mclust (version 6.1)

clustCombiOptim: Optimal number of clusters obtained by combining mixture components

Description

Return the optimal number of clusters by combining mixture components based on the entropy method discussed in the reference given below.

Usage

clustCombiOptim(object, reg = 2, plot = FALSE, ...)

Value

The function returns a list with the following components:

numClusters.combi

The estimated number of clusters.

z.combi

A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth cluster.

cluster.combi

The clustering labels.

Arguments

object

An object of class 'clustCombi' resulting from a call to clustCombi.

reg

The number of parts of the piecewise linear regression for the entropy plots. Choose 2 for a two-segment piecewise linear regression model (i.e. 1 change-point), and 3 for a three-segment piecewise linear regression model (i.e. 3 change-points).

plot

Logical, if TRUE an entropy plot is also produced.

...

Further arguments passed to or from other methods.

Author

J.-P. Baudry, A. E. Raftery, L. Scrucca

References

J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.

See Also

combiPlot, entPlot, clustCombi

Examples

data(Baudry_etal_2010_JCGS_examples)
output <- clustCombi(data = ex4.1) 
combiOptim <- clustCombiOptim(output)
str(combiOptim)

# plot optimal clustering with alpha color transparency proportional to uncertainty
zmax <- apply(combiOptim$z.combi, 1, max)
col <- mclust.options("classPlotColors")[combiOptim$cluster.combi]
vadjustcolor <- Vectorize(adjustcolor)
alphacol = (zmax - 1/combiOptim$numClusters.combi)/(1-1/combiOptim$numClusters.combi)
col <- vadjustcolor(col, alpha.f = alphacol)
plot(ex4.1, col = col, pch = mclust.options("classPlotSymbols")[combiOptim$cluster.combi])