summary.EMclust {mclust02}R Documentation

Summary function for EMclust

Description

Optimal model characteristics and classification for EMclust results.

Usage

## S3 method for class 'EMclust':
summary(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 all of the numbers of mixture components used in the original analysis.
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"))

[Package mclust02 version 2.1-18 Index]