plot.precision: Plot Shrinkage vs. Variance for Ridge Precision

In genridge: Generalized Ridge Trace Plots for Ridge Regression

Plot Shrinkage vs. Variance for Ridge Precision

Description

This function uses the results of precision to plot a measure of shrinkage of the coefficients in ridge regression against a selected measure of their estimated sampling variance, so as to provide a direct visualization of the tradeoff between bias and precision.

Usage

## S3 method for class 'precision'
plot(
  x,
  xvar = "norm.beta",
  yvar = c("det", "trace", "max.eig"),
  labels = c("lambda", "df"),
  label.cex = 1.25,
  label.prefix,
  criteria = NULL,
  pch = 16,
  cex = 1.5,
  col,
  main = NULL,
  xlab,
  ylab,
  ...
)

Arguments

x	A data frame of class "precision" resulting from precision called on a "ridge" object. Named x only to conform with the plot generic.
xvar	The character name of the column to be used for the horizontal axis. Typically, this is the normalized sum of squares of the coefficients ("norm.beta") used as a measure of shrinkage / bias.
yvar	The character name of the column to be used for the vertical axis. One of c("det", "trace", "max.eig"). See precision for definitions of these measures.
labels	The character name of the column to be used for point labels. One of c("lambda", "df").
label.cex	Character size for point labels.
label.prefix	Character or expression prefix for the point labels.
criteria	The vector of optimal shrinkage criteria from the ridge call to be added as points in the plot.
pch	Plotting character for points
cex	Character size for points
col	Point colors
main	Plot title
xlab	Label for horizontal axis
ylab	Label for vertical axis
...	Other arguments passed to plot.

Value

Returns nothing. Used for the side effect of plotting.

Author(s)

Michael Friendly

See Also

ridge for details on ridge regression as implemented here. precision for definitions of the measures

Examples

lambda <- c(0, 0.001, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(Employed ~ GNP + Unemployed + Armed.Forces + 
                  Population + Year + GNP.deflator, 
                data=longley, lambda=lambda)

criteria <- lridge$criteria |> print()

pridge <- precision(lridge) |> print()

plot(pridge)
# also show optimal criteria
plot(pridge, criteria = criteria)

# use degrees of freedom as point labels 
plot(pridge, labels = "df")
plot(pridge, labels = "df", label.prefix="df:")
# show the trace measure
plot(pridge, yvar="trace")

genridge documentation built on April 3, 2025, 11:07 p.m.