R: Best model based on BIC

mclustModel {mclust} | R Documentation

## 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.

### See Also

`mclustBIC`

### Examples

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

[Package mclust version 5.4.8 Index]