trinROC-package: trinROC: Statistical Tests for Assessing Trinormal ROC Data

In trinROC: Statistical Tests for Assessing Trinormal ROC Data

trinROC: Statistical Tests for Assessing Trinormal ROC Data

Description

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.

Details

See vignette("Overview", package = "trinROC") for an overview of the package. Further, sd(), var() and cov() are chosen with options(trinROC.MLE = TRUE) according to the maximum likelihood estimates (default). Change to sample estimates by setting options(trinROC.MLE = FALSE)

Author(s)

Maintainer: Reinhard Furrer [e-mail] (ORCID)

Authors:

• Samuel Noll [e-mail]

• Annina Cincera [e-mail]

Other contributors:

• Benjamin Reiser [contributor]

• Christos T. Nakas [e-mail] [contributor]

References

Noll, S., Furrer, R., Reiser, B. and Nakas, C. T. (2019). Inference in ROC surface analysis via a trinormal model-based testing approach. Stat, 8(1), e249.

See Also

Useful links:

• https://www.math.uzh.ch/pages/trinROC/

• Report bugs at https://git.math.uzh.ch/reinhard.furrer/trinROC/-/issues

trinROC documentation built on Oct. 4, 2024, 5:10 p.m.