plot.kda.kde {ks} | R Documentation |
Kernel discriminant analysis plot for 1- to 3-dimensional data.
## univariate ## S3 method for class 'kda.kde': plot(x, y, y.group, prior.prob=NULL, xlim, ylim, xlab="x", ylab="Weighted density function", drawpoints=FALSE, col, ptcol, jitter=TRUE, ...) ## bivariate ## S3 method for class 'kda.kde': plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75), abs.cont, xlim, ylim, xlab, ylab, drawpoints=FALSE, drawlabels=TRUE, col, partcol, ptcol, ...) ## trivariate ## S3 method for class 'kda.kde': plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75), abs.cont, colors, alphavec, xlab, ylab, zlab, drawpoints=FALSE, size=3, ptcol="blue", ...)
x |
an object of class kda.kde (output from
kda.kde ) |
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 for contour level curves |
abs.cont |
vector of absolute density estimate heights for contour level curves |
xlim,ylim |
axes limits |
xlab,ylab,zlab |
axes labels |
drawpoints |
if TRUE then draw data points |
drawlabels |
if TRUE then draw contour labels (2-d plot) |
jitter |
if TRUE then jitter rug plot (1-d plot) |
ptcol |
vector of colours for data points of each group |
partcol |
vector of colours for partition classes (1-d, 2-d plot) |
col |
vector of colours for density estimates (1-d, 2-d plot) |
colors |
vector of colours for contours of density estimates (3-d plot) |
alphavec |
vector of transparency values - one for each contour (3-d plot) |
size |
size of plotting symbol (3-d plot) |
... |
other graphics parameters |
– For 1-d plots:
The partition induced by the discriminant analysis is plotted as rug
plot (with the ticks inside the axes). If drawpoints=TRUE
then
the data points are plotted as a rug plot with the ticks outside the
axes, their colour is controlled by ptcol
.
– For 2-d plots:
The partition classes are displayed using the colours in partcol
.
The default contours of the density estimate are 25%, 50%, 75% or
cont=c(25,50,75)
for highest density regions.
See plot.kde
for more details.
– For 3-d plots:
Default contours are cont=c(25,50,75)
for highest density
regions. See plot.kde
for more
details. The colour of each group is colors
. The transparency of
each contour (within each group) is alphavec
. Default range is
0.1 to 0.5.
– 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 y
and y.group
are missing then the training
data points are plotted. Otherwise, the test data y
are plotted.
Plot of 1-d and 2-d density estimates for discriminant analysis is sent to graphics window. Plot for 3-d is sent to RGL 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) ## univariate example ir <- iris[,1] ir.gr <- iris[,5] hs <- hkda(x=ir, x.gr=ir.gr) kda.fhat <- kda.kde(ir, ir.gr, hs=hs, xmin=3, xmax=9) plot(kda.fhat, xlab="Sepal length") ## bivariate example ir <- iris[,1:2] ir.gr <- iris[,5] H <- Hkda(ir, ir.gr, bw="plugin", pre="scale") kda.fhat <- kda.kde(ir, ir.gr, Hs=H) plot(kda.fhat, cont=0, partcol=4:6) plot(kda.fhat, drawlabels=FALSE, drawpoints=TRUE) ## trivariate example ## colour indicates species, transparency indicates density heights ir <- iris[,1:3] ir.gr <- iris[,5] H <- Hkda(ir, ir.gr, bw="plugin", pre="scale") kda.fhat <- kda.kde(ir, ir.gr, Hs=H) plot(kda.fhat, cont=50, alpha=0.5)