R: Stepwise Model Selection

stepwise {Rcmdr}

R Documentation

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.

Author(s)

John Fox jfox@mcmaster.ca

References

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

Examples

# adapted from ?stepAIC in MASS
require(MASS)
example(birthwt)
birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
stepwise(birthwt.glm, trace = FALSE)
stepwise(birthwt.glm, direction="forward/backward")

[Package Rcmdr version 1.8-4 Index]