Learn R Programming

mcca (version 0.7.0)

Multi-Category Classification Accuracy

Description

It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) . Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) . Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) . Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) . Li et al. (2018) . We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) .

Copy Link

Version

Install

install.packages('mcca')

Monthly Downloads

239

Version

0.7.0

License

GPL

Issues

Pull Requests

Stars

Forks

Maintainer

Ming Gao

Last Published

December 20th, 2019

Functions in mcca (0.7.0)

pm

Calculate Probability Matrix
print.mcca.ccp

Print Method for mcca ccp class
print.mcca.hum

Print Method for mcca hum class
print.mcca.rsq

Print Method for mcca rsq class
print.mcca.pdi

Print Method for mcca pdi class
rsq

Calculate RSQ Value
idi

Calculate IDI Value
plot.mcca.hum

Plot 3D ROC surface
ccp

Calculate CCP Value
hum

Calculate HUM Value
ests

Inference for Accuracy Measures based on Bootstrap
nri

Calculate NRI Value
mcca-package

Diagnostic accuracy methods for classifiers
pdi

Calculate PDI Value
estp

Inference for Accuracy Improvement Measures based on Bootstrap