DAAG (version 0.37)

cv.binary: Cross-Validation for Regression with a Binary Response

Description

This function gives internal and cross-validation measures of predictive accuracy for regression with a binary response. The data are randomly assigned to a number of `folds'. Each fold is removed, in turn, while the remaining data is used to re-fit the regression model and to predict at the deleted observations.

Usage

cv.binary(obj=frogs.glm, 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

• the order in which folds were deleted
• internal estimate of accuracy
• cross-validation estimate of accuracy

See Also

glm

Examples

data(frogs)
frogs.glm <- glm(pres.abs ~ log(distance) + log(NoOfPools), 
   family=binomial,data=frogs)
cv.binary(frogs.glm)