ci.cvAUC_withIC

This function is nearly verbatim ci.cvAUC from the cvAUC package. The only difference is that it additionally returns estimated influence functions.

ci.cvAUC_withIC(
  predictions,
  labels,
  label.ordering = NULL,
  folds = NULL,
  confidence = 0.95
)

Arguments

predictions	A vector, matrix, list, or data frame containing the predictions.
labels	A vector, matrix, list, or data frame containing the true class labels. Must have the same dimensions 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).
folds	If specified, this must be a vector of fold ids equal in length to predictions and labels, or a list of length V (for V-fold cross-validation) of vectors of indexes for the observations contained in each fold. The folds argument must only be specified if the predictions and labels arguments are vectors.
confidence	number between 0 and 1 that represents confidence level.

Value

A list containing the following named elements:

cvAUC

Cross-validated area under the curve estimate.

se

Standard error.

ci

A vector of length two containing the upper and lower bounds for the confidence interval.

confidence

A number between 0 and 1 representing the confidence.

ic

A vector of the influence function evaluated at observations.