mclust (version 2.1-14)

EMclust: BIC for Model-Based Clustering

Description

BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models.

Usage

EMclust(data, G, emModelNames, hcPairs, subset, eps, tol, itmax, equalPro,
        warnSingular, ...)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

G

An integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated. The default is 1:9.

emModelNames

A vector of character strings indicating the models to be fitted in the EM phase of clustering. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal vo

hcPairs

A matrix of merge pairs for hierarchical clustering such as produced by function hc. The default is to compute a hierarchical clustering tree by applying function hc with modelName = .Mclust$hcModelName[1]

subset

A logical or numeric vector specifying the indices of a subset of the data to be used in the initial hierarchical clustering phase.

eps

A scalar tolerance for deciding when to terminate computations due to computational singularity in covariances. Smaller values of eps allow computations to proceed nearer to singularity. The default is .Mclust$eps.

tol

A scalar tolerance for relative convergence of the loglikelihood. The default is .Mclust$tol.

itmax

An integer limit on the number of EM iterations. The default is .Mclust$itmax.

equalPro

Logical variable indicating whether or not the mixing proportions are equal in the model. The default is .Mclust$equalPro.

warnSingular

A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is warnSingular=FALSE.

...

Provided to allow lists with elements other than the arguments can be passed in indirect or list calls with do.call.

Value

• Bayesian Information Criterion for the specified mixture models numbers of clusters. Auxiliary information returned as attributes.

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

summary.EMclust, EMclustN, hc, me, mclustOptions

Examples

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

irisBic <- EMclust(irisMatrix)
irisBic
plot(irisBic)

irisBic <- EMclust(irisMatrix, subset = sample(1:nrow(irisMatrix), 100))
irisBic
plot(irisBic)