mclustModel {mclust}R Documentation

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.
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 BICvalues). The default is to select the best model for all numbers of mixture components used to obtain BICvalues.
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 select the best model for parameterizations used to obtain BICvalues.
... 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:

modelName A character string denoting the model corresponding to the optimal BIC.
n The number of observations in the data.
d The dimension of the data.
G The number of mixture components in the model corresponding to the optimal BIC.
bic The optimal BIC value.
loglik The loglikelihood corresponding to the optimal BIC.
z A 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 and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.

See Also

mclustBIC

Examples

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

[Package mclust version 3.1-10.3 Index]