functions for model selection
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,
nested=(nbest==1),...)
## 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,nested=(nbest==1),...)
## S3 method for class '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 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 |
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 |
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 |
df |
Specify a number of degrees of freedom for the summary
statistics. The default is |
id |
Which model or models (ordered as in the summary output) to return coefficients and variance matrix for |
vcov |
If |
... |
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 |
obj |
A copy of the |
The coef method returns a coefficient vector or list of vectors,
the vcov method returns a matrix or list of matrices.
As part of the setup process, the code initially fits models with the
first variable in x, the first two, the first three, and so on.
For forward and backward selection it is possible that the model with the k
first variables will be better than the model with k
variables from the selection algorithm. If it is, the model with the
first k variables will be returned, with a warning. This can
happen for forward and backward selection. It (obviously) can't for
exhaustive search.
With nbest=1 you can avoid these extra models with
nested=TRUE, which is the default.
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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