# stepwise

From Rcmdr v2.0-3
by John Fox

##### Stepwise Model Selection

This function is a front end to the `stepAIC`

function in the

- Keywords
- models

##### Usage

```
stepwise(mod,
direction = c("backward/forward", "forward/backward", "backward", "forward"),
criterion = c("BIC", "AIC"), ...)
```

##### Arguments

- mod
- a model object of a class that can be handled by
`stepAIC`

. - direction
- if
`"backward/forward"`

(the default), selection starts with the full model and eliminates predictors one at a time, at each step considering whether the criterion will be improved by adding back in a variable removed at a previous st - criterion
- for selection. Either
`"BIC"`

(the default) or`"AIC"`

. Note that`stepAIC`

labels the criterion in the output as`"AIC"`

regardless of which criterion is employed. - ...
- arguments to be passed to
`stepAIC`

.

##### Value

- The model selected by
`stepAIC`

.

##### References

W. N. Venables and B. D. Ripley
*Modern Applied Statistics Statistics with S, Fourth Edition*
Springer, 2002.

##### See Also

##### Examples

```
# adapted from ?stepAIC in MASS
require(MASS)
example(birthwt)
birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
stepwise(birthwt.glm, trace = FALSE)
stepwise(birthwt.glm, direction="forward/backward")
```

*Documentation reproduced from package Rcmdr, version 2.0-3, License: GPL (>= 2)*

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