bic: BIC for Parameterized Gaussian Mixture Models

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

BIC for Parameterized Gaussian Mixture Models

Description

Computes 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, noise=FALSE, equalPro=FALSE, ...)

Arguments

modelName	A character string indicating the model. The help file for mclustModelNames describes the available models.
loglik	The log-likelihood for a data set with respect to the Gaussian mixture model specified in the modelName argument.
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 Gaussian mixture model used to compute loglik.
noise	A logical variable indicating whether or not the model includes an optional Poisson noise component. The default is to assume no noise component.
equalPro	A logical variable indicating whether or not the components in the model are assumed to be present in equal proportion. The default is to assume unequal mixing proportions.
...	Catches unused arguments in an indirect or list call via do.call.

Value

The BIC or Bayesian Information Criterion for the given input arguments.

See Also

mclustBIC, nVarParams, mclustModelNames.

Examples

n <- nrow(iris)
d <- ncol(iris)-1
G <- 3

emEst <- me(modelName="VVI", data=iris[,-5], unmap(iris[,5]))
names(emEst)

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

mclust documentation built on May 29, 2024, 8:06 a.m.