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.

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 selectionstatistic 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 2.7 Index]