all-subsets regressiom
Usage
leaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2", "r2"), nbest=10, names=NULL, df=NROW(x), strictly.compatible=T)
Arguments
x
|
A matrix of predictors
|
y
|
A response vector
|
wt
|
Optional weight vector
|
int
|
Add an intercept to the model
|
method
|
Calculate Cp, adjusted R-squared or R-squared
|
nbest
|
Number of subsets of each size to report
|
names
|
vector of names for columns of x
|
df
|
Total degrees of freedom to use instead of nrow(x) in calculating Cp and adjusted R-squared
|
strictly.compatible
|
Implement misfeatures of leaps() in S
|
Description
leaps() performs an exhaustive search for the best subsets of the variables in x for predicting y in linear regression, using an efficient branch-and-bound algorithm. subsets
does the same thing better.Value
A list with components
which
|
logical matrix. Each row can be used to select the columns of x in the respective model
|
size
|
Number of variables, including intercept if any, in the model
|
cp
|
or adjr2 or r2 is the value of the chosen model selectionstatistic for each model
|
label
|
vector of names for the columns of x
|
Note
With strictly.compatible=T
the function will stop with an error if x
is not of full rank or if it has more than 31 columns. It will ignore the column names of x
even if names==NULL
and will replace them with "0" to "9", "A" to "Z".References
Alan Miller "Subset Selection in Regression" Chapman & Hall
S documentation for leaps()
See Also
subsets
, subsets.formula
, subsets.default
, summary.leaps
Examples
x<-matrix(rnorm(100),ncol=4)
y<-rnorm(25)
leaps(x,y)