predict.cv.gcdclust: make predictions from a "cv.gcdclust" object.

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

Description

This function makes 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'
predict(object, newx, s=c("lambda.1se","lambda.min"),...)

Arguments

object	fitted cv.gcdclust object.
newx	matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.gcdclust.
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. Alternatively s="lambda.min" can be used. 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 make a prediction.

Value

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

References

HHSVM-CEN 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 coef.cv.gcdclust methods.

Examples

data(FHT)
set.seed(2011)
cv=cv.gcdclust(FHT$x, FHT$y,
lambda2 = 1, pred.loss="misclass", 
lambda.factor=0.05,nfolds=5,KK=2)
pre = predict(cv$gcdclust.fit, newx = FHT$x, 
s = cv$lambda.1se, type = "class",KK=2)

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