Plot for kernel discriminant analysis

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Description

Plot for kernel discriminant analysis for 1- to 3-dimensional data.

Usage

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## S3 method for class 'kda'
plot(x, y, y.group, ...)

Arguments

x

object of class kda (output from kda)

y

matrix of test data points

y.group

vector of group labels for test data points

...

other graphics parameters:

rugsize

height of rug-like plot for partition classes (1-d)

prior.prob

vector of prior probabilities

col.part

vector of colours for partition classes (1-d, 2-d)

and those used in plot.kde

Details

For kda objects, the function headers for the different dimensional data are

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  ## univariate
  plot(x, y, y.group, prior.prob=NULL, xlim, ylim, xlab="x",
     ylab="Weighted density function", drawpoints=FALSE, col, col.part,
     col.pt, lty, jitter=TRUE, rugsize, ...)

  ## bivariate
  plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75), abs.cont, 
     approx.cont=FALSE, xlim, ylim, xlab, ylab, drawpoints=FALSE, 
     drawlabels=TRUE, col, col.part, col.pt, ...)

  ## trivariate
  plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75), abs.cont, 
     approx.cont=FALSE, colors, alphavec, xlab, ylab, zlab, 
     drawpoints=FALSE, size=3, col.pt="blue", ...)
  

Value

Plots for 1-d and 2-d are sent to graphics window. Plot for 3-d is sent to RGL window.

See Also

kda, kde

Examples

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library(MASS)
data(iris)

## univariate example
ir <- iris[,1]
ir.gr <- iris[,5]
kda.fhat <- kda(x=ir, x.group=ir.gr, xmin=3, xmax=9)
plot(kda.fhat, xlab="Sepal length")

## bivariate example
ir <- iris[,1:2]
ir.gr <- iris[,5]
kda.fhat <- kda(x=ir, x.group=ir.gr)
plot(kda.fhat)

## trivariate example
ir <- iris[,1:3]
ir.gr <- iris[,5] 
H <- Hkda(x=ir, x.group=ir.gr, bw="plugin", pilot="dscalar")
kda.fhat <- kda(x=ir, x.group=ir.gr, Hs=H)
plot(kda.fhat, drawpoints=TRUE, col.pt=c(2,3,4))
   ## colour=species, transparency=density heights

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