auc.para.frequentist {auRoc}

AUC by Frequentist Parametric Methods

Description

Obtain the point estimate and the confidence interval of the AUC using some frequentist parametric methods.

Usage

   auc.para.frequentist(x, y, conf.level=0.95, 
                        dist=c("normalDV", "normalEV", "exponential"),
                        method=c("lrstar", "lr", "wald", "RG1", "RG2"))

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.

dist

the name of a parametric distribution. normalEV stands for normal distributions with equal variance; normalDV stands for normal distributions with unequal variances; exponential stands for exponential distributions. The default is normalDV. It can be abbreviated.

method

a method used to construct the CI. lrstar uses the likelihood ratio test based on higher-order asymptotic results; lr uses the signed log-likelihood ratio test; wald uses the Wald test; RG1 is the approximate "t" solution to the Behrens-Fisher problem; RG2 is the normal approximation to RG1. RG1 and RG2 are for normal distributions. The default is lrstar. It can be abbreviated.

Details

Use a variety of frequentist methods for different parametric models to estimate the AUC.

Value

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

Note

The observations from class P tend to have larger values then that from class N.

Author(s)

Dai Feng

References

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

Examples

  #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")