leaps: 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)

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

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

References

Alan Miller "Subset Selection in Regression" Chapman & Hall

Examples

Run this code

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