This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive fraction. For the Bayesian methods, we provide tools for assessing model fit via posterior predictive checks, while the model choice can be carried out via several information criteria. Numerical and graphical outputs are provided for all methods. This is the only package implementing Bayesian procedures for ROC curves.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-066.zip
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For attribution, please cite this work as
Rodríguez-Álvarez & Inácio, "The R Journal: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates", The R Journal, 2021
BibTeX citation
@article{RJ-2021-066,
author = {Rodríguez-Álvarez, María Xosé and Inácio, Vanda},
title = {The R Journal: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-066},
doi = {10.32614/RJ-2021-066},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {525-555}
}