gglasso (version 1.5.1)

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

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.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

Author

Yi Yang and Hui Zou
Maintainer: Yi Yang <[e-mail]>

Details

A plot is produced.

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)