proc.est: Partial AUC Estimation

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

Description

Estimate the area of region under ROC curve with pre-specific FPR constraint (FPR-pAUC). See Yang et al., 2017 for details.

Usage

proc.est(response, predictor, threshold = 0.9, method = "MW",
  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	numeric; false positive rate (FPR) constraint.
method	methods to estimate FPR-pAUC. MW: Mann-Whitney statistic. expect: method in (2.2) Wang and Chang, 2011. jackknife: jackknife method in Yang et al., 2017.
smooth	if TRUE, the ROC curve is passed to smooth to be smoothed.

Details

This function estimates FPR partial AUC given response, predictor and pre-specific FPR constraint. MW: Mann-Whitney statistic. expect: method in (2.2) Wang and Chang, 2011. jackknife: jackknife method in Yang et al., 2017.

Value

Estimate of FPR partial AUC.

Author(s)

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

See Also

tproc.est, podc.est

Examples

library('pROC')
data(aSAH)
proc.est(aSAH$outcome, aSAH$s100b, method='expect',threshold=0.8)

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