ClusROC: ROC Analysis in Three-Class Classification Problems for
Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:
(i) true class fractions (TCFs) at fixed pairs of thresholds;
(ii) the ROC surface;
(iii) the volume under ROC surface (VUS);
(iv) the optimal pairs of thresholds.
Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.
||R (≥ 3.5.0), stats, utils, graphics, nlme
||rgl, car, numDeriv, ggplot2, ggpubr, snow, doSNOW, foreach
||Duc-Khanh To [aut, cre] (<https://orcid.org/0000-0002-4641-0764>), with contributions from Gianfranco Adimari and Monica Chiogna
||Duc-Khanh To <toduc at stat.unipd.it>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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