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, ...)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 |
scale |
used in the definition of the AIC statistic for selecting the models,
currently only for |
direction |
the mode of stepwise search, can be one of |
trace |
if positive, information is printed during the running of
|
keep |
a filter function whose input is a fitted model object and the
associated |
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 |
k |
the multiple of the number of degrees of freedom used for the penalty.
Only |
... |
any additional arguments to |
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
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 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$anova
cpus1 <- cpus
for(v in names(cpus)[2:7])
cpus1[[v]] <- cut(cpus[[v]], unique(quantile(cpus[[v]])),
include.lowest = TRUE)
cpus0 <- cpus1[, 2:8] # excludes names, authors' predictions
cpus.samp <- sample(1:209, 100)
cpus.lm <- lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8])
cpus.lm2 <- stepAIC(cpus.lm, trace = FALSE)
cpus.lm2$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$anovaPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.