bicE {mclust} | R Documentation

## BIC for a Parameterized MVN Mixture Model

### Description

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

### Usage

    bicE(loglik, n, G, equalPro, noise = FALSE, ...)
    bicV(loglik, n, G, equalPro, noise = FALSE, ...)
    bicEII(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicVII(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicEEI(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicVEI(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicEVI(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicVVI(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicEEE(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicEEV(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicVEV(loglik, n, d, G, equalPro, noise = FALSE, ...)
    bicVVV(loglik, n, d, G, equalPro, noise = FALSE, ...)

### Arguments

`loglik` | The loglikelihood for a data set with respect to the MVN mixture model.
`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 MVN mixture model used to compute `loglik`.
`equalPro` | A logical variable indicating whether or not the components in the model are assumed to be present in equal proportion. The default is `.Mclust\$equalPro`.
`noise` | A logical variable indicating whether or not the model includes and optional Poisson noise component. The default is to assume that the model does not include a noise component.
`...` | Catch unused arguments from a `do.call` call.

### Value

The BIC or Bayesian Information Criterion for the MVN mixture model and data set corresponding to the input arguments.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`bic`, `EMclust`, `estepE`, `mclustOptions`, `do.call`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]

    n <- nrow(irisMatrix)
    d <- ncol(irisMatrix)
    G <- 3

    emEst <- meVVI(data=irisMatrix, unmap(irisClass))
    names(emEst)

    bicVVI(loglik=emEst$loglik, n=n, d=d, G=G)
    do.call("bicVVI", emEst)  ## alternative call

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