Quantifying Performance of a Binary Classifier Through Weight of Evidence

Documentation for package ‘wevid’ version 0.6.2

wevid-package	Quantifying performance of a diagnostic test using the sampling distribution of the weight of evidence favouring case over noncase status
auroc.crude	Summary evaluation of predictive performance
auroc.model	Summary evaluation of predictive performance
cleveland	Example datasets
fitonly	Example datasets
lambda.crude	Summary evaluation of predictive performance
lambda.model	Summary evaluation of predictive performance
mean.Wdensities	Summary evaluation of predictive performance
pima	Example datasets
plotcumfreqs	Plot the cumulative frequency distributions in cases and in controls
plotroc	Plot crude and model-based ROC curves
plotWdists	Plot the distribution of the weight of evidence in cases and in controls
prop.belowthreshold	Proportions of cases and controls below a threshold of weight of evidence
recalibrate.p	Recalibrate posterior probabilities
summary-densities	Summary evaluation of predictive performance
summary.Wdensities	Summary evaluation of predictive performance
Wdensities	Compute densities of weights of evidence in cases and 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
wevid.datasets	Example datasets