plot.glmpath {glmpath}R Documentation

Plots the regularization path computed from glmpath

Description

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.

Usage

  plot.glmpath(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, ...)

Arguments

x a glmpath 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.
plot.all.steps If TRUE, all the steps taken along the path are marked on the plot. If FALSE, which is the default, only the steps at which the active set changed are shown on the plot.
xlimit When the user wants to visualize a (beginning) sub-part of the plot, xlimit sets an upper limit to the L1 norm or the number of steps, or a lower limit to λ.
predictor If TRUE and type=coefficients, the predictor step estimates are connected with dotted lines. If FALSE, only the corrector step estimates are connected with solid lines.
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

Author(s)

Mee Young Park and Trevor Hastie

References

Mee Young Park and Trevor Hastie (2007) L1 regularization path algorithm for generalized linear models. J. R. Statist. Soc. B, 69, 659-677.

See Also

cv.glmpath, glmpath, predict.glmpath

Examples

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)

[Package glmpath version 0.94 Index]