StepAIC() function is define under the MASS package which performs stepwise model selection under exact AIC.
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. Only k = 2 gives the genuine AIC: k = log(n) is sometimes referred to as BIC or SBC.
Description
Performs stepwise model selection by AIC.
Usage
stepAIC(object, scope, scale = 0, 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. 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 and aov models (see extractAIC for details).
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
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 glm (and other fits), but it can also slow them down. 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.)
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).