auRoc (version 0.2-1)

auc.nonpara.mw: AUC Based on the Mann-Whitney Statistic

Description

Obtain the point estimate and the confidence interval of the AUC by various methods based on the Mann-Whitney statistic.

Usage

auc.nonpara.mw(x, y, conf.level=0.95, 
                  method=c("newcombe", "pepe", "delong", "DL.corr",
                           "jackknife", "bootstrapP", "bootstrapBCa"), 
                  nboot)

Arguments

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. 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); DL.corr is a method proposed in Perme and Manevski (2018); 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.

nboot

number of bootstrap iterations.

Value

Point estimate and lower and upper bounds of the CI of the AUC.

Details

The function implements various methods based on the Mann-Whitney statistic.

References

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

Examples

# NOT RUN {
  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")
# }