cmb.plot {cmbClust}

Graphic display for the results of conditional mixture modeling and model-based clustering

Description

cmb.plot demonstrates the clustering results of functions cmb.em and cmb.search. A graph with a combination of pairwise scatter plot for data points, pairwise contour plot of estimated mixture density, and pairwise regression curves is produced.

Usage

cmb.plot(obj, allcolors = NULL, allpch = NULL, lwd = 1, cex.text = 1, cex.point = 0.6, 
mar = c(0.6,0.6,0.6,0.6), oma = c(3.5,3.5,2.5,14), nlevels = 30)

Arguments

obj

output object of the function cmb.em() or cmb.search()

allcolors

colous of clusters (length K)

allpch

styles of data points in clusters (length K)

lwd

line width, a positive number, defaulting to 1

cex.text

magnification of labels and titles, defaulting to 1

cex.point

magnification of plotting symbols, defaulting to 0.6

mar

margin sizes of plots in lines of text (length 4)

oma

outer margin sizes of a pairwise plot in lines of text (length 4)

nlevels

number of contour levels, defaulting to 30

Value

This function generates a graphic.

Examples

set.seed(4)
K <- 3
l <- 2
x <- as.matrix(iris[,-5])

# Run EM algorithm for fitting a conditioning mixture model 

obj <- cmb.em(x = x, order = c(1,2,3,4), l, K, method = "stepwise",
silent = TRUE, Parallel = FALSE)
cmb.plot(obj)