mclust (version 5.2.2)

plot.Mclust: Plot Model-Based Clustering Results

Description

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

Usage

"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:

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.

Value

Model-based clustering plots:

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.

Details

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

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)