mclust (version 4.1)

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, 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.

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"))