plot.kde.part: Partition plot for kernel density clustering

In ks: Kernel Smoothing

Partition plot for kernel density clustering

Description

Plot of partition for kernel density clustering for 2-dimensional data.

Usage

mvnorm.mixt.part(mus, Sigmas, props=1, xmin, xmax, gridsize, max.iter=100,
   verbose=FALSE)
kms.part(x, H, xmin, xmax, gridsize, verbose=FALSE, ...)

## S3 method for class 'kde.part'
plot(x, display="filled.contour", col, col.fun, alpha=1, add=FALSE, ...)

Arguments

mus	(stacked) matrix of mean vectors
Sigmas	(stacked) matrix of variance matrices
props	vector of mixing proportions
xmin, xmax	vector of minimum/maximum values for grid
gridsize	vector of number of grid points
max.iter	maximum number of iterations
verbose	flag to print out progress information. Default is FALSE.
x	matrix of data values or an object of class kde.part
H	bandwidth matrix. If missing, Hpi(x,deriv,order=1) is called by default.
display	type of display, "filled.contour" for filled contour plot
col, col.fun	vector of plotting colours or colour function
alpha	colour transparency. Default is 1.
add	flag to add to current plot. Default is FALSE.
...	other parameters

Details

For 2-d data, kms.part and mvnorm.mixt.part produce a kde.part object whose values are the class labels, rather than probability density values.

Value

A kernel partition is an object of class kde.part which is a list with fields:

x	data points - same as input
eval.points	vector or list of points at which the estimate is evaluated
estimate	density estimate at eval.points
H	bandwidth matrix
gridtype	"linear"
gridded	flag for estimation on a grid
binned	flag for binned estimation
names	variable names
w	vector of weights
cont	vector of probability contour levels
end.points	matrix of final iterates starting from x
label	vector of cluster labels
mode	matrix of cluster modes
nclust	number of clusters
nclust.table	frequency table of cluster labels
tol.iter, tol.clust, min.clust.size	tuning parameter values - same as input

Plot is sent to graphics window.

See Also

plot.kde, kms

Examples

## normal mixture partition
mus <- rbind(c(-1,0), c(1, 2/sqrt(3)), c(1,-2/sqrt(3)))
Sigmas <- 1/25*rbind(invvech(c(9, 63/10, 49/4)), invvech(c(9,0,49/4)), invvech(c(9,0,49/4)))
props <- c(3,3,1)/7
gridsize <- c(11,11) ## small gridsize illustrative purposes only 
nmixt.part <- mvnorm.mixt.part(mus=mus, Sigmas=Sigmas, props=props, gridsize=gridsize)
plot(nmixt.part, asp=1, xlim=c(-3,3), ylim=c(-3,3), alpha=0.5)

## kernel mean shift partition
set.seed(81928192)
x <- rmvnorm.mixt(n=10000, mus=mus, Sigmas=Sigmas, props=props)
msize <- round(prod(gridsize)*0.1)
kms.nmixt.part <- kms.part(x=x, min.clust.size=msize, gridsize=gridsize)
plot(kms.nmixt.part, asp=1, xlim=c(-3,3), ylim=c(-3,3), alpha=0.5)

ks documentation built on Sept. 30, 2024, 9:15 a.m.