plot.glmpath {glmpath} | R Documentation |
This function takes a glmpath
object and visualizes the
regularization path. The horizontal axis can be norm,
lambda
or step.
The vertical axis can be
coefficients,
aic
or bic.
## S3 method for class 'glmpath' plot(x, xvar = c("norm", "lambda", "step"), type = c("coefficients", "aic", "bic"), plot.all.steps = FALSE, xlimit = NULL, predictor = FALSE, omit.zero = TRUE, breaks = TRUE, mar = NULL, eps = .Machine$double.eps, main = NULL, ...)
x |
a |
xvar |
horizontal axis. |
type |
type of the plot, or the vertical axis. Default is
|
plot.all.steps |
If |
xlimit |
When the user wants to visualize a (beginning) sub-part of the plot,
|
predictor |
If |
omit.zero |
If |
breaks |
If |
mar |
margin relative to the current font size |
eps |
an effective zero |
main |
title of the plot |
... |
other options for the plot |
Mee Young Park and Trevor Hastie
Mee Young Park and Trevor Hastie (2007) L1 regularization path algorithm for generalized linear models. J. R. Statist. Soc. B, 69, 659-677.
cv.glmpath, glmpath, predict.glmpath
data(heart.data) attach(heart.data) fit <- glmpath(x, y, family=binomial) par(mfrow=c(3, 2)) plot(fit) plot(fit, xvar="lambda") plot(fit, xvar="step") plot(fit, xvar="step", xlimit=8) plot(fit, type="aic") plot(fit, type="bic") detach(heart.data)