| leaps {leaps} | R Documentation |
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. It is a compatibility wrapper for regsubsets does the same thing better.
leaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2", "r2"), nbest=10, names=NULL, df=NROW(x), strictly.compatible=TRUE)
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 |
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 |
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".
Alan Miller "Subset Selection in Regression" Chapman & Hall
regsubsets, regsubsets.formula, regsubsets.default
x<-matrix(rnorm(100),ncol=4) y<-rnorm(25) leaps(x,y)