leaps (version 2.1-1)

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.

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=T)

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

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)