2005.

See Also

https://cran.r-project.org/package=ROCR

cvAUC documentation built on March 18, 2022, 7:58 p.m.