stepAIC {MASS}  R Documentation 
Performs stepwise model selection by exact AIC.
stepAIC(object, scope, scale = 0, direction = c("both", "backward", "forward"), trace = 1, keep = NULL, steps = 1000, use.start = FALSE, k = 2, ...)
object 
an object representing a model of an appropriate class. This is used as the initial model in the stepwise search. 
scope 
defines the range of models examined in the stepwise search.
This should be either a single formula, or a list containing
components upper and lower , both formulae. See the
details for how to specify the formulae and how they are used.

scale 
used in the definition of the AIC statistic for selecting the models,
currently only for lm , aov and
glm models.

direction 
the mode of stepwise search, can be one of "both" ,
"backward" , or "forward" , with a default of "both" .
If the scope argument is missing the default for
direction is "backward" .

trace 
if positive, information is printed during the running of
stepAIC .
Larger values may give more information on the fitting process.

keep 
a filter function whose input is a fitted model object and the
associated AIC statistic, and whose output is arbitrary.
Typically keep will select a subset of the components of
the object and return them. The default is not to keep anything.

steps 
the maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early. 
use.start 
if true the updated fits are done starting at the linear predictor for
the currently selected model. This may speed up the iterative
calculations for glm (and other fits), but it can also slow them
down. Not used in R.

k 
the multiple of the number of degrees of freedom used for the penalty.
Only k = 2 gives the genuine AIC: k = log(n) is
sometimes referred to as BIC or SBC.

... 
any additional arguments to extractAIC . (None are currently used.)

The set of models searched is determined by the scope
argument.
The righthandside of its lower
component is always included
in the model, and righthandside of the model is included in the
upper
component. If scope
is a single formula, it
specifes the upper
component, and the lower
model is
empty. If scope
is missing, the initial model is used as the
upper
model.
Models specified by scope
can be templates to update
object
as used by update.formula
.
There is a potential problem in using glm
fits with a
variable scale
, as in that case the deviance is not simply
related to the maximized loglikelihood. The glm
method for
extractAIC
makes the
appropriate adjustment for a gaussian
family, but may need to be
amended for other cases. (The binomial
and poisson
families have fixed scale
by default and do not correspond
to a particular maximumlikelihood problem for variable scale
.)
Where a conventional deviance exists (e.g. for lm
, aov
and glm
fits) this is quoted in the analysis of variance table:
it is the unscaled deviance.
the stepwiseselected model is returned, with up to two additional
components. There is an "anova"
component corresponding to the
steps taken in the search, as well as a "keep"
component if the
keep=
argument was supplied in the call. The
"Resid. Dev"
column of the analysis of deviance table refers
to a constant minus twice the maximized log likelihood: it will be a
deviance only in cases where a saturated model is welldefined
(thus excluding lm
, aov
and survreg
fits,
for example).
The model fitting must apply the models to the same dataset. This may
be a problem if there are missing values and an na.action
other than
na.fail
is used (as is the default in R).
We suggest you remove the missing values first.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
quine.hi < aov(log(Days + 2.5) ~ .^4, quine) quine.nxt < update(quine.hi, . ~ .  Eth:Sex:Age:Lrn) quine.stp < stepAIC(quine.nxt, scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1), trace = FALSE) quine.stp$anova cpus1 < cpus attach(cpus) for(v in names(cpus)[2:7]) cpus1[[v]] < cut(cpus[[v]], unique(quantile(cpus[[v]])), include.lowest = TRUE) detach() cpus0 < cpus1[, 2:8] # excludes names, authors' predictions cpus.samp < sample(1:209, 100) cpus.lm < lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8]) cpus.lm2 < stepAIC(cpus.lm, trace = FALSE) cpus.lm2$anova example(birthwt) birthwt.glm < glm(low ~ ., family = binomial, data = bwt) birthwt.step < stepAIC(birthwt.glm, trace = FALSE) birthwt.step$anova birthwt.step2 < stepAIC(birthwt.glm, ~ .^2 + I(scale(age)^2) + I(scale(lwt)^2), trace = FALSE) birthwt.step2$anova quine.nb < glm.nb(Days ~ .^4, data = quine) quine.nb2 < stepAIC(quine.nb) quine.nb2$anova