CVbinary {DAAG}

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)