tableplot.colldig {VisCollin}

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