wevid: Quantifying Performance of a Binary Classifier Through Weight of Evidence

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Package details

AuthorPaul McKeigue [aut] (<https://orcid.org/0000-0002-5217-1034>), Marco Colombo [ctb, cre] (<https://orcid.org/0000-0001-6672-0623>)
MaintainerMarco Colombo <mar.colombo13@gmail.com>
URL http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html
Package repositoryView on CRAN
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wevid documentation built on Sept. 12, 2019, 5:04 p.m.