plot the cross-validation curve of the sparse DWD

Description

Plots the cross-validation curve against a function of lambda values. The function also provides the upper and lower standard deviation curves.

Usage

1
2
## S3 method for class 'cv.sdwd'
plot(x, sign.lambda, ...)

Arguments

x

A fitted cv.sdwd object.

sign.lambda

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

...

Other graphical parameters to plot.

Details

This function depicts the cross-validation curves. This function is modified based on the plot.cv function from the glmnet and the gcdnet packages.

Author(s)

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang@umn.edu

References

Wang, B. and Zou, H. (2015) “Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, forthcoming.
http://arxiv.org/abs/1501.06066

Yang, Y. and Zou, H. (2013) “An Efficient Algorithm for Computing the HHSVM and Its Generalizations", Journal of Computational and Graphical Statistics, 22(2), 396–415
http://users.stat.umn.edu/~yiyang/resources/papers/JCGS_gcdnet.pdf

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33(1), 1–22
http://www.jstatsoft.org/v33/i01/paper

See Also

cv.sdwd.

Examples

1
2
3
4
5
data(colon)
colon$x = colon$x[ ,1:100] # this example only uses the first 100 columns 
set.seed(1)
cv = cv.sdwd(colon$x, colon$y, lambda2=1, nfolds=5)
plot(cv)