combiPlot {mclust}

Plot Classifications Corresponding to Successive Combined Solutions

Description

Plot classifications corresponding to successive combined solutions.

Usage

combiPlot(data, z, combiM, ...)

Arguments

data

The data.

z

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.

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

clustCombi, combMat, clustCombi

Examples

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:")