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)
```

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

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

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

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

Run the code above in your browser using DataLab