Obtain the point estimate and the confidence interval of the AUC by various methods based on the Mann-Whitney statistic.
auc.nonpara.mw(x, y, conf.level=0.95,
method=c("newcombe", "pepe", "delong",
"jackknife", "bootstrapP", "bootstrapBCa"),
nboot)Point estimate and lower and upper bounds of the CI of the AUC.
a vector of observations from class P.
a vector of observations from class N.
confidence level of the interval. The default is 0.95.
a method used to construct the CI. newcombe is
the method recommended in Newcombe (2006); pepe is the method
proposed in Pepe (2003); delong is the method proposed in
Delong et al. (1988); jackknife uses the
jackknife method; bootstrapP uses the bootstrap with
percentile CI; bootstrapBCa uses bootstrap with
bias-corrected and accelerated CI. The default is newcombe. It can be abbreviated.
number of bootstrap iterations.
The function implements various methods based on the Mann-Whitney statistic.
Elizabeth R Delong, David M Delong, and Daniel L Clarke-Pearson (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 837-845
Dai Feng, Giuliana Cortese, and Richard Baumgartner (2015) A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size. Statistical Methods in Medical Research DOI: 10.1177/0962280215602040
Robert G Newcombe (2006) Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 2: asymptotic methods and evaluation. Statistics in medicine 25(4) 559-573
Margaret Sullivan Pepe (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press
Qin, G., & Hotilovac, L. (2008). Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Statistical Methods in Medical Research, 17(2), pp. 207-21