plot.cv.sdwd {sdwd}

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

## 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 [e-mail]

References

Wang, B. and Zou, H. (2016) “Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, 25(3), 826–838.
https://www.tandfonline.com/doi/full/10.1080/10618600.2015.1049700

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.
https://www.tandfonline.com/doi/full/10.1080/10618600.2012.680324

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.
https://www.jstatsoft.org/v33/i01/paper

See Also

cv.sdwd.

Examples

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)