wevid (0.5.1)

Quantifying Performance of a Binary Classifier Through Weight of Evidence.

http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html
http://cran.r-project.org/web/packages/wevid

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 (2018), ). 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.

Maintainer: Marco Colombo
Author(s): Paul McKeigue [aut] (<https://orcid.org/0000-0002-5217-1034>), Marco Colombo [ctb, cre] (<https://orcid.org/0000-0001-6672-0623>)

License: GPL-3

Uses: ggplot2, pROC, reshape2, zoo

Released 12 days ago.