| 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] kda.fhat <- kda.kde(ir, ir.gr, hs=sqrt(c(0.01, 0.04, 0.07))) 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)