leaps: all-subsets regressiom

In leaps: Regression Subset Selection

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)

leaps documentation built on June 22, 2024, 9:58 a.m.