plot.kda: Plot for kernel discriminant analysis

Description

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

Usage

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

Arguments

object of class kda (output from kda)

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

Value

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

Details

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

  ## 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", ...)

Examples

Run this code

# NOT RUN {
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)
# }
# NOT RUN {
## trivariate example
ir <- iris[,1:3]
ir.gr <- iris[,5] 
H <- Hkda(x=ir, x.group=ir.gr, bw="plugin")
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
# }