nMclustParamsComp: Number of Components of Gaussian Mixture Components

In radeksalac/mclustSBIC: Bayesian Information Criterion for Singular Gaussian Mixture Models

Description

Computes the number of parameters specific to each component distribution for a given parameterized Gaussian mixture model, that is not counting the mixture parameters.

Usage

nMclustParamsComp <- function (modelName, d,
          ...)

Arguments

modelName	A character string indicating the model to be fitted in the EM phase of clustering. The help file for mclustModelNames describes the available models.
d	Number of variables in the data.
...	Catches unused arguments in indirect or list calls via do.call.

Value

Return an object of class 'integer', which is the number of parameters specific to each component excluding the mixture parameters.

References

Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.

Drton M. and Plummer M. (2017) A Bayesian information criterion for singular models, Journal of the Royal Statistical Society, Series B, 79, Part 2, pp. 323-380.

Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.

Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (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

mclustSBIC, mclustMaxLik, mclustSBIClearnCoeff, nMclustParamsShared, priorControl, emControl, mclustModel, summary.mclustBIC, hc, me, mclustModelNames, mclust.options

Examples

irisParamsComp = nMclustParamsComp(modelName = 'VEV', d = ncol(iris[,-5]))

radeksalac/mclustSBIC documentation built on Jan. 15, 2022, 8:12 a.m.