coef.cv.gcdclust: get coefficients or make coefficient predictions from a...

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

Description

This function gets coefficients or makes coefficient predictions from a cross-validated gcdclust model, using the stored "gcdclust.fit" object, and the optimal value chosen for lambda.

Usage

## S3 method for class 'cv.gcdclust'
coef(object,s=c("lambda.1se","lambda.min"),...)

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.
...	not used. Other arguments to predict.

Details

This function makes it easier to use the results of cross-validation to get coefficients or make coefficient predictions.

Value

The object returned depends the ... argument which is passed on to the predict method for gcdclust objects.

References

Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: http://code.google.com/p/gcdclust/

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/

See Also

cv.gcdclust, and predict.cv.gcdclust methods.

Examples

data(test)
x=test$x
y=test$y
cv=cv.gcdclust(x, y, 
	lambda2 = 1,nfolds=5 , KK=3)
coef(cv,s="lambda.min")

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