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mclust (version 3.4.7)

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.
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
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 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

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

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