combiPlot: Plot Classifications Corresponding to Successive Combined Solutions

Description

Plot classifications corresponding to successive combined solutions.

Usage

combiPlot(data, z, combiM, …)

Arguments

data

The data.

A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class, for the initial solution (ie before any combining). Typically, the one returned by Mclust/BIC.

combiM

A "combining matrix" (as provided by clustCombi), ie a matrix whose kth row contains only zeros, but in columns corresponding to the labels of the classes in the initial solution to be merged together to get the combined solution.

…

Other arguments to be passed to the Mclust plot functions.

Value

Plot the classifications obtained by MAP from the matrix t(combiM %*% t(z)), which is the matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class, according to the combined solution obtained by merging (according to combiM) the initial solution described by z.

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.

Examples

Run this code

# NOT RUN {
data(Baudry_etal_2010_JCGS_examples)
MclustOutput <- Mclust(ex4.1) 

MclustOutput$G # Mclust/BIC selected 6 classes

par(mfrow=c(2,2))

combiM0 <- diag(6) # is the identity matrix
# no merging: plot the initial solution, given by z
combiPlot(ex4.1, MclustOutput$z, combiM0, cex = 3) 
title("No combining")

combiM1 <- combMat(6, 1, 2) # let's merge classes labeled 1 and 2
combiM1
combiPlot(ex4.1, MclustOutput$z, combiM1)
title("Combine 1 and 2")

# let's merge classes labeled 1 and 2, and then components labeled (in this 
# new 5-classes combined solution...) 1 and 2
combiM2 <- combMat(5, 1, 2) %*% combMat(6, 1, 2) 
combiM2 
combiPlot(ex4.1, MclustOutput$z, combiM2)
title("Combine 1, 2 and then 1 and 2 again")

plot(0,0,type="n", xlab = "", ylab = "", axes = FALSE)
legend("center", legend = 1:6,
       col = mclust.options("classPlotColors"), 
       pch = mclust.options("classPlotSymbols"), 
       title = "Class labels:")
# }

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