mclust (version 5.2.2)

summary.mclustBIC: Summary function for model-based clustering via BIC

Description

Optimal model characteristics and classification for model-based clustering via mclustBIC.

Usage

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

Arguments

object

An 'mclustBIC' object, which is the result of applying mclustBIC 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) from which the best model according to BIC will be selected (as.character(G) must be a subset of the row names of object). The default is to select the best model for all numbers of mixture components used to obtain object.

modelNames

A vector of integers giving the model parameterizations from which the best model according to BIC will be selected (as.character(model) must be a subset of the column names of object). The default is to select the best model for parameterizations used to obtain object.

...

Not used. For generic/method consistency.

Value

A list giving the optimal (according to BIC) parameters, conditional probabilities z, and log-likelihood, together with the associated classification and its uncertainty.The details of the output components are as follows:

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.

See Also

mclustBIC mclustModel

Examples

irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))