plot.glmnetcr: Plots the Regularization Path Computed

In glmnetcr: Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response

Description

This function takes a glmnetcr object and visualizes the regularization path. The horizontal axis can be norm, lambda or step. The vertical axis can be coefficients, aic or bic.

Usage

## S3 method for class 'glmnetcr'
plot(x, xvar = c("norm", "lambda", "step"),
type = c("coefficients", "aic", "bic"), omit.zero = TRUE, breaks = TRUE, mar = NULL,
eps = .Machine$double.eps, main = NULL, ...)

Arguments

x	a glmnetcr object
xvar	horizontal axis. xvar=norm plots against the L1 norm of the coefficients (to which L1 norm penalty was applied); xvar=lambda plots against λ; and xvar=step plots against the number of steps taken. Default is "norm".
type	type of the plot, or the vertical axis. Default is "coefficients".
omit.zero	If TRUE and type="coefficients", the predictors that were never in the active set are omitted.
breaks	If TRUE, vertical lines are drawn at the points where the active set changes and numbered with the degrees of freedom.
mar	margin relative to the current font size
eps	an effective zero
main	title of the plot
...	other options for the plot

Note

This function was adapted from the glmpath package by Park and Hastie for a glmnetcr object.

Author(s)

Kellie J. Archer [e-mail]

See Also

See also glmnetcr, predict.glmnetcr

Examples

library(glmnetcr)
data(diabetes)
x<-diabetes[,2:dim(diabetes)[2]]
y<-diabetes$y
summary(y)
fit<-glmnetcr(x,y)
plot(fit)
plot(fit,xvar="step",type="bic")

glmnetcr documentation built on July 8, 2020, 6:21 p.m.