R: Plot Model-Based Clustering Results

plot.Mclust {mclust} | R Documentation

## Plot Model-Based Clustering Results

### Description

Plot model-based clustering results: BIC, classification, uncertainty and (for univariate and bivariate data) density.

### Usage

    ## S3 method for class 'Mclust'
    plot(x, what = c("BIC", "classification", "uncertainty", "density"),
         dimens = NULL, xlab = NULL, ylab = NULL, ylim = NULL,
         addEllipses = TRUE, main = TRUE, ...)

### Arguments

`x` | Output from `Mclust`.
`what` | The type of graph requested: `"BIC"` `"classification"` `"uncertainty"` `"density"` By default, all the above graphs are produced. See the description below.
`dimens` | A vector of length one or two giving the integer dimensions of the desired coordinate projections for multivariate data in case of `"classification"` or `"uncertainty"` plots.
`xlab, ylab` | Optional labels for the x-axis and the y-axis.
`ylim` | Optional limits for the vertical axis of the BIC plot.
`addEllipses` | A logical indicating whether or not to add ellipses with axes corresponding to the within-cluster covariances in case of `"classification"` or `"uncertainty"` plots.
`main` | A logical or `NULL` indicating whether or not to add a title to the plot identifying the dimensions used.
`...` | Other graphics parameters.

### Details

For more flexibility in plotting, use `mclust1Dplot`, `mclust2Dplot`, `surfacePlot`, `coordProj`, or `randProj`.

### Value

Model-based clustering plots:

`"BIC"` =

  BIC values used for choosing the number of clusters.

`"classification"` =

  a plot showing the clustering. For data in more than two dimensions a pairs plot is produced, followed by a coordinate projection plot using specified `dimens`.

`"uncertainty"` =

  a plot of classification uncertainty. For data in more than two dimensions a coordinate projection plot is drawn using specified `dimens`.

`"density"` =

  a plot of estimated density. For two dimensional a contour plot is drawn, while for data in more than two dimensions a matrix of contours for pairs of variables is produced.

### References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### See Also

`Mclust`, `plot.mclustBIC`, `plot.mclustICL`, `mclust1Dplot`, `mclust2Dplot`, `surfacePlot`, `coordProj`, `randProj`.

### Examples

    precipMclust <- Mclust(precip)
    plot(precipMclust)

    faithfulMclust <- Mclust(faithful)
    plot(faithfulMclust)

    irisMclust <- Mclust(iris[,-5])
    plot(irisMclust)

[Package mclust version 5.3 Index]