View source: R/plot.cv.gglasso.R
plot.cv.gglasso | R Documentation |
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.
## S3 method for class 'cv.gglasso'
plot(x, sign.lambda = 1, ...)
x |
fitted |
sign.lambda |
either plot against |
... |
other graphical parameters to plot |
A plot is produced.
Yi Yang and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>
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/
cv.gglasso
.
# 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)
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