R: Weight of Evidence for Quantifying Performance of a Binary Classifier

# Weight of Evidence for Quantifying Performance of a Binary Classifier

## Documentation for package ‘wevid’ version 0.7.0

- DESCRIPTION file.

## Help Pages

wevid-package | Quantifying performance of a diagnostic test using the sampling distribution of the weight of evidence favouring case over noncase status
auroc.model | Compute area under the ROC curve according to model-based densities
cleveland | Example dataset based on cross-validated prediction of outcome in the Cleveland Heart Study
lambda.model | Compute the expected information for discrimination (expected weight of evidence) from the model-based densities
means.densities | Means of densities in cases and controls
plotcumfreqs | Plot the cumulative frequency distributions in cases and in controls
plotroc | Plot crude and model-based ROC curves
plotW | plot log case/control density ratio against weight of evidence as a check that the densities are mathematically consistent
plotWdists | Plot the distribution of the weight of evidence in cases and in controls
prop.belowthreshold | Proportions of cases and controls below a given threshold of W (natural logs)
Wdensities.fromraw | Adjust the crude densities of weights of evidence in cases and controls to make them mathematically consistent
Wdensities.mix | Compute smoothed densities for a spike-slab mixture distribution
Wdensities.unadjusted | Calculate the unadjusted smoothed densities of W in cases and in controls
weightsofevidence | Calculate weights of evidence in natural log units
wevid | Quantifying performance of a diagnostic test using the sampling distribution of the weight of evidence favouring case over noncase status
wtrue.results | Summary evaluation of predictive performance