plot.dade {ks} | R Documentation |
Density estimate plot and partition for bi- and tri-variate data for kernel, linear and quadratic discriminant analysis
## bivariate ## S3 method for class 'dade': plot(x, y, y.group, prior.prob=NULL, display="part", cont=c(25,50,75), ncont=NULL, xlim, ylim, xlabs="x", ylabs="y", drawlabels=TRUE, cex=1, pch, lty, col, lcol, ...) ## trivariate ## S3 method for class 'dade': plot(x, y, y.group, prior.prob=NULL, cont=c(25,50), colors, alphalo=0.2, alphahi=0.6, ...)
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 |
cex,pch,lty,xlim, ylim, xlabs, ylabs |
usual graphics parameters |
drawlabels |
draw contour labels |
col, lcol |
colour for plotting symbol and lines respectively |
colors |
vector of colours for each group |
alphalo, alphahi |
minimum and maximum transparency |
... |
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.
## bivariate example library(MASS) data(iris) ir <- iris[,c(1,2)] ir.gr <- iris[,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) 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) ## trivariate example ## Not run: ir <- iris[,1:3] ir.gr <- iris[,5] H <- Hkda(ir, ir.gr, bw="plugin", pre="scale") fhat <- kda.kde(ir, ir.gr, H) plot(fhat, cont=c(25,50)) ## colour indicates species, transparency indicates density heights qda.fhat <- pda.pde(ir, ir.gr, type="quad") plot(qda.fhat, cont=c(25,50)) ## End(Not run)