R: BIC Plot for Model-Based Clustering

plot.mclustBIC {mclust} | R Documentation

## BIC Plot for Model-Based Clustering

### Description

Plots the BIC values returned by the `mclustBIC` function.

### Usage

    ## S3 method for class 'mclustBIC'
    plot(x, G = NULL, modelNames = NULL,
         symbols = NULL, colors = NULL,
         xlab = NULL, ylab = "BIC", ylim = NULL,
         legendArgs = list(x = "bottomright", ncol = 2, cex = 1, inset = 0.01),
         ...)

### Arguments

`x` | Output from `mclustBIC`.
`G` | One or more numbers of components corresponding to models fit in `x`. The default is to plot the BIC for all of the numbers of components fit.
`modelNames` | One or more model names corresponding to models fit in `x`. The default is to plot the BIC for all of the models fit.
`symbols` | Either an integer or character vector assigning a plotting symbol to each unique class in `classification`. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `unique`). The default is given by `mclust.options("classPlotSymbols")`.
`colors` | Either an integer or character vector assigning a color to each unique class in `classification`. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `unique`). The default is given by `mclust.options("classPlotColors")`.
`xlab` | Optional label for the horizontal axis of the BIC plot.
`ylab` | Label for the vertical axis of the BIC plot.
`ylim` | Optional limits for the vertical axis of the BIC plot.
`legendArgs` | Arguments to pass to the `legend` function. Set to `NULL` for no legend.
`...` | Other graphics parameters.

### Value

A plot of the BIC values.

### 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

`mclustBIC`

### Examples

    ## Not run:
    plot(mclustBIC(precip), legendArgs =  list(x = "bottomleft"))

    plot(mclustBIC(faithful))

    plot(mclustBIC(iris[,-5]))

    ## End(Not run)

[Package mclust version 5.3 Index]