R: all-subsets regressiom

leaps {leaps} | R Documentation

## all-subsets regressiom

### Description

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, ...

### 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

`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

### 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

`regsubsets`, `regsubsets.formula`, `regsubsets.default`

### Examples

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

[Package leaps version 3.0 Index]