glmnet (version 4.1-8)

plot.glmnet: plot coefficients from a "glmnet" object

Description

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

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

Author

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie hastie@stanford.edu

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

Run this code
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)

Run the code above in your browser using DataCamp Workspace