mclust (version 2.1-14)

bic: BIC for Parameterized MVN Mixture Models

Description

Compute 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, ...)

Arguments

modelName

A character string indicating the model. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal

loglik

The loglikelihood for a data set with respect to the MVN mixture model specified in the modelName argument.

n

The number of observations in the data use 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.

...

Arguments for diagonal-specific methods, in particular [object Object],[object Object]

Value

• The BIC or Bayesian Information Criterion for the given 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

bicE, ..., bicVVV, EMclust, estep, mclustOptions, do.call.

Examples

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

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

emEst <- me(modelName="VVI", data=irisMatrix, unmap(irisClass))
names(emEst)

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