tableplot.colldiag: Tableplot for Collinearity Diagnostics

In VisCollin: Visualizing Collinearity Diagnostics

Tableplot for Collinearity Diagnostics

Description

These methods produce a tableplot of collinearity diagnostics, showing the condition indices and variance proportions for predictors in a linear or generalized linear regression model. This encodes the condition indices using squares whose background color is red for condition indices > 10, green for values > 5 and green otherwise, reflecting danger, warning and OK respectively. The value of the condition index is encoded within this using a white square proportional to the value (up to some maximum value, cond.max),

Variance decomposition proportions are shown by filled circles whose radius is proportional to those values and are filled (by default) with shades ranging from white through pink to red. Rounded values of those diagnostics are printed in the cells.

Usage

## S3 method for class 'lm'
tableplot(values, ...)

## S3 method for class 'glm'
tableplot(values, ...)

## S3 method for class 'colldiag'
tableplot(
  values,
  prop.col = c("white", "pink", "red"),
  cond.col = c("#A8F48D", "#DDAB3E", "red"),
  cond.max = 100,
  prop.breaks = c(0, 20, 50, 100),
  cond.breaks = c(0, 5, 10, 1000),
  show.rows = nvar:1,
  title = "",
  patterns,
  ...
)

Arguments

values	A "colldiag", "lm" or "glm" object
...	other arguments, for consistency with generic
prop.col	A vector of colors used for the variance proportions. The default is c("white", "pink", "red").
cond.col	A vector of colors used for the condition indices
cond.max	Maximum value to scale the white squares for the condition indices
prop.breaks	Scale breaks for the variance proportions
cond.breaks	Scale breaks for the condition indices
show.rows	Rows of the eigenvalue decompositon of the model matrix to show in the display. The default nvar:1 puts the smallest dimensions at the top of the display.
title	title used for the resulting graphic
patterns	pattern matrix used for table plot.

Value

None. Used for its graphic side-effect

Author(s)

Michael Friendly

References

Friendly, M., & Kwan, E. (2009). "Where’s Waldo: Visualizing Collinearity Diagnostics." The American Statistician, 63, 56–65. Online: https://www.datavis.ca/papers/viscollin-tast.pdf.

Examples

# None yet

VisCollin documentation built on Sept. 8, 2023, 5:58 p.m.