compute_ROC-internal {sMSROC}

Weighted empirical ROC curve estimator

Description

Computes the weighted empirical ROC curve estimator associated to the input biomarker.

Usage

compute_ROC(marker, probs, grid)

Arguments

marker

vector with the biomarker values.

probs

vector containing the probabilities corresponding to the predictive model.

grid

grid size.

Details

This function computes the weighted empirical estimators for the sensitivity (SE) and specificity (SP) using as weights the probabilities given by the predictive model. Then, the ROC curve is approximated through linear interpolation of 1 - SP and SE and computed at a partition of the [0,1] interval of size grid.

Value

The returned value is a list with the following components:

SE

vector with the weighted empirical estimator of the sensitivity.

SP

vector with the weighted empirical estimator of the specificity.

u

vector containing the points between 0 and 1 at which the ROC curve estimator will be computed. Its size is determined by the grid parameter.

ROC

ROC curve approximated at each point of the vector u.

auc

area under the weighted empirical ROC curve estimator.

marker

vector with the ordered biomarker values.

probs

vector with the probabilities of the predictive model corresponding to each biomarker value.

See Also

sMSbinout, sMStimerc and sMStimeic