mclust (version 2.1-14)

summary.EMclust: Summary function for EMclust

Description

Optimal model characteristics and classification for EMclust results.

Usage

summary.EMclust(object, data, G, modelNames, ...)

Arguments

object

An "EMclust" object, which is the result of applying EMclust to data.

data

The matrix or vector of observations used to generate `object'.

G

A vector of integers giving the numbers of mixture components (clusters) over which the summary is to take place (as.character(G) must be a subset of the column names of object). The default is to summarize over a

modelNames

A vector of character strings denoting the models over which the summary is to take place (must be a subset of the row names of `object'). The default is to summarize over all models used in the original analysis.

...

Not used. For generic/method consistency.

Value

• A list giving the optimal (according to BIC) parameters, conditional probabilities z, and loglikelihood, together with the associated classification and its uncertainty.

References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

See Also

EMclust

Examples

data(iris)
irisMatrix <- as.matrix(iris[,1:4])

irisBic <- EMclust(irisMatrix)
summary(irisBic, irisMatrix)
summary(irisBic, irisMatrix, G = 1:6, modelName = c("VII", "VVI", "VVV"))