proc.ci: Partial AUC Inference

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/proc.ci.R

Description

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

Usage

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proc.ci(response, predictor, cp = 0.95, threshold = 0.9, method = "MW")

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 0/1, 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.

cp

numeric; coverage probability of confidence interval.

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.

Details

This function infers FPR partial AUC given response, predictor and pre-specific FPR constraint. MW: Mann-Whitney statistic. method in Yang et al., 2017 adapted from Wang and Chang, 2011. jackknife: jackknife method in Yang et al., 2017.

Value

Confidence interval of FPR partial AUC.

Author(s)

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

See Also

tproc.est, podc.ci

Examples

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library('pROC')
data(aSAH)
proc.ci(aSAH$outcome, aSAH$s100b, cp=0.95 ,threshold=0.9,method='expect')

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