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).