stepAIC
Performs stepwise model selection by AIC.
 Keywords
 models
Usage
stepAIC(object, scope, scale = 0,
direction = c("both", "backward", "forward"),
trace = 1, keep = NULL, steps = 1000, use.start = FALSE,
k = 2, …)
Arguments
 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
andlower
, 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
andaov
models (seeextractAIC
for details).  direction

the mode of stepwise search, can be one of
"both"
,"backward"
, or"forward"
, with a default of"both"
. If thescope
argument is missing the default fordirection
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. Typicallykeep
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.)
Details
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
specifies 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.
Value
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).
Note
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.
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
See Also
Examples
library(MASS)
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$anova
cpus1 < cpus
for(v in names(cpus)[2:7])
cpus1[[v]] < cut(cpus[[v]], unique(quantile(cpus[[v]])),
include.lowest = TRUE)
cpus0 < cpus1[, 2:8] # excludes names, authors' predictions
cpus.samp < sample(1:209, 100)
cpus.lm < lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8])
cpus.lm2 < stepAIC(cpus.lm, trace = FALSE)
cpus.lm2$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