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.

Examples

precipMclust <- Mclust(precip)
plot(precipMclust)

faithfulMclust <- Mclust(faithful)
plot(faithfulMclust)

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

[Package mclust version 5.3 Index]