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plot.kde.part

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, 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, "slice" for contour plot, "persp" for perspective plot, "image" for image plot, "filled.contour" for filled contour plot (1st form), "filled.contour2" (2nd form)
`col`	vector of plotting colours
`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.

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
nmixt.part <- mvnorm.mixt.part(mus=mus, Sigmas=Sigmas, props=props)
plot(nmixt.part, asp=1, xlim=c(-3,3), ylim=c(-3,3))

## kernel mean shift partition
if (interactive()) {
set.seed(81928192)
x <- rmvnorm.mixt(n=1000, mus=mus, Sigmas=Sigmas, props=props)
msize <- round(151^2*0.05)
kms.nmixt.part <- kms.part(x=x, min.clust.size=msize)
plot(kms.nmixt.part, asp=1, xlim=c(-3,3), ylim=c(-3,3))}

ks

Kernel Smoothing

v1.12.0

GPL-2 | GPL-3

Authors

Tarn Duong [aut, cre], Matt Wand [ctb], Jose Chacon [ctb], Artur Gramacki [ctb]

Initial release

2021-02-06