
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 cv.HDtweedie
plot(x, sign.lambda, ...)
fitted cv.HDtweedie
object
either plot against log(lambda)
(default) or
its negative if sign.lambda=-1
.
other graphical parameters to plot
A plot is produced.
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), ``Tweedie's Compound Poisson Model With Grouped Elastic Net,'' Journal of Computational and Graphical Statistics, 25, 606-625.
Friedman, J., Hastie, T., and Tibshirani, R. (2010), ``Regularization paths for generalized linear models via coordinate descent,'' Journal of Statistical Software, 33, 1.
# NOT RUN {
# load HDtweedie library
library(HDtweedie)
# load data set
data(auto)
# 5-fold cross validation using the lasso
cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5,lambda.factor=.0005)
# make a CV plot
plot(cv0)
# define group index
group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)
# 5-fold cross validation using the grouped lasso
cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5,lambda.factor=.0005)
# make a CV plot
plot(cv1)
# }
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