gcdnet (version 1.0.5)

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

Description

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

Usage

# S3 method for cv.gcdnet
predict(object, newx, s=c("lambda.1se","lambda.min"),...)

Value

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

Arguments

object

fitted cv.gcdnet object.

newx

matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.gcdnet.

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.

Author

Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <[e-mail]>

Details

This function makes it easier to use the results of cross-validation to make a prediction.

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: https://github.com/emeryyi/fastcox.git

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.gcdnet, and coef.cv.gcdnet methods.

Examples

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