coef.cv.gcdnet: Get coefficients or make coefficient predictions from a "cv.gcdnet" object.

Description

This function gets coefficients or makes coefficient 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
coef(object, 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.
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.

Author

Yi Yang, Yuwen Gu and Hui Zou

Maintainer: Yi Yang <yi.yang6@mcgill.ca>

Details

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

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/

Examples

Run this code


data(FHT)
set.seed(2011)
cv <- cv.gcdnet(FHT$x, FHT$y, lambda2 = 1, nfolds = 5)
coef(cv, s = "lambda.min")

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