clustCombiOptim: Optimal number of clusters obtained by combining mixture...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/clustCombi.R

Description

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

Usage

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

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.

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.

Author(s)

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

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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])

mclust documentation built on Nov. 22, 2017, 5:05 p.m.