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

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

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 class 'glmnet'
plot(x, xvar = c("norm", "lambda", "dev"), label = FALSE, ...)

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

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

## S3 method for class '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.

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie [e-mail]

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

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)

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.