plot.cv.glmnetr {glmnetr} | R Documentation |
Plot cross-validation deviances, or model coefficients.
Description
By default, with coefs=FALSE, plots the average deviances as function of lam (lambda) and gam (gamma), and also indicates the gam and lam which minimize deviance based upon a cv.glmnetr() output object. Optionally, with coefs=TRUE, plots the relaxed lasso coefficients.
Usage
## S3 method for class 'cv.glmnetr'
plot(
x,
gam = NULL,
lambda.lo = NULL,
plup = 0,
title = NULL,
coefs = FALSE,
comment = TRUE,
...
)
Arguments
x |
a cv.glmnetr() output object. |
gam |
a specific level of gamma for plotting. By default gamma.min will be used. |
lambda.lo |
a lower limit of lambda when plotting. |
plup |
an indicator to plot the upper 95 percent two-sided confidence limits. |
title |
a title for the plot. |
coefs |
default of FALSE plots deviances, option of TRUE plots coefficients. |
comment |
default of TRUE to write to console information on lam and gam selected for output. FALSE will suppress this write to console. |
... |
Additional arguments passed to the plot function. |
Value
This program returns a plot to the graphics window, and may provide some numerical information to the R Console. If gam is not specified, then then the gamma.min from the deviance minimizing (lambda.min, gamma.min) pair will be used, and the corresponding lambda.min will be indicated by a vertical line, and the lambda minimizing deviance under the restricted set of models where gamma=0 will be indicated by a second vertical line.
See Also
plot.glmnetr
, plot.nested.glmnetr
, cv.glmnetr
Examples
# set seed for random numbers, optionally, to get reproducible results
set.seed(82545037)
sim.data=glmnetr.simdata(nrows=100, ncols=100, beta=NULL)
xs=sim.data$xs
y_=sim.data$y_
event=sim.data$event
# for this example we use a small number for folds_n to shorten run time
cv_glmnetr_fit = cv.glmnetr(xs, NULL, y_, NULL, family="gaussian", folds_n=3, limit=2)
plot(cv_glmnetr_fit)
plot(cv_glmnetr_fit, coefs=1)