`combiPlot.Rd`

Plot classifications corresponding to successive combined solutions.

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

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 |

combiM | A "combining matrix" (as provided by |

... | Other arguments to be passed to the |

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`

.

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.*

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

if (FALSE) { 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:")}