R: Plot Combined Clusterings Results

plot.clustCombi {mclust} | R Documentation

## 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 class 'clustCombi'
    plot(x, what = c("classification", "entropy", "tree"), ...)

### 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.

### 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.

### 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

`combiPlot`, `entPlot`, `combiTree`, `clustCombi`.

### Examples

    ## Not run:
    data(Baudry_etal_2010_JCGS_examples)

    ## 1D Example
    output <- clustCombi(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(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(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)

    ## End(Not run)

[Package mclust version 5.3 Index]