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trinROC

This package helps to assess three-class Receiver Operating Characteristic (ROC) type data. It provides several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.

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Version

Install

install.packages('trinROC')

Monthly Downloads

143

Version

0.7

License

LGPL-2.1

Maintainer

Reinhard Furrer

Last Published

October 4th, 2024

Functions in trinROC (0.7)

trinROC-package

trinROC: Statistical Tests for Assessing Trinormal ROC Data
emp.vus

Empirical VUS calculation
boxcoxROC

Box-Cox transformation on three-class ROC data
trinROC.test

Trinormal based ROC test
rocsurf.trin

Trinormal ROC surface plot
roc3.test

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

Determine equidistant means of trinormal ROC data simulation
krebs

Synthetic small data set to investigate three-class ROC data.
roc.eda

Exploratory data analysis for a three-class ROC marker
boot.test

Bootstrap test for three-class ROC data
rocsurf.emp

Empirical ROC surface plot
trinVUS.test

Trinormal VUS test
cancer

Synthetic data set to investigate three-class ROC data.