Plot for kernel discriminant analysis
Plot for kernel discriminant analysis for 1- to 3-dimensional data.
## S3 method for class 'kda' plot(x, y, y.group, ...)
x |
object of class |
y |
matrix of test data points |
y.group |
vector of group labels for test data points |
... |
other graphics parameters:
and those used in |
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", ...)Plots for 1-d and 2-d are sent to graphics window. Plot for 3-d is sent to graphics/RGL window.
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] kda.fhat <- kda(x=ir, x.group=ir.gr) plot(kda.fhat, alpha=0.05) ## colour=species, transparency=density heights
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