mclust: mclustModel – R documentation

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mclustModel

Best model based on BIC

Description

Determines the best model from clustering via mclustBIC for a given set of model parameterizations and numbers of components.

Usage

mclustModel(data, BICvalues, G, modelNames, ...)

Arguments

`data`	The matrix or vector of observations used to generate ‘object’.
`BICvalues`	An `'mclustBIC'` object, which is the result of applying `mclustBIC` to `data`.
`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 `BICvalues`). The default is to select the best model for all numbers of mixture components used to obtain `BICvalues`.
`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 `BICvalues`). The default is to select the best model for parameterizations used to obtain `BICvalues`.
`...`	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 indicating the model. The help file for `mclustModelNames` describes the available models.
`n`	The number of observations in the data.
`d`	The dimension of the data.
`G`	The number of components in the Gaussian mixture 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. `Vinv` The estimate of the reciprocal hypervolume of the data region used in the computation when the input indicates the addition of a noise component to the model.
`z`	A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.

Examples

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

mclust

Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

v5.4.10

GPL (>= 2)

Authors

Chris Fraley [aut], Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>), Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)

Initial release

2022-05-20