# plot.glmnet

##### plot coefficients from a "glmnet" object

Produces a coefficient profile plot of the coefficient paths for a fitted
`"glmnet"`

object.

- Keywords
- models, regression

##### Usage

```
# S3 method for glmnet
plot(x, xvar = c("norm", "lambda", "dev"),
label = FALSE, ...)
```# S3 method for mrelnet
plot(x, xvar = c("norm", "lambda", "dev"),
label = FALSE, type.coef = c("coef", "2norm"), ...)

# S3 method for multnet
plot(x, xvar = c("norm", "lambda", "dev"),
label = FALSE, type.coef = c("coef", "2norm"), ...)

# S3 method for relaxed
plot(x, xvar = c("lambda", "dev"), label = FALSE,
gamma = 1, ...)

##### Arguments

- x
fitted

`"glmnet"`

model- xvar
What is on the X-axis.

`"norm"`

plots against the L1-norm of the coefficients,`"lambda"`

against the log-lambda sequence, and`"dev"`

against the percent deviance explained.- label
If

`TRUE`

, label the curves with variable sequence numbers.- …
Other graphical parameters to plot

- type.coef
If

`type.coef="2norm"`

then a single curve per variable, else if`type.coef="coef"`

, a coefficient plot per response- gamma
Value of the mixing parameter for a "relaxed" fit

##### Details

A coefficient profile plot is produced. If `x`

is a multinomial model,
a coefficient plot is produced for each class.

##### References

Friedman, J., Hastie, T. and Tibshirani, R. (2008)
*Regularization Paths for Generalized Linear Models via Coordinate
Descent*

##### See Also

`glmnet`

, and `print`

, `predict`

and `coef`

methods.

##### Examples

```
# NOT RUN {
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
g2=sample(1:2,100,replace=TRUE)
g4=sample(1:4,100,replace=TRUE)
fit1=glmnet(x,y)
plot(fit1)
plot(fit1,xvar="lambda",label=TRUE)
fit3=glmnet(x,g4,family="multinomial")
plot(fit3,pch=19)
# }
```

*Documentation reproduced from package glmnet, version 3.0-2, License: GPL-2*