# plot.cv.hqreg: Plot the cross-validation curve for a "cv.hqreg" object In hqreg: Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression

## Description

Plot the cross-validation curve for a "cv.hqreg" object against the `lambda` values used, along with standard error bars.

## Usage

 ```1 2``` ```## S3 method for class 'cv.hqreg' plot(x, log.l = TRUE, nvars = TRUE, ...) ```

## Arguments

 `x` A `"cv.hqreg"` object. `log.l` Should `log(lambda)` be used instead of `lambda` for X-axis? Default is TRUE. `nvars` If `TRUE` (the default), places an axis on top of the plot denoting the number of variables with nonzero coefficients at each `lambda`. `...` Other graphical parameters to `plot`

## Details

Produces a plot of mean cv errors at each `lambda` along with upper and lower standard error bars.

## Author(s)

Congrui Yi <[email protected]>

## References

Yi, C. and Huang, J. (2016) Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression, https://arxiv.org/abs/1509.02957
Journal of Computational and Graphical Statistics, accepted in Nov 2016
http://www.tandfonline.com/doi/full/10.1080/10618600.2016.1256816

`hqreg`, `cv.hqreg`
 ```1 2 3 4 5 6``` ```X = matrix(rnorm(1000*100), 1000, 100) beta = rnorm(10) eps = 4*rnorm(1000) y = drop(X[,1:10] %*% beta + eps) cv = cv.hqreg(X, y, seed = 123) plot(cv) ```