# regsubsets

##### functions for model selection

Generic function for regression subset selection with methods for formula and matrix arguments.

- Keywords
- regression

##### Usage

`regsubsets(x=, ...)`regsubsets.formula(x=, data=, weights=rep(1, length(y)), nbest=1, nvmax=8, force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive", "backward", "forward", "seqrep"), really.big=FALSE,...)

regsubsets.default(x=, y=, weights=rep(1, length(y)), nbest=1, nvmax=8,
force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive",
"backward", "forward", "seqrep"), really.big=FALSE,...)

summary.regsubsets(object,all.best=TRUE,matrix=TRUE,matrix.logical=FALSE,df=NULL,...)

##### Arguments

- x
- design matrix or model formula for full model
- data
- Optional data frame
- y
- response vector
- weights
- weight vector
- nbest
- number of subsets of each size to record
- nvmax
- maximum size of subsets to examine
- force.in
- index to columns of design matrix that should be in all models
- force.out
- index to columns of design matrix that should be in no models
- intercept
- Add an intercept?
- method
- Use exhaustive search, forward selection, backward selection or sequential replacement to search.
- really.big
- Must be T to perform exhaustive search on more than 50 variables.
- object
- regsubsets object
- all.best
- Show all the best subsets or just one of each size
- matrix
- Show a matrix of the variables in each model or just summary statistics
- matrix.logical
- With
`matrix=TRUE`

, the matrix is logical`TRUE`

/`FALSE`

or string`"*"`

/code{" "} - df
- Specify a number of degrees of freedom for the summary
statistics. The default is
`n-1`

- ...
- Other arguments for future methods

##### Value

- An object of class "regsubsets" containing no user-serviceable parts. It is designed to be processed by
`summary.regsubsets`

.

##### Note

This function improves on `leaps`

in several ways. The design matrix need not be of full rank. The ability to restrict `nvmax`

speeds up exhaustive searches considerably. There is no hard-coded limit to the number of variables.

##### See Also

##### Examples

```
data(swiss)
a<-regsubsets(as.matrix(swiss[,-1]),swiss[,1])
summary(a)
b<-regsubsets(Fertility~.,data=swiss)
summary(a)
```

*Documentation reproduced from package leaps, version 2.4, License: GPL version 2 or later*

### Community examples

**seydoun**at Nov 13, 2017 leaps v3.0

data(swiss) a<-regsubsets(as.matrix(swiss[,-1]),swiss[,1]) summary(a) b<-regsubsets(Fertility~.,data=swiss,nbest=2) summary(b) coef(a, 1:3) vcov(a, 3)