DAAG (version 1.22)

CVbinary: 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

See Also

glm

Examples

# NOT RUN {
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)
# }