plot.coxpath {glmpath} | R Documentation |
This function takes a coxpath
object and visualizes the
regularization path. The horizontal axis can be norm,
lambda
or step.
The vertical axis can be
coefficients,
aic
or bic.
plot.coxpath(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, main = NULL, eps = .Machine$double.eps, ...)
x |
a coxpath 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, 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 |
main |
title of the plot |
eps |
an effective zero |
... |
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.coxpath, coxpath, predict.coxpath
data(lung.data) attach(lung.data) fit <- coxpath(lung.data) 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(lung.data)