R: BIC for Parameterized Gaussian Mixture Models

bic {mclust} | R Documentation

## BIC for Parameterized Gaussian Mixture Models

### Description

Computes the BIC (Bayesian Information Criterion) for parameterized mixture models given the loglikelihood, the dimension of the data, and number of mixture components in the model.

### Usage

    bic(modelName, loglik, n, d, G, noise=FALSE, equalPro=FALSE, ...)

### Arguments

`modelName` | A character string indicating the model. The help file for `mclustModelNames` describes the available models.
`loglik` | The log-likelihood for a data set with respect to the Gaussian mixture model specified in the `modelName` argument.
`n` | The number of observations in the data used to compute `loglik`.
`d` | The dimension of the data used to compute `loglik`.
`G` | The number of components in the Gaussian mixture model used to compute `loglik`.
`noise` | A logical variable indicating whether or not the model includes an optional Poisson noise component. The default is to assume no noise component.
`equalPro` | A logical variable indicating whether or not the components in the model are assumed to be present in equal proportion. The default is to assume unequal mixing proportions.
`...` | Catches unused arguments in an indirect or list call via `do.call`.

### Value

The BIC or Bayesian Information Criterion for the given input arguments.

### See Also

`mclustBIC`, `nVarParams`, `mclustModelNames`.

### Examples

    n <- nrow(iris)
    d <- ncol(iris)-1
    G <- 3

    emEst <- me(modelName="VVI", data=iris[,-5], unmap(iris[,5]))
    names(emEst)

    args(bic)
    bic(modelName="VVI", loglik=emEst$loglik, n=n, d=d, G=G)
    # do.call("bic", emEst)    ## alternative call

[Package mclust version 5.4.8 Index]