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pairs.ridge.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pairs.ridge.R
\name{pairs.ridge}
\alias{pairs.ridge}
\title{Scatterplot Matrix of Bivariate Ridge Trace Plots}
\usage{
\method{pairs}{ridge}(
x,
variables,
radius = 1,
lwd = 1,
lty = 1,
col = c("black", "red", "darkgreen", "blue", "darkcyan", "magenta", "brown",
"darkgray"),
center.pch = 16,
center.cex = 1.25,
digits = getOption("digits") - 3,
diag.cex = 2,
diag.panel = panel.label,
fill = FALSE,
fill.alpha = 0.3,
...
)
}
\arguments{
\item{x}{A \code{ridge} object, as fit by \code{\link{ridge}}}
\item{variables}{Predictors in the model to be displayed in the plot: an
integer or character vector, giving the indices or names of the variables.}
\item{radius}{Radius of the ellipse-generating circle for the covariance
ellipsoids.}
\item{lwd, lty}{Line width and line type for the covariance ellipsoids.
Recycled as necessary.}
\item{col}{A numeric or character vector giving the colors used to plot the
covariance ellipsoids. Recycled as necessary.}
\item{center.pch}{Plotting character used to show the bivariate ridge
estimates. Recycled as necessary.}
\item{center.cex}{Size of the plotting character for the bivariate ridge
estimates}
\item{digits}{Number of digits to be displayed as the (min, max) values in
the diagonal panels}
\item{diag.cex}{Character size for predictor labels in diagonal panels}
\item{diag.panel}{Function to draw diagonal panels. Not yet implemented:
just uses internal \code{panel.label} to write the variable name and ranges.}
\item{fill}{Logical vector: Should the covariance ellipsoids be filled?
Recycled as necessary.}
\item{fill.alpha}{Numeric vector: alpha transparency value(s) for filled
ellipsoids. Recycled as necessary.}
\item{\dots}{Other arguments passed down}
}
\value{
None. Used for its side effect of plotting.
}
\description{
Displays all possible pairs of bivariate ridge trace plots for a given set
of predictors.
}
\examples{
longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])
lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)
pairs(lridge, radius=0.5, diag.cex=1.75)
data(prostate)
py <- prostate[, "lpsa"]
pX <- data.matrix(prostate[, 1:8])
pridge <- ridge(py, pX, df=8:1)
pairs(pridge)
}
\references{
Friendly, M. (2013). The Generalized Ridge Trace Plot:
Visualizing Bias \emph{and} Precision. \emph{Journal of Computational and
Graphical Statistics}, \bold{22}(1), 50-68,
doi:10.1080/10618600.2012.681237,
\url{https://www.datavis.ca/papers/genridge-jcgs.pdf}
}
\seealso{
\code{\link{ridge}} for details on ridge regression as implemented here
\code{\link{plot.ridge}}, \code{\link{traceplot}} for other plotting methods
}
\author{
Michael Friendly
}
\keyword{hplot}