cv.gcdclust: Do a cross validation

In KarimOualkacha/HHSVM-ClusterNet: hhsvm classification using the cluster elastic net penality

Description

This function is used to choce the best lambda.min that gives the minimum error of classification

Usage

cv.gcdclust(x, y, KK, lambda = NULL, pred.loss = c("misclass", "loss"), nfolds = 5, foldid, delta = 2, ...)

Arguments

object

fitted cv.gcdclust object.

s

value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object, it is the largest value of lambda such that error is within 1 standard error of the minimum. Alternatively s="lambda.min" can be used, it is the optimal value of lambda that gives minimum cross validation error cvm. If s is numeric, it is taken as the value(s) of lambda to be used.

Details

This function makes cross-validation to get lambda.min.

Value

Result of cross validation

References

HHSVM-CEN

Examples

data(test)
x=test$x
y=test$y
cv=cv.gcdclust(x,y,KK=3,lambda2=1)

KarimOualkacha/HHSVM-ClusterNet documentation built on May 7, 2019, 12:28 p.m.