bic {mclust}R Documentation

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, equal volume, equal shape
"VEI": diagonal, varying volume, equal shape
"EVI": diagonal, equal volume, varying shape
"VVI": diagonal, varying volume, varying shape
"EEE": ellipsoidal, equal volume, shape, and orientation
"EEV": ellipsoidal, equal volume and equal shape
"VEV": ellipsoidal, equal shape
"VVV": ellipsoidal, varying volume, shape, and orientation
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
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.

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)
## Not run: do.call("bic", emEst)    ## alternative call

[Package mclust version 2.1-11 Index]