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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/predict.cv.gcdnet.R

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

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

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.

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 gcdnet objects.

Author(s)

Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <[email protected]>

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

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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")

gcdnet documentation built on Nov. 20, 2017, 5:07 p.m.