plot.Mclust {mclust} | R Documentation

## Plot Model-Based Clustering Results

### Description

Plot model-based clustering results: BIC, classification, uncertainty and (for one- and two-dimensional data) density.

### Usage

    plot.Mclust(x, data, dimens = c(1, 2), scale = FALSE, ...)

### Arguments

`x` | Output from `Mclust`.
`data` | The data used to produce `x`.
`dimens` | An integer vector of length two specifying the dimensions for coordinate projections if the data is more than two-dimensional. The default is `c(1,2)` (the first two dimesions).
`scale` | A logical variable indicating whether or not the two chosen dimensions should be plotted on the same scale, and thus preserve the shape of the distribution. Default: `scale=FALSE`
`...` | Further arguments to the lower level plotting functions.

### Value

Plots selected via a menu including the following options: BIC values used for choosing the number of clusters For data in more than two dimensions, a pairs plot of the showing the classification, coordinate projections of the data, showing location of the mixture components, classification, and/or uncertainty. For one- and two- dimensional data, plots showing location of the mixture components, classification, uncertainty, and or density.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`Mclust`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisMclust <- Mclust(irisMatrix)

    ## Not run: plot(irisMclust,irisMatrix)

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