plot.cv.gglasso {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, ...)
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/
# 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)