plot.cv.gglasso: plot the cross-validation curve produced by cv.gglasso

In emeryyi/gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

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.gglasso'
plot(x, sign.lambda = 1, ...)

Arguments

x	fitted cv.gglasso 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 and Hui Zou
Maintainer: Yi Yang <[e-mail]>

References

Yang, Y. and Zou, H. (2015), “A Fast Unified Algorithm for Computing Group-Lasso Penalized Learning Problems,” Statistics and Computing. 25(6), 1129-1141.
BugReport: https://github.com/emeryyi/gglasso

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.gglasso.

Examples

# load gglasso library
library(gglasso)

# load data set
data(colon)

# define group index
group <- rep(1:20,each=5)

# 5-fold cross validation using group lasso 
# penalized logisitic regression
cv <- cv.gglasso(x=colon$x, y=colon$y, group=group, loss="logit",
pred.loss="misclass", lambda.factor=0.05, nfolds=5)

# make a CV plot
plot(cv)

emeryyi/gglasso documentation built on Feb. 23, 2020, 5:42 a.m.