This function is a front end to the stepAIC
function in the
MASS package.
stepwise(mod,
direction = c("backward/forward", "forward/backward", "backward", "forward"),
criterion = c("BIC", "AIC"), ...)
The model selected by stepAIC
.
a model object of a class that can be handled by stepAIC
.
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.
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
.
John Fox jfox@mcmaster.ca
W. N. Venables and B. D. Ripley Modern Applied Statistics Statistics with S, Fourth Edition Springer, 2002.
stepAIC
# 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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