predict.cv.gcdnet {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 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 <[e-mail]>

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/gcdnet

Gu, Y., and Zou, H. (2016). "High-dimensional generalizations of asymmetric least squares regression and their applications." The Annals of Statistics, 44(6), 2661–2694.

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