mclust (version 3.4.7)

summary.mclustBIC: Summary Function for model-based clustering.

Description

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

Usage

## S3 method for class 'mclustBIC':
summary(object, data, G, modelNames, \dots)

Arguments

object

An "mclustBIC" object, which is the result of applying mclustBIC to data.

data

The matrix or vector of observations used to generate `object'.

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 object). The default is to

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 object). The default is to select th

...

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.
• parameters
  A list with the following components: [object Object],[object Object],[object Object]
• 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.

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 mclustModel

Examples

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