Choose a model by AIC in a Stepwise Algorithm

Usage

stepAIC(object, scope, scale, 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.
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 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.)

Description

Performs stepwise model selection by exact AIC.

Value

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).

See Also

addterm, dropterm, step, step.glm

Examples

data(quine)
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

data(cpus)
cpus1 <- cpus
attach(cpus)
for(v in names(cpus)[2:6]) 
  cpus1[[v]] <- cut(cpus[[v]], quantile(cpus[[v]]), 
                    include.lowest = TRUE)
detach()
set.seed(123)
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

data(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


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