ES (version 1.1)

cv.ES: Edge Selection with Cross validation

Description

Computes K-Fold cross validation based on mean squared prediction error.

Usage

cv.ES(x,object,K=10,M)

Value

cv.ES picks a model which minimizes the mean squared prediction errors using the input vector M. cv.ES also pick a model with a mean squared prediction error less than or equals to the minimum mean square prediction plus its standard error.

Arguments

x

Data Matrix. The columns represent the different variables, while the rows represent identically and independently distributed samples.

object

Lars object, generated from ES function.

K

Number of Folds in cross validation.

M

A vector of values that determine the points where cross validation are done. If not specified, the value of M will be determined using the object

References

Edge Selection for Undirected Graphs

See Also

ES, ESpredict

Examples

data(marks)
attach(marks)
object <- ES(marks)
cv.ES(marks,object)
detach(marks)