clustCombiOptim {mclust} | R Documentation |
Return the optimal number of clusters by combining mixture components based on the entropy method discussed in the reference given below.
clustCombiOptim(object, reg = 2, plot = FALSE, ...)
object |
An object of class |
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 |
... |
Further arguments passed to or from other methods. |
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. |
J.-P. Baudry, A. E. Raftery, L. Scrucca
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.
combiPlot
, entPlot
, clustCombi
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])