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

## To run an example, see man page for bic
## Not run: 
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
## End(Not run)

[Package mclust version 2.1-11 Index]