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

In gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

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 class '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

Details

A plot is produced.

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.

Examples

# 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)

gcdnet documentation built on Aug. 14, 2022, 9:05 a.m.