Summary method for a `leaps' object
Usage
summary.leaps(ll, all.best=TRUE, matrix=T, matrix.logical=F, show=T, df=NULL)
Arguments
ll
|
"leaps" object such as returned by subsets
|
all.best
|
Report all best subsets or just one of each size
|
matrix
|
Show a matrix indicating which variables are in the model
|
matrix.logical
|
Use T/F rather than "*" " " in the matrix
|
show
|
print results
|
df
|
Number to use instead of number of observations in calculating model choice statistics
|
Description
Displays the best subsets found by subsets()
and returns a logical matrix indicating which variables are in each subset and a collection of popular model choice statistics.Value
A list with components
which
|
logical matrix indicating which variables are in each subset
|
rss
|
Residual sum of squares for each subset
|
rsq
|
R-squared for each subset
|
adjr2
|
Adjusted R-squared for each subset
|
cp
|
Cp for each subset
|
bic
|
Bayesian Information Criterion for each subset
|
Warning
The R-squared, Cp and adjusted R-squared are well-known to be useless for model choice after this sort of search. They may be useful for calculating other quantities of interest.References
Alan Miller "Subset Selection in Regression" Chapman & HallSee Also
subsets
Examples
data(swiss)
a<-subsets(as.matrix(swiss[,-1]),swiss[,1])
b<-summary(a)