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=TRUE, lty, lcol, col, ptcol, ...) ## bivariate ## S3 method for class 'kda.kde': plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75), ncont=NULL, xlim, ylim, xlab="x", ylab="y", drawpoints=FALSE, drawlabels=TRUE, cex=1, pch, lty, col, lcol, ptcol, ...) ## trivariate ## S3 method for class 'kda.kde': plot(x, y, y.group, prior.prob=NULL, cont=c(25,50), colors, alphavec, origin=c(0,0,0), endpts, xlab="x", ylab="y", zlab="z", drawpoints=FALSE, size=3, ptcol, ...)
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 (of maximum height) for contour level curves (2-d plot) |
ncont |
number of contour level curves (2-d plot) |
cex,pch,lty,xlim,ylim,xlab,ylab,zlab |
usual graphics parameters |
drawpoints |
if TRUE then draw data points |
drawlabels |
if TRUE then draw contour labels (2-d plot) |
col |
vector of colours for partition classes |
ptcol |
vector of colours for data points of each group |
lcol |
vector of colours for contour lines of density estimates |
colors |
vector of colours for contours of density estimates (3-d plot) |
origin |
origin vector (3-d plot) |
endpts |
vector of end points for each of the 3 axes (3-d plot) |
alphavec |
vector of transparency values - one for each contour (3-d plot) |
size |
size and of plotting symbol (3-d plot) |
... |
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.
The object x
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.
– 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:
If display="part"
then a partition induced by the discriminant
analysis is also plotted. If this is not desired, set
display=""
. Its colours are controlled by col
(the default is heat.colors
).
The plotting symbols are set by pch
and the colour by ptcol
.
Unlike plot.kde
, the contour plots are automatically
added to the plot. Default contours are cont=c(25,50,75)
.
The line types are set by lty
.
cont
and ncont
control the number of level curves (only
one of these needs to be set).
– For 3-d plots:
Default contours are cont=c(25,50)
. colors
are
set one per group - default is heat.colors
. The transparency of
each contour (within each group) is alphavec
. Default range is
0.1 to 0.5. origin
is the point where
the three axes meet. endpts
is the vector of the
maximum axis values to be plotted. Default endpts
is the
maxima for the plotting grid from x
.
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) xlab <- "Sepal length (mm)" ylab <- "Sepal width (mm)" xlim <- c(4,8) ylim <- c(2,4.5) ## univariate example ir <- iris[,1] ir.gr <- iris[,5] kda.fhat <- kda.kde(ir, ir.gr, hs=sqrt(c(0.01, 0.04, 0.07))) plot(kda.fhat, xlab=xlab, ptcol=1:3) ## 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, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim) plot(kda.fhat, ncont=6, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim, col=c("transparent", "grey", "#8f8f8f"), drawlabels=FALSE, pch=c(1,5,10)) ## trivariate example ## Not run: 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) ## colour indicates species, transparency indicates density heights ## End(Not run)