ci.cvAUC_withIC: ci.cvAUC_withIC
In benkeser/cvma: Cross-validation-based maximal association

Description Usage Arguments Value

This function is nearly verbatim ci.cvAUC from the cvAUC package. The only difference is that it additionally returns estimated influence functions, which allows for variable importance measures to be computed later.

1 2	ci.cvAUC_withIC(predictions, labels, label.ordering = NULL, folds = NULL, confidence = 0.95)

`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.

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.