Plot for kernel discriminant analysis

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

Usage

# 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

Value

Plots for 1-d and 2-d are sent to graphics window. Plot for 3-d is sent to graphics/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,
       ylab="Weighted density function", drawpoints=FALSE, col, col.fun,
       col.part, col.pt, lty, jitter=TRUE, rugsize, add=FALSE, alpha=1, ...)

  ## bivariate
  plot(x, y, y.group, prior.prob=NULL, display.part="filled.contour",
       cont=c(25,50,75), abs.cont, approx.cont=TRUE, xlim, ylim, xlab, ylab,
       drawpoints=FALSE, drawlabels=TRUE, cex=1, pch, lty, part=TRUE, col,
       col.fun, col.part, col.pt, alpha=1, lwd=1, lwd.part=0, add=FALSE, ...)

  ## trivariate
  plot(x, y, y.group, prior.prob=NULL, display="plot3D", cont=c(25,50,75),
       abs.cont, approx.cont=TRUE, colors, col, col.fun, col.pt, alpha=0.5,
       alphavec, xlab, ylab, zlab, drawpoints=FALSE, size=3, cex=1, pch,
       theta=-30, phi=40, d=4, ticktype="detailed", bty="f", add=FALSE, ...)

Examples

## univariate example
data(iris)
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, alpha=0.2, drawlabels=FALSE)


## trivariate example
ir <- iris[,1:3]
ir.gr <- iris[,5]
kda.fhat <- kda(x=ir, x.group=ir.gr)
plot(kda.fhat) ## colour=species, transparency=density heights