# leaps

From leaps v2.4
by Thomas Lumley

##### 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.

- 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=T)`

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

in the respective modelsize Number of variables, including intercept if any, in the model cp or `adjr2`

or`r2`

is the value of the chosen model selectionstatistic for each modellabel 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

```
x<-matrix(rnorm(100),ncol=4)
y<-rnorm(25)
leaps(x,y)
```

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

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