leaps (version 2.4)

regsubsets: functions for model selection

Description

Generic function for regression subset selection with methods for formula and matrix arguments.

Usage

regsubsets(x=, ...)
regsubsets.formula(x=, data=, weights=rep(1, length(y)), nbest=1, nvmax=8, force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive", "backward", "forward", "seqrep"), really.big=FALSE,...)
regsubsets.default(x=, y=, weights=rep(1, length(y)), nbest=1, nvmax=8,
force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive",
"backward", "forward", "seqrep"), really.big=FALSE,...)
summary.regsubsets(object,all.best=TRUE,matrix=TRUE,matrix.logical=FALSE,df=NULL,...)

Arguments

x

design matrix or model formula for full model

data

Optional data frame

y

response vector

weights

weight vector

nbest

number of subsets of each size to record

nvmax

maximum size of subsets to examine

force.in

index to columns of design matrix that should be in all models

force.out

index to columns of design matrix that should be in no models

intercept

Add an intercept?

method

Use exhaustive search, forward selection, backward selection or sequential replacement to search.

really.big

Must be T to perform exhaustive search on more than 50 variables.

object

regsubsets object

all.best

Show all the best subsets or just one of each size

matrix

Show a matrix of the variables in each model or just summary statistics

matrix.logical

With matrix=TRUE, the matrix is logical TRUE/FALSE or string "*"/code{" "}

df

Specify a number of degrees of freedom for the summary statistics. The default is n-1

...

Other arguments for future methods

Value

• An object of class "regsubsets" containing no user-serviceable parts. It is designed to be processed by summary.regsubsets.

See Also

leaps

Examples

data(swiss)
a<-regsubsets(as.matrix(swiss[,-1]),swiss[,1])
summary(a)
b<-regsubsets(Fertility~.,data=swiss)
summary(a)

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