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, ...)
- an object representing a model of an appropriate class (mainly
"glm"). 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 components
lower, both formulae. See the details for how to specify the formulae and how they are used.
- used in the definition of the AIC statistic for selecting the models, currently only for
glmmodels. The default value,
- 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
step. Larger values may give more detailed information.
- a filter function whose input is a fitted model object and the associated
AICstatistic, and whose output is arbitrary. Typically
keepwill select a subset of the components of the object and return them. The default is not to keep anything.
- 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.
- the multiple of the number of degrees of freedom used for the penalty. Only
k = 2gives the genuine AIC:
k = log(n)is sometimes referred to as BIC or SBC.
- any additional arguments to
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 specifies the
upper component, and the
lower model is empty. If
scope is missing, the initial model is used as the
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
poisson families have fixed
scale by default and do not correspond to a particular maximum-likelihood problem for variable
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
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.
Calls to the function
nobs are used to check that the number of observations involved in the fitting process remains unchanged.
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).
This function differs considerably from the function in S, which uses a number of approximations and does not in general compute the correct AIC.
This is a minimal implementation. Use
stepAIC in package MASS for a wider range of object classes.
Documentation reproduced from R 3.0.2. License: GPL-2.