| plot.dade {ks} | R Documentation |
Density estimate plot and partition for bivariate data for kernel, linear and quadratic discriminant analysis
## S3 method for class 'dade':
plot(x, y, y.group, prior.prob=NULL, display="part", cont=c(25,50,75),
ncont=NULL, ...)
x |
an object of class dade i.e. output from
kda.kde or pda.pde |
display |
include plot of partition classes |
y |
matrix of test data points |
y.group |
vector of group labels for test data points |
prior.prob |
vector of prior probabilities |
cont |
vector of percentages (of maximum height) for contour level curves |
ncont |
number of contour level curves |
... |
other graphics parameters |
If prior.prob is set to a particular value then this is used.
The default is NULL which means that the sample proportions are used.
If display="part" then a partition induced by the discriminant
analysis is also plotted. If this is not desired then set
display="". Its colours are controlled by col
(the default is 2 to nu+1, where nu is the
number of groups).
Unlike plot.kde, the contour plots are automatically
added to the plot. The line types are set by lty (the default
is 1 to nu). Also,
cont and ncont control the number of level curves (only
one of these needs to be set).
The object fhat contains the training data and its group
labels. If y and y.group are missing then the training
data points are plotted. Otherwise, the test data y are plotted.
The plotting symbols are set by pch (the default is 1 to
nu), one for each group.
Plot of density estimates (and partition) for discriminant analysis is sent to graphics window.
Bowman, A.W. & Azzalini, A. (1997) Applied Smoothing Techniques for Data Analysis. Clarendon Press. Oxford.
Simonoff, J. S., (1996) Smoothing Methods in Statistics. Springer-Verlag. New York.
library(MASS)
data(iris)
iris.mat <- rbind(iris[,,1], iris[,,2], iris[,,3])
ir <- iris.mat[,c(1,2)]
ir.gr <- iris.mat[,5]
xlab <- "Sepal length (mm)"
ylab <- "Sepal width (mm)"
xlim <- c(4,8)
ylim <- c(2,4.5)
H <- Hkda(ir, ir.gr, bw="plugin", pre="scale")
fhat <- kda.kde(ir, ir.gr, H, gridsize=c(250,250))
lda.fhat <- pda.pde(ir, ir.gr, type="line")
qda.fhat <- pda.pde(ir, ir.gr, type="quad")
layout(rbind(c(1,2), c(3,4)))
plot(fhat, cont=0, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim, pch=c(1,5,10))
plot(fhat, ncont=6, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim,
col=c("transparent", "grey", "#8f8f8f"), drawlabels=FALSE)
plot(lda.fhat, ncont=6, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, disp="")
plot(qda.fhat, ncont=6, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, lty=c(2,5,3))
layout(1)