regsubsets {leaps} | R Documentation |
Model selection by exhaustive search, forward or backward stepwise, or sequential replacement
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,...)
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 |
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.
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 |
coef
method returns a coefficient vector or list of vectors,
the vcov
method returns a matrix or list of matrices.
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)