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 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.