mclustModel: Best model based on BIC

Description

Determines the best model from clustering via mclustBIC for a given set of model parameterizations and numbers of components.

Usage

mclustModel(data, BICvalues, G, modelNames, ...)

Arguments

data

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

BICvalues

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

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 BICvalues). The default

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 BICvalues). The default is to sele

...

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.
The details of the output components are as follows:
modelNameA character string indicating the model. The help file for mclustModelNames describes the available models.
nThe number of observations in the data.
dThe dimension of the data.
GThe number of components in the Gaussian mixture model corresponding to the optimal BIC.
bicThe optimal BIC value.
loglikThe loglikelihood corresponding to the optimal BIC.
parametersA list with the following components: [object Object],[object Object],[object Object],[object Object]
zA matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.

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.

Examples

Run this code

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