ci.cvAUC_withIC: ci.cvAUC_withIC

In benkeser/predtmle: Small sample estimators of cross-validated prediction metrics

Description

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

Usage

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.

benkeser/predtmle documentation built on May 20, 2019, 5:41 p.m.