auc.nonpara.mw {auRoc} | R Documentation |
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", "DL.corr", "jackknife", "bootstrapP", "bootstrapBCa"), nboot)
x |
a vector of observations from class P. |
y |
a vector of observations from class N. |
conf.level |
confidence level of the interval. The default is 0.95. |
method |
a method used to construct the CI. |
nboot |
number of bootstrap iterations. |
The function implements various methods based on the Mann-Whitney statistic.
Point estimate and lower and upper bounds of the CI of the AUC.
The observations from class P tend to have larger values then that from class N.
Dai Feng, Damjan Manevski, Maja Pohar Perme
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 (2017) 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 26(6) 2603-2621 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
Maja Pohar Perme and Damjan Manevski (2018) Confidence intervals for the Mann-Whitney test. Statistical Methods in Medical Research DOI: 10.1177/0962280218814556
data(petBrainGlioma) y <- subset(petBrainGlioma, grade==1, select="FDG", drop=TRUE) x <- subset(petBrainGlioma, grade==2, select="FDG", drop=TRUE) auc.nonpara.mw(x, y) auc.nonpara.mw(x, y, method="delong")