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, ...

```
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)
```

A list with components

- which
logical matrix. Each row can be used to select the columns of

`x`

in the respective model- size
Number of variables, including intercept if any, in the model

- cp
or

`adjr2`

or`r2`

is the value of the chosen model selection statistic for each model- label
vector of names for the columns of x

- 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

Alan Miller "Subset Selection in Regression" Chapman & Hall

`regsubsets`

, `regsubsets.formula`

,
`regsubsets.default`

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

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