Optimal model characteristics and classification for model-based
clustering via mclustBIC.
# S3 method for mclustBIC
summary(object, data, G, modelNames, …)An 'mclustBIC' object,
which is the result of applying mclustBIC
to data.
The matrix or vector of observations used to generate `object'.
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.
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.
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:
A character string denoting the model corresponding to the optimal BIC.
The number of observations in the data.
The dimension of the data.
The number of mixture components in the model corresponding to the optimal BIC.
The optimal BIC value.
The log-likelihood corresponding to the optimal BIC.
A list with the following components:
proA vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed.
meanThe mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
varianceA list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for mclustVariance
for details.
A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class.
map(z): The classification corresponding to z.
The uncertainty associated with the classification.
"bestBICvalues" Some of the best bic values for the analysis.
"prior" The prior as specified in the input.
"control" The control parameters for EM as specified in
the input.
"initialization" The parameters used to initial EM for
computing the maximum likelihood values used to obtain the BIC.
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.
# NOT RUN {
irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))
# }
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