quadplot: Plotting of 4 dimensional membership representation simplex

In klaR: Classification and Visualization

Plotting of 4 dimensional membership representation simplex

Description

For a 4 class discrimination problem the membership values of each class are visualized in a 3 dimensional barycentric coordinate system.

Usage

quadplot(e = NULL, f = NULL, g = NULL, h = NULL, angle = 75, 
    scale.y = 0.6, label = 1:4, labelcol = rainbow(4), 
    labelpch = 19, labelcex = 1.5, main = "", s3d.control = list(), 
    simplex.control = list(), legend.control = list(), ...)

Arguments

e	either a matrix with 4 columns represanting the membership values or a vector with the membership values of the first class
f	vector with the membership values of the second class
g	vector with the membership values of the third class
h	vector with the membership values of the forth class
angle	angle between x and y axis
scale.y	scale of y axis related to x- and z axis
label	label for the classes
labelcol	colors to use for the labels
labelpch	pch for the labels
labelcex	cex for the labels
main	main title of the plot
s3d.control	a list with further arguments passed to the underlying scatterplot3d function call that sets up the plot
simplex.control	a list with further arguments passed to the underlying function call that draws the barycentric coordinate system
legend.control	a list with further arguments passed to the underlying function call that adds the legend
...	further arguments passed to the underlying plot function that draws the data points

Details

The membership values are calculated with quadtrafo and plotted with scatterplot3d.

Value

A scatterplot3d object.

Author(s)

Karsten Luebke, [e-mail], and Uwe Ligges

References

Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789

See Also

triplot, scatterplot3d

Examples

library("MASS")
data(B3)
opar <- par(mfrow = c(1, 2), pty = "s")
posterior <- predict(lda(PHASEN ~ ., data = B3))$post
s3d <- quadplot(posterior, col = rainbow(4)[B3$PHASEN], 
        labelpch = 22:25, labelcex = 0.8,
        pch = (22:25)[apply(posterior, 1, which.max)], 
        main = "LDA posterior assignments")
quadlines(centerlines(4), sp = s3d, lty = "dashed")

posterior <- predict(qda(PHASEN ~ ., data = B3))$post
s3d <- quadplot(posterior, col = rainbow(4)[B3$PHASEN], 
        labelpch = 22:25, labelcex = 0.8,
        pch = (22:25)[apply(posterior, 1, which.max)],
        main = "QDA posterior assignments")
quadlines(centerlines(4), sp = s3d, lty = "dashed")
par(opar)

klaR documentation built on March 31, 2023, 3:03 p.m.