mclust (version 5.4.10)

plot.clustCombi: Plot Combined Clusterings Results

Description

Plot combined clusterings results: classifications corresponding to Mclust/BIC and to the hierarchically combined classes, "entropy plots" to help to select a number of classes, and the tree structure obtained from combining mixture components.

Usage

# S3 method for clustCombi
plot(x, what = c("classification", "entropy", "tree"), ...)

Value

Classifications are plotted with combiPlot, which relies on the Mclust plot functions. Entropy plots are plotted with entPlot and may help to select a number of classes: please see the article cited in the references. Tree plots are produced by combiTree and graph the tree structure implied by the clusters combining process.

Arguments

x

Object returned by clustCombi function.

what

Type of plot.

...

Other arguments to be passed to other functions: combiPlot, entPlot, combiTree. Please see the corresponding documentations.

Author

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

combiPlot, entPlot, combiTree, clustCombi.

Examples

# \donttest{
data(Baudry_etal_2010_JCGS_examples)

## 1D Example 
output <- clustCombi(data = Test1D, G=1:15)

# plots the hierarchy of combined solutions, then some "entropy plots" which 
# may help one to select the number of classes (please see the article cited 
# in the references)
plot(output) 

## 2D Example 
output <- clustCombi(data = ex4.1) 

# plots the hierarchy of combined solutions, then some "entropy plots" which 
# may help one to select the number of classes (please see the article cited 
# in the references)
plot(output) 

## 3D Example 
output <- clustCombi(data = ex4.4.2)

# plots the hierarchy of combined solutions, then some "entropy plots" which 
# may help one to select the number of classes (please see the article cited 
# in the references)
plot(output)
# }