plot.ConfusionTable: Extended Confusion Table Plot

In ORION: Ordinal Relations

Description

Plots an extended confusion table.

Usage

## S3 method for class 'ConfusionTable'
plot(
  x = NULL,
  classNames = NULL,
  main = "extended confusion table",
  xlab = "original class labels",
  ylab = "predicted class labels",
  cascLab = "inner classes",
  otherLab = "outer classes",
  digits = 3,
  ignore = 0,
  casc.colors = c("#f5f5f5", "#8c510a"),
  other.colors = c("#f5f5f5", "#01665e"),
  colSep = "#b2182b",
  las = 1,
  color.key = TRUE,
  cex = 1,
  cex.lab = 1,
  ...
)

Arguments

x	A ConfusionTable object as it is returned by confusionTable-function.
classNames	Vector of the original class names. If not given the class number is used instead.
main	See plot.
xlab	A title for the x axis (see plot).
ylab	A title for the y axis (see plot).
cascLab	Character string used as header for the cascade part of the extended confusion table.
otherLab	Character string used as header for the other class part of the extended confusion table.
digits	Integer indicating the number of decimal places to be used (see round).
ignore	A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element.
casc.colors	A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the first class. The color palette is calcuated by an interpolation between the 2 given colors.
other.colors	A 2-element vector of the color for the minimal and maximal class-wise sensitivity of the second class. The color palette is calcuated by an interpolation between the 2 given colors.
colSep	Color, which is used for the vertical line separating the cascade classes and the other classes.
las	See par.
color.key	Specifies whether a color key is drawn (TRUE) or not (FALSE).
cex	See par.
cex.lab	See par.
...	Further arguments passed from other methods.

Value

No return value, called to generate the confusion table plot.

See Also

confusionTable, plot.Subcascades, plot.Conf

Examples

library(TunePareto)
data(esl)
data <- esl$data
labels <- esl$labels
foldList <- generateCVRuns(labels  = labels,
                          ntimes      = 2,
                          nfold       = 2,
                          leaveOneOut = FALSE,
                          stratified  = TRUE)
predMap <- predictionMap(data, labels, foldList = foldList, 
                        classifier = tunePareto.svm(), kernel='linear')
# generate Subcascades object
conf.table <- confusionTable(predMap,cascade='0>1>3>4',other.classes = 'all')

plot(conf.table)

ORION documentation built on Feb. 7, 2022, 5:15 p.m.