auc.para.frequentist {auRoc} | R Documentation |
Obtain the point estimate and the confidence interval of the AUC using some frequentist parametric methods.
auc.para.frequentist(x, y, conf.level=0.95, dist=c("normalDV", "normalEV", "exponential"), method=c("lrstar", "lr", "wald", "RG1", "RG2"))
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. |
dist |
the name of a parametric distribution. |
method |
a method used to construct the CI. |
Use a variety of frequentist methods for different parametric models to estimate the AUC.
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
Giuliana Cortese and Laura Ventura (2013) Accurate higher-order likelihood inference on P(Y < X). Computational Statistics 28(3) 1035-1059
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
Benjamin Reiser and Irwin Guttman (1986) Statistical inference for Pr(Y < X): The normal case. Technometrics 28(3) 253-257
#Example 1 data(petBrainGlioma) y <- subset(petBrainGlioma, grade==1, select="FDG", drop=TRUE) x <- subset(petBrainGlioma, grade==2, select="FDG", drop=TRUE) auc.para.frequentist(x, y, dist="exp") #Example 2 data(petBrainGlioma) y <- subset(petBrainGlioma, grade==1, select="ACE", drop=TRUE) x <- subset(petBrainGlioma, grade==2, select="ACE", drop=TRUE) auc.para.frequentist(x, y, method="RG1")