# leaps

##### all-subsets regressiom

leaps() performs an exhaustive search for the best subsets of the
variables in x for predicting y in linear regression, using an efficient
branch-and-bound algorithm. It is a compatibility wrapper for
`regsubsets`

does the same thing better.

Since the algorithm returns a best model of each size, the results do not depend on a penalty model for model size: it doesn't make any difference whether you want to use AIC, BIC, CIC, DIC, ...

- Keywords
- regression

##### Usage

```
leaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2", "r2"), nbest=10,
names=NULL, df=NROW(x), strictly.compatible=TRUE)
```

##### Arguments

- x
A matrix of predictors

- y
A response vector

- wt
Optional weight vector

- int
Add an intercept to the model

- method
Calculate Cp, adjusted R-squared or R-squared

- nbest
Number of subsets of each size to report

- names
vector of names for columns of

`x`

- df
Total degrees of freedom to use instead of

`nrow(x)`

in calculating Cp and adjusted R-squared- strictly.compatible
Implement misfeatures of leaps() in S

##### Value

A list with components

logical matrix. Each row can be used to select the columns of `x`

in the respective model

Number of variables, including intercept if any, in the model

or `adjr2`

or `r2`

is the value of the chosen model
selection statistic for each model

vector of names for the columns of x

##### Note

With `strictly.compatible=T`

the function will stop with an error if `x`

is not of full rank or if it has more than 31 columns. It will ignore the column names of `x`

even if `names==NULL`

and will replace them with "0" to "9", "A" to "Z".

##### References

Alan Miller "Subset Selection in Regression" Chapman \& Hall

##### See Also

##### Examples

```
# NOT RUN {
x<-matrix(rnorm(100),ncol=4)
y<-rnorm(25)
leaps(x,y)
# }
```

*Documentation reproduced from package leaps, version 3.1, License: GPL (>= 2)*