plot.kda | R Documentation |
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, 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, ...)
Plots for 1-d and 2-d are sent to graphics window. Plot for 3-d is sent to graphics/RGL window.
kda
, kde
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, 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
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