cv.ES: Edge Selection with Cross validation

In ES: Edge Selection

Description

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

Usage

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

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

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.

References

Edge Selection for Undirected Graphs

See Also

ES, ESpredict

Examples

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

ES documentation built on May 2, 2019, 8:29 a.m.