plot.cv.gcdnet {gcdnet} | 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.gcdnet' plot(x, sign.lambda, ...)
x |
fitted |
sign.lambda |
either plot against |
... |
other graphical parameters to plot |
A plot is produced.
Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>
Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: https://github.com/emeryyi/fastcox.git
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/
# fit an elastic net penalized logistic regression # with lambda2 = 1 for the L2 penalty. Use the # logistic loss as the cross validation # prediction loss. Use five-fold CV to choose # the optimal lambda for the L1 penalty. data(FHT) set.seed(2011) cv=cv.gcdnet(FHT$x, FHT$y, method ="logit", lambda2 = 1, pred.loss="loss", nfolds=5) plot(cv)