ks (version 1.9.5)

plot.kda: Plot for kernel discriminant analysis

Description

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: [object Object],[object Object],[object Object] 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", ...)

See Also

kda, kde

Examples

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) ## colour=species, transparency=density heights