| step {stats} | R Documentation |
Select a formula-based model by AIC.
step(object, scope, scale = 0,
direction = c("both", "backward", "forward"),
trace = 1, keep = NULL, steps = 1000, k = 2, ...)
object |
an object representing a model of an appropriate class (mainly
"lm" and "glm").
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, 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 step.
Larger values may give more detailed information.
|
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. |
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.
|
step uses add1 and drop1
repeatedly; it will work for any method for which they work, and that
is determined by having a valid method for extractAIC.
When the additive constant can be chosen so that AIC is equal to
Mallows' Cp, this is done and the tables are labelled
appropriately.
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. So using
. in a scope formula means ‘what is
already there’, with .^2 indicating all interactions of
existing terms.
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
function 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.)
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 R's default of
na.action = na.omit is used. We suggest you remove the
missing values first.
This function differs considerably from the function in S, which uses a number of approximations and does not compute the correct AIC.
This is a minimal implementation. Use stepAIC
in package MASS for a wider range of object classes.
B. D. Ripley: step is a slightly simplified version of
stepAIC in package MASS (Venables &
Ripley, 2002 and earlier editions).
The idea of a step function follows that described in Hastie &
Pregibon (1992); but the implementation in R is more general.
Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer (4th ed).
example(lm) step(lm.D9) summary(lm1 <- lm(Fertility ~ ., data = swiss)) slm1 <- step(lm1) summary(slm1) slm1$anova