Function headers for the different dimensional data are
## univariate
plot(x, y, y.group, prior.prob=NULL, xlim, ylim,
xlab="x", ylab="Weighted density function", drawpoints=FALSE,
col, ptcol, jitter=TRUE, ...) ## bivariate
plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75),
abs.cont, approx.cont=FALSE, xlim, ylim, xlab, ylab, drawpoints=FALSE,
drawlabels=TRUE, col, partcol, ptcol, ...)
## trivariate
plot(x, y, y.group, prior.prob=NULL, cont=c(25,50,75),
abs.cont, approx.cont=FALSE, colors, alphavec, xlab, ylab, zlab,
drawpoints=FALSE, size=3, ptcol="blue", ...)
The arguments are
ll{
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
approx.cont flag to compute approximate contour levels
xlim, ylim axes limits
xlab, ylab, zlab axes labels
drawpoints flag to draw data points. Default is FALSE.
drawlabels flag to draw contour labels (2-d plot). Default is TRUE.
jitter flag to jitter rug plot (1-d plot). Default is TRUE.
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)
}
-- 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.