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), <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.
Version: | 0.6 |
Depends: | R (≥ 2.10) |
Imports: | ggplot2, mclust, pROC, reshape2, zoo |
Published: | 2019-04-04 |
Author: | Paul McKeigue |
Maintainer: | Marco Colombo <mar.colombo13 at gmail.com> |
License: | GPL-3 |
URL: | http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html |
NeedsCompilation: | no |
Citation: | wevid citation info |
CRAN checks: | wevid results |
Reference manual: | wevid.pdf |
Package source: | wevid_0.6.tar.gz |
Windows binaries: | r-devel: wevid_0.6.zip, r-release: wevid_0.6.zip, r-oldrel: wevid_0.6.zip |
OS X binaries: | r-release: wevid_0.6.tgz, r-oldrel: wevid_0.5.2.tgz |
Old sources: | wevid archive |
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