klaR: quadplot – R documentation

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quadplot

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, karsten.luebke@fom.de, 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

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

Classification and Visualization

v0.6-15

GPL-2 | GPL-3

Authors

Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf, David Meyer

Initial release

2020-02-18