mclust (version 2.1-14)

bicE: 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

## To run an example, see man page for bic
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