summary-densities: Summary evaluation of predictive performance

In wevid: Quantifying Performance of a Binary Classifier Through Weight of Evidence

Description

Summary evaluation of predictive performance

Usage

## S3 method for class 'Wdensities'
summary(object, ...)

## S3 method for class 'Wdensities'
mean(x, ...)

auroc.crude(densities)

auroc.model(densities)

lambda.crude(densities)

lambda.model(densities)

Arguments

object, x, densities	Densities object produced by Wdensities.
...	Further arguments passed to or from other methods. These are currently ignored.

Value

summary returns a data frame that reports the number of cases and controls, the test log-likelihood, the crude and model-based C-statistic and expected weight of evidence Lambda.

mean returns a numeric vector listing the mean densities of the weight of evidence in controls and in cases.

auroc.crude and auroc.model return the area under the ROC curve according to the crude and the model-based densities of weight of evidence, respectively.

lambda.crude and lambda.model return the expected weight of evidence (expected information for discrimination) in bits from the crude and the model-based densities, respectively.

Examples

data(cleveland)
densities <- with(cleveland, Wdensities(y, posterior.p, prior.p))

summary(densities)
mean(densities)
auroc.model(densities)
lambda.model(densities)

wevid documentation built on Sept. 12, 2019, 5:04 p.m.