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"), ...)

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.

Value

The model selected by stepAIC.

References

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

Examples

Run this code

# NOT RUN {
# 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"))
}
# }