```
data(Baudry_etal_2010_JCGS_examples)
# run Mclust using provided data
output <- clustCombi(data = ex4.1)
# \donttest{
# or run Mclust and then clustcombi on the returned object
mod <- Mclust(ex4.1)
output <- clustCombi(mod)
# }
output
summary(output)
# \donttest{
# run Mclust using provided data and any further optional argument provided
output <- clustCombi(data = ex4.1, modelName = "EEV", G = 1:15)
# }
# plot the hierarchy of combined solutions
plot(output, what = "classification")
# plot some "entropy plots" which may help one to select the number of classes
plot(output, what = "entropy")
# plot the tree structure obtained from combining mixture components
plot(output, what = "tree")
# the selected model and number of components obtained from Mclust using BIC
output$MclustOutput
# the matrix whose [i,k]th entry is the probability that i-th observation in
# the data belongs to the k-th class according to the BIC solution
head( output$combiz[[output$MclustOutput$G]] )
# the matrix whose [i,k]th entry is the probability that i-th observation in
# the data belongs to the k-th class according to the first combined solution
head( output$combiz[[output$MclustOutput$G-1]] )
# the matrix describing how to merge the 6-classes solution to get the
# 5-classes solution
output$combiM[[5]]
# for example the following code returns the label of the class (in the
# 5-classes combined solution) to which the 4th class (in the 6-classes
# solution) is assigned. Only two classes in the (K+1)-classes solution
# are assigned the same class in the K-classes solution: the two which
# are merged at this step
output$combiM[[5]]
# recover the 5-classes soft clustering from the 6-classes soft clustering
# and the 6 -> 5 combining matrix
all( output$combiz[[5]] == t( output$combiM[[5]] %*% t(output$combiz[[6]]) ) )
# the hard clustering under the 5-classes solution
head( output$classification[[5]] )
```

Run the code above in your browser using DataLab