stepwise: Stepwise Model Selection

Description

This function is a front end to the stepAIC function in the MASS package.

Usage

stepwise(mod, 
    direction = c("backward/forward", "forward/backward", "backward", "forward"), 
    criterion = c("BIC", "AIC"), ...)

Value

The model selected by stepAIC.

Arguments

mod: a model object of a class that can be handled by stepAIC.
direction: if "backward/forward" (the default), selection starts with the full model and eliminates predictors one at a time, at each step considering whether the criterion will be improved by adding back in a variable removed at a previous step; if "forward/backwards", selection starts with a model including only a constant, and adds predictors one at a time, at each step considering whether the criterion will be improved by removing a previously added variable; "backwards" and "forward" are similar without the reconsideration at each step.
criterion: for selection. Either "BIC" (the default) or "AIC". Note that stepAIC labels the criterion in the output as "AIC" regardless of which criterion is employed.
...: arguments to be passed to stepAIC.

Author

John Fox jfox@mcmaster.ca

References

W. N. Venables and B. D. Ripley Modern Applied Statistics Statistics with S, Fourth Edition Springer, 2002.

Examples

Run this code

# adapted from ?stepAIC in MASS
if (require(MASS)){
data(birthwt)
bwt <- with(birthwt, {
    race <- factor(race, labels = c("white", "black", "other"))
    ptd <- factor(ptl > 0)
    ftv <- factor(ftv)
    levels(ftv)[-(1:2)] <- "2+"
    data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
               ptd, ht = (ht > 0), ui = (ui > 0), ftv)
})
birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
print(stepwise(birthwt.glm, trace = FALSE))
print(stepwise(birthwt.glm, direction="forward/backward"))
}

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