pairs.ridge {genridge}

Scatterplot Matrix of Bivariate Ridge Trace Plots

Description

Displays all possible pairs of bivariate ridge trace plots for a given set of predictors.

Usage

## S3 method for class 'ridge'
pairs(
  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

x

A ridge object, as fit by ridge

variables

Predictors in the model to be displayed in the plot: an integer or character vector, giving the indices or names of the variables.

radius

Radius of the ellipse-generating circle for the covariance ellipsoids.

lwd, lty

Line width and line type for the covariance ellipsoids. Recycled as necessary.

col

A numeric or character vector giving the colors used to plot the covariance ellipsoids. Recycled as necessary.

center.pch

Plotting character used to show the bivariate ridge estimates. Recycled as necessary.

center.cex

Size of the plotting character for the bivariate ridge estimates

digits

Number of digits to be displayed as the (min, max) values in the diagonal panels

diag.cex

Character size for predictor labels in diagonal panels

diag.panel

Function to draw diagonal panels. Not yet implemented: just uses internal panel.label to write the variable name and ranges.

fill

Logical vector: Should the covariance ellipsoids be filled? Recycled as necessary.

fill.alpha

Numeric vector: alpha transparency value(s) for filled ellipsoids. Recycled as necessary.

...

Other arguments passed down

Value

None. Used for its side effect of plotting.

Author(s)

Michael Friendly

References

Friendly, M. (2013). The Generalized Ridge Trace Plot: Visualizing Bias and Precision. Journal of Computational and Graphical Statistics, 22(1), 50-68, doi:10.1080/10618600.2012.681237, https://www.datavis.ca/papers/genridge-jcgs.pdf

See Also

ridge for details on ridge regression as implemented here

plot.ridge, traceplot for other plotting methods

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)