proc.est: Partial AUC Estimation

Description

Estimate the area of region under ROC curve with pre-specific FPR constraint (FPR-pAUC). See http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html 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) http://www.ncbi.nlm.nih.gov/pubmed/20729218. jackknife: jackknife method in http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html.

smooth

if TRUE, the ROC curve is passed to smooth to be smoothed.

Value

Estimate of FPR partial AUC.

Details

This function estimates FPR partial AUC given response, predictor and pre-specific FPR constraint. MW: Mann-Whitney statistic. expect: method in (2.2) http://www.ncbi.nlm.nih.gov/pubmed/20729218. jackknife: jackknife method in http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html.

Examples

Run this code


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