This function calculates Area Under the ROC Curve (AUC). The AUC can be defined as the probability that the fit model will score a randomly drawn positive sample higher than a randomly drawn negative sample. This is also equal to the value of the Wilcoxon-Mann-Whitney statistic. This function is a wrapper for functions from the ROCR package.
cvAUC-package
(Package: cvAUC) :
Cross-Validated Area Under the ROC Curve Confidence Intervals
This package contains various 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.
ci.pooled.cvAUC
(Package: cvAUC) :
Confidence Intervals for Cross-validated Area Under the ROC Curve (AUC) Estimates for Pooled Repeated Measures Data
This function calculates influence curve based confidence intervals for cross-validated area under the curve (AUC) estimates, for a pooled repeated measures data set.
cvAUC
(Package: cvAUC) :
Cross-validated Area Under the ROC Curve (AUC)
This function calculates cross-validated area under the ROC curve (AUC) esimates. For each fold, the empirical AUC is calculated, and the mean of the fold AUCs is the cross-validated AUC estimate. The area under the ROC curve is equal to the probability that the classifier will score a randomly drawn positive sample higher than a randomly drawn negative sample. This function is a simple wrapper for the AUC functionality inside the ROCR package.
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