Optimal model characteristics and classification for model-based clustering via mclustBIC.

# S3 method for mclustBIC
summary(object, data, G, modelNames, ...)

## Arguments

object 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.

## 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:

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 log-likelihood corresponding to the optimal BIC.

parameters

A list with the following components:

pro

A vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed.

mean

The 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.

variance

A 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.

z

A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class.

classification

map(z): The classification corresponding to z.

uncertainty

The uncertainty associated with the classification.

Attributes:

"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.

mclustBIC mclustModel

## Examples

irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
#> Best BIC values:
#>              VEV,2        VEV,3      VVV,2
#> BIC      -561.7285 -562.5522369 -574.01783
#> BIC diff    0.0000   -0.8237748  -12.28937
#>
#> Classification table for model (VEV,2):
#>
#>   1   2
#>  50 100 summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))
#> Best BIC values:
#>              VVV,2       VVV,3      VVV,4
#> BIC      -574.0178 -580.839630 -630.59996
#> BIC diff    0.0000   -6.821798  -56.58213
#>
#> Classification table for model (VVV,2):
#>
#>   1   2
#>  50 100 `