mclust (version 5.0.2)

plot.Mclust: Plot Model-Based Clustering Results

Description

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

Usage

## 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: [object Object],[object Object],[object Object],[object Object] 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 if what = "classification".

main

A logical variable 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: [object Object],[object Object],[object Object],[object Object]

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)