AUC {cvAUC} R Documentation

## Area Under the ROC Curve

### Description

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.

### Usage

AUC(predictions, labels, label.ordering = NULL)


### Arguments

 predictions A vector of predictions, or predicted probabilities, for each observation. labels A binary vector containing the true values for each observation. Must have the same length as predictions. label.ordering The default ordering of the classes can be changed by supplying a vector containing the negative and the positive class label (negative label first, positive label second).

### Value

The value returned is the Area Under the ROC Curve (AUC).

### Author(s)

Erin LeDell oss@ledell.org

### References

References to the underlying ROCR code, used to calculate area under the ROC curve, can be found on the ROCR homepage at: https://ipa-tys.github.io/ROCR/

prediction, performance, cvAUC, ci.cvAUC, ci.pooled.cvAUC

### Examples

library(cvAUC)
auc <- AUC(ROCR.simple$predictions, ROCR.simple$labels)