wevid-package: Quantifying performance of a diagnostic test using the...

In pmckeigue/wevid: Weight of evidence for quantifying performance of a binary classifier

Description

This package provides functions for quantifying the performance of a diagnostic test (or any other binary classifier) by calculating and plotting the distributions in cases and noncases of the weight of evidence favouring case over noncase status.

Details

To use it, you should have computed on a test dataset (or on test folds used for cross-validation:

1. The prior probability of case status (this may be just the frequency of cases in the training data.

2. The posterior probability of case status (using the model learned on the training data to predict on the test data)

3. The observed case status (coded as 0=noncase, 1=case).

Author(s)

Paul McKeigue [e-mail]

Citation for the statistical methods used in this package: McKeigue P. Quantifying performance of a diagnostic test as the expected information for discrimination: relation to the C-statistic. Statistical Methods for Medical Research 2018, in press.

See Also

Useful links:

• http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/demoplotw.html

pmckeigue/wevid documentation built on May 29, 2019, 5:41 a.m.