[aut],
Marco Colombo
[ctb, cre]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.
| Version: | 0.6.2 |
| Depends: | R (≥ 2.10) |
| Imports: | ggplot2, mclust, pROC (≥ 1.9), reshape2, zoo |
| Suggests: | testthat (≥ 2.0.0) |
| Published: | 2019-09-12 |
| Author: | Paul McKeigue
[aut],
Marco Colombo
[ctb, cre] |
| 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_0.6.2.tar.gz |
| Windows binaries: | r-devel: wevid_0.6.2.zip, r-release: wevid_0.6.2.zip, r-oldrel: wevid_0.6.2.zip |
| macOS binaries: | r-release: wevid_0.6.2.tgz, r-oldrel: wevid_0.6.2.tgz |
| Old sources: | wevid archive |
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