mclust (version 5.0.2)

mclustICL: ICL Criterion for Model-Based Clustering

Description

ICL (Integrated Complete-data Likelihood) for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.

Usage

mclustICL(data, G = NULL, modelNames = NULL, 
          initialization = list(hcPairs = NULL, 
                                subset = NULL, 
                                noise = NULL), 
          ...)
## S3 method for class 'mclustICL':
summary(object, G, modelNames, \dots)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

G

An integer vector specifying the numbers of mixture components (clusters) for which the criteria should be calculated. The default is G = 1:9.

modelNames

A vector of character strings indicating the models to be fitted in the EM phase of clustering. The help file for mclustModelNames describes the available models. The default is:

initialization

A list containing zero or more of the following components: [object Object],[object Object]

...

Futher arguments used in the call to Mclust. See also mclustBIC.

object

An integer vector specifying the numbers of mixture components (clusters) for which the criteria should be calculated. The default is G = 1:9.

Value

• Returns an object of class 'mclustICL' containing the the ICL criterion for the specified mixture models and numbers of clusters.

  The corresponding print method shows the matrix of values and the top models according to the ICL criterion. The summary method shows only the top models.

References

Biernacki, C., Celeux, G., Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Analysis and Machine Intelligence, 22 (7), 719-725.

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

plot.mclustICL, Mclust, mclustBIC, mclustBootstrapLRT, bic, icl

Examples

data(faithful)
faithful.ICL <- mclustICL(faithful)
faithful.ICL
summary(faithful.ICL)
plot(faithful.ICL)
# compare with
faithful.BIC = mclustBIC(faithful)
faithful.BIC
plot(faithful.BIC)