proc.est {tpAUC} | R Documentation |
Partial AUC Estimation
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. |
smooth |
if |
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
Examples
library('pROC')
data(aSAH)
proc.est(aSAH$outcome, aSAH$s100b, method='expect',threshold=0.8)