roc3.test {trinROC}

Statistical test function for computing multiple tests on three-class ROC data

Description

A statistical test function that assesses three-class ROC data with the trinormal based ROC test, the trinormal VUS test and the Bootstrap test.

Usage

roc3.test(
  dat,
  type = c("ROC", "VUS", "Bootstrap"),
  paired = FALSE,
  conf.level = 0.95,
  n.boot = 1000,
  p.adjust = FALSE
)

Arguments

dat

A data frame of the following structure: The first column represents a factor with three levels, containing the true class membership of each measurement. The levels are ordered according to the convention of higher values for more severe disease status.

type

A character, specifying which tests are applied to dat. "ROC" implies the trinormal based ROC test, "VUS" the trinormal VUS test and "Bootstrap" the Bootstrap test.

paired

A logical indicating whether data arose from a paired setting. If data is paired, each class must have equal sample size for both classifiers.

conf.level

confidence level of the interval. A numeric value between (0,1) yielding the significance level \alpha=1-\code{conf.level}.

n.boot

An integer incicating the number of Bootstrap replicates sampled to obtain the variance of the VUS. Default is 1000.

p.adjust

A logical, indicating whether a FDR adjustment should be applied to the p-values. Default is FALSE.

Details

For the preliminary assessment of a classifier, different statistical tests have been proposed in the literature. This function can be used for either comparison of single classifiers to a null hypothesis of being not better than a random allocation function or comparison of two classifiers under the null hypothesis of having equal discriminatory power. Depending on the specification of the user, (s)he can apply the trinormal based ROC test (LINK), the test developed by Xiong et. al. or the Bootstrap test or any combination of these tests. More information of the specific tests can be obtained by calling ?functionname. If more than two markers are present, a pairwise comparison between each marker is realized.

Value

A list with components:

Overview

a data frame with number of columns according to number of markers. Rows contain the following information about the makers:

1. Index according to smallest VUS

2. VUS

3. P-values of statistical test specified by type

4. Number of NA's

O.orig

the unsorted Overview.

P.values

a list, containing the upper triangular matrices of the optionally adjusted p-values of the statistical tests chosen by type.

Test.Values

a list, containing the upper triangular matrices of the test values of the statistical tests chosen by type.

Note

If type = "Bootstrap", the Bootstrap test is evaluated. This may take some time, especially with sample sizes > 100.

Examples

data(krebs)
roc3.test(krebs, type = c("ROC", "VUS"), paired = TRUE)[c("Overview","P.values")]