# leaps

From leaps v3.1
0th

Percentile

##### all-subsets regressiom

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.

Since the algorithm returns a best model of each size, the results do not depend on a penalty model for model size: it doesn't make any difference whether you want to use AIC, BIC, CIC, DIC, ...

Keywords
regression
##### 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=TRUE)
##### 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

##### 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 selection statistic 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

regsubsets, regsubsets.formula, regsubsets.default

• leaps
##### Examples
# NOT RUN {
x<-matrix(rnorm(100),ncol=4)
y<-rnorm(25)
leaps(x,y)
# }

Documentation reproduced from package leaps, version 3.1, License: GPL (>= 2)

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