| 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 and aov models
(see extractAIC for details).
|
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 right-hand-side of its lower component is always included
in the model, and right-hand-side 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 log-likelihood. 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 maximum-likelihood 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 stepwise-selected 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 well-defined
(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