# plot_glmnet: Plot a glmnet model

## Description

Plot the coefficient paths of a `glmnet`

model.

An enhanced version of `plot.glmnet`

.

## Usage

plot_glmnet(x = stop("no 'x' argument"),
xvar = c("rlambda", "lambda", "norm", "dev"),
label = 10, nresponse = NA, grid.col = NA, s = NA, ...)

## Arguments

xvar

What gets plotted along the x axis. One of:
`"rlambda"`

(default) decreasing log lambda (lambda is the glmnet penalty)
`"lambda"`

log lambda
`"norm"`

L1-norm of the coefficients
`"dev"`

percent deviance explained
The default `xvar`

differs from `plot.glmnet`

to allow
`s`

to be plotted when this function is invoked by
`plotres`

.

label

Default `10`

.
Number of variable names displayed on the right of the plot.
One of:
`FALSE`

display no variables
`TRUE`

display all variables
`integer`

(default) number of variables to display (default is 10)

nresponse

Which response to plot for multiple response models.

grid.col

Default `NA`

.
Color of the optional grid, for example `grid.col="lightgray"`

.

s

For use by `plotres`

.
The x position of the gray vertical line indicating the lambda
`s`

passed by `plotres`

to `predict.glmnet`

to
calculate the residuals.
Plotres defaults to `s=0`

.

…

Dot arguments are passed internally to
`matplot`

.

Use `col`

to change the color of curves; for example `col=1:4`

.
The six default colors are intended to be distinguishable yet
harmonious (to my eye at least), with adjacent colors as different as
easily possible.

## Examples

# NOT RUN {
if (require(glmnet)) {
x <- matrix(rnorm(100 * 10), 100, 10) # n=100 p=10
y <- x[,1] + x[,2] + 2 * rnorm(100) # y depends only on x[,1] and x[,2]
mod <- glmnet(x, y)
plot_glmnet(mod)
# plotres(mod) # plot the residuals
}
# }