Choose a model by AIC in a Stepwise Algorithm
Performs stepwise model selection by AIC.
stepAIC(object, scope, scale = 0, direction = c("both", "backward", "forward"), trace = 1, keep = NULL, steps = 1000, use.start = FALSE, k = 2, ...)
- an object representing a model of an appropriate class. This is used as the initial model in the stepwise search.
- defines the range of models examined in the stepwise search.
This should be either a single formula, or a list containing
lower, both formulae. See the details for how to specify the formulae and
- used in the definition of the AIC statistic for selecting the models,
currently only for
- the mode of stepwise search, can be one of
"forward", with a default of
"both". If the
scopeargument is missing the default for
- if positive, information is printed during the running of
stepAIC. Larger values may give more information on the fitting process.
- a filter function whose input is a fitted model object and the
AICstatistic, and whose output is arbitrary. Typically
keepwill select a subset of the components of the object and return them. The default
- 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.
- if true the updated fits are done starting at the linear predictor for
the currently selected model. This may speed up the iterative
glm(and other fits), but it can also slow them down. Not used in R.
- the multiple of the number of degrees of freedom used for the penalty.
k = 2gives 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
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
upper component, and the
lower model is
scope is missing, the initial model is used as the
Models specified by
scope can be templates to update
object as used by
There is a potential problem in using
glm fits with a
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
families have fixed
scale by default and do not correspond
to a particular maximum-likelihood problem for variable
Where a conventional deviance exists (e.g. for
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
survregfits, 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$anova cpus1 <- cpus for(v in names(cpus)[2:7]) cpus1[[v]] <- cut(cpus[[v]], unique(quantile(cpus[[v]])), include.lowest = TRUE) cpus0 <- cpus1[, 2:8] # excludes names, authors' predictions cpus.samp <- sample(1:209, 100) cpus.lm <- lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8]) cpus.lm2 <- stepAIC(cpus.lm, trace = FALSE) cpus.lm2$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