auc.para.bayes: AUC by the Bayesian MCMC

In auRoc: Various Methods to Estimate the AUC

Description

Obtain the point estimate and the credible interval of the AUC using the Bayesian MCMC.

Usage

   auc.para.bayes(x, y, conf.level=0.95, 
                  dist=c("normalDV", "normalEV", "exponential"),
                  nburn=1000, niter=10000, nthin=1, seed=100)

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.
nburn	number of burn-in. The default is 1000.
niter	number of iterations. The default is 10000.
nthin	number of thinnng interval. The default is 1.
seed	the seed. The default is 100.

Details

Use the Bayesian MCMC to estimate the parameters of the distributions and hence the AUC values.

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

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

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.bayes(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.bayes(x, y, dist="normalDV")
  

auRoc documentation built on April 14, 2020, 6:57 p.m.