tproc.est: Two-Way Partial AUC Estimation

In tpAUC: Estimation and Inference of Two-Way pAUC, pAUC and pODC

Description

Estimate the area of region under ROC curve under pre-specific FPR/TPR constraints (two-way partial AUC). See Yang et al., 2016 for details.

Usage

tproc.est(response, predictor, threshold = c(1, 0), smooth = FALSE)

Arguments

response	a factor, numeric or character vector of responses; typically encoded with 0 (negative) and 1 (positive). Only two classes can be used in a ROC curve. If its levels are not 0 and 1, the first level will be defaultly regarded as negative.
predictor	a numeric vector of the same length than response, containing the predicted value of each observation. An ordered factor is coerced to a numeric.
threshold	a length-two numeric vector; the first element is FPR threshold, the second is TPR.
smooth	if TRUE, the ROC curve is passed to smooth to be smoothed.

Details

This function estimates two-way partial AUC given response, predictor and pre-specific FPR/TPR constraints.

Value

Estimate of two-way partial AUC.

Author(s)

Hanfang Yang, Kun Lu, Xiang Lyu, Feifang Hu, Yichuan Zhao.

See Also

roc, podc.est, proc.est

Examples

library('pROC')
data(aSAH)
tproc.est(aSAH$outcome, aSAH$s100b, threshold=c(0.8,0.2))

tpAUC documentation built on May 1, 2019, 8:44 p.m.