# plot.cv.sdwd: plot the cross-validation curve of the sparse DWD In sdwd: Sparse Distance Weighted Discrimination

## 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 [email protected]

## 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

`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) ```

sdwd documentation built on May 30, 2017, 12:40 a.m.