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gcdnet (version 1.0.5)

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

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.gcdnet
plot(x, sign.lambda, ...)

Arguments

x

fitted cv.gcdnet object

sign.lambda

either plot against log(lambda) (default) or its negative if sign.lambda=-1.

...

other graphical parameters to plot

Author

Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>

Details

A plot is produced.

References

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/

See Also

cv.gcdnet.

Examples

Run this code
# 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)

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