Model selection by exhaustive search, forward or backward stepwise, or sequential replacement

`regsubsets(x=, ...)`# S3 method for formula
regsubsets(x=, data=, weights=NULL, nbest=1, nvmax=8,
force.in=NULL, force.out=NULL, intercept=TRUE,
method=c("exhaustive", "backward", "forward", "seqrep"),
really.big=FALSE,
nested=(nbest==1),...)

# S3 method for default
regsubsets(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,nested=(nbest==1),...)

# S3 method for biglm
regsubsets(x,nbest=1,nvmax=8,force.in=NULL,
method=c("exhaustive","backward", "forward", "seqrep"),
really.big=FALSE,nested=(nbest==1),...)

# S3 method for regsubsets
summary(object,all.best=TRUE,matrix=TRUE,matrix.logical=FALSE,df=NULL,...)

# S3 method for regsubsets
coef(object,id,vcov=FALSE,...)
# S3 method for regsubsets
vcov(object,id,...)

x

design matrix or model formula for full model, or `biglm`

object

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 TRUE to perform exhaustive search on more than 50 variables.

nested

See the Note below: if `nested=FALSE`

, models with
columns 1, 1 and 2, 1-3, and so on, will also be considered

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 `"*"`

/`" "`

df

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

id

Which model or models (ordered as in the summary output) to return coefficients and variance matrix for

vcov

If `TRUE`

, return the variance-covariance matrix as an attribute

...

Other arguments for future methods

`regsubsets`

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

.

`summary.regsubsets`

returns an object with elements

A logical matrix indicating which elements are in each model

The r-squared for each model

Residual sum of squares for each model

Adjusted r-squared

Mallows' Cp

Schwartz's information criterion, BIC

A version of the `which`

component that is formatted
for printing

A copy of the `regsubsets`

object

The coef method returns a coefficient vector or list of vectors, the vcov method returns a matrix or list of matrices.

Since this function returns separate best models of all sizes up to
`nvmax`

and since different model selection criteria such as AIC,
BIC, CIC, DIC, ... differ only in how models of different sizes are compared, the
results do not depend on the choice of cost-complexity tradeoff.

When `x`

is a `biglm`

object it is assumed to be the full
model, so `force.out`

is not relevant. If there is an intercept it
is forced in by default; specify a `force.in`

as a logical vector
with `FALSE`

as the first element to allow the intercept to be
dropped.

The model search does not actually fit each model, so the returned
object does not contain coefficients or standard errors. Coefficients
and the variance-covariance matrix for one or model models can be
obtained with the `coef`

and `vcov`

methods.

# NOT RUN { data(swiss) a<-regsubsets(as.matrix(swiss[,-1]),swiss[,1]) summary(a) b<-regsubsets(Fertility~.,data=swiss,nbest=2) summary(b) coef(a, 1:3) vcov(a, 3) # }

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