plot.cv.gcdnet: Plot the cross-validation curve produced by cv.gcdnet

Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the lambda values used. This function is modified based on the plot.cv function from the glmnet package.

Usage

# S3 method for cv.gcdnet
plot(x, sign.lambda = 1, ...)

Arguments

x: fitted cv.gcdnet object
sign.lambda: either plot against log(lambda) (default) or its negative if sign.lambda=-1.
...: other graphical parameters to plot

Author

Yi Yang, Yuwen Gu and Hui Zou

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

Details

A plot is produced.

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


# fit an elastic net penalized logistic regression with lambda2 = 1 for the
# L2 penalty. Use the logistic loss as the cross validation prediction loss.
# Use five-fold CV to choose the optimal lambda for the L1 penalty.
data(FHT)
set.seed(2011)
cv=cv.gcdnet(FHT$x, FHT$y, method ="logit", lambda2 = 1,
             pred.loss="loss", nfolds=5)
plot(cv)

Run the code above in your browser using DataLab

Get 50% off unlimited learning