CVbinary: Cross-Validation for Regression with a Binary Response

In DAAG: Data Analysis and Graphics Data and Functions

Cross-Validation for Regression with a Binary Response

Description

These functions give training (internal) and cross-validation measures of predictive accuracy for regression with a binary response. The data are randomly divided between a number of ‘folds’. Each fold is removed, in turn, while the remaining data are used to re-fit the regression model and to predict at the omitted observations.

Usage

CVbinary(obj, rand=NULL, nfolds=10, print.details=TRUE)

cv.binary(obj, rand=NULL, nfolds=10, print.details=TRUE)

Arguments

obj	a glm object
rand	a vector which assigns each observation to a fold
nfolds	the number of folds
print.details	logical variable (TRUE = print detailed output, the default)

Value

cvhat	predicted values from cross-validation
internal	internal or (better) training predicted values
training	training predicted values
acc.cv	cross-validation estimate of accuracy
acc.internal	internal or (better) training estimate of accuracy
acc.training	training estimate of accuracy

Note

The term ‘training’ seems preferable to the term ‘internal’ in connection with predicted values, and the accuracy measure, that are based on the observations used to derive the model.

Author(s)

J.H. Maindonald

See Also

glm

Examples

frogs.glm <- glm(pres.abs ~ log(distance) + log(NoOfPools),
                 family=binomial,data=frogs)
CVbinary(frogs.glm)
mifem.glm <- glm(outcome ~ ., family=binomial, data=mifem)
CVbinary(mifem.glm)

DAAG documentation built on May 29, 2024, 9:13 a.m.