Model selection by exhaustive search, forward or backward
stepwise, or sequential replacement
Usage
regsubsets(x=, ...)
## S3 method for class '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,...)
## Default S3 method:
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,...)
## S3 method for class 'biglm'
regsubsets(x,nbest=1,nvmax=8,force.in=NULL,
method=c("exhaustive","backward", "forward", "seqrep"), really.big=FALSE,...)
## S3 method for class 'regsubsets'
summary(object,all.best=TRUE,matrix=TRUE,matrix.logical=FALSE,df=NULL,...)
## S3 method for class 'regsubsets'
coef(object,id,vcov=FALSE,...)
## S3 method for class 'regsubsets'
vcov(object,id,...)
Arguments
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.
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
Details
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.
Value
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
which
A logical matrix indicating which elements are in each
model
rsq
The r-squared for each model
rss
Residual sum of squares for each model
adjr2
Adjusted r-squared
cp
Mallows' Cp
bic
Schwartz's information criterion, BIC
outmat
A version of the which component that is formatted
for printing
obj
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.