mclust (version 1.1-7)

emclust1: BIC from hierarchical clustering followed by EM for a parameterized Gaussian mixture model.

Description

Bayesian Information Criterion for various numbers of clusters computed from hierarchical clustering followed by EM for a selected parameterization of Gaussian mixture models possibly with Poisson noise.

Usage

emclust1(data, nclus, modelid, k, equal=F, noise, Vinv)

Arguments

data

matrix of observations.

nclus

An integer vector specifying the numbers of clusters for which the BIC is to be calculated. Default: 1:9 without noise; 0:9 with noise.

modelid

An integer or vector of two integers specifying the model(s) to be used in the hierarchical clustering and EM phases of the BIC calculations. The allowed values or modelid and their interpretation are as follows: "EI" : uniform s

k

If k is specified, the hierarchical clustering phase will use a sample of size k of the data in the initial hierarchical clustering phase. The default is to use the entire data set.

equal

Logical variable indicating whether or not the mixing proportions are equal in the model. The default is to assume they are unequal.

noise

A logical vector of length equal to the number of observations in the data, whose elements indicate an initial estimate of noise (indicated by T) in the data. By default, emclust1 fits Gaussian mixture models in which it is assum

Vinv

An estimate of the inverse hypervolume of the data region (needed only if noise is specified). Default : determined by the function hypvol.

Value

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

NOTE

The reciprocal condition estimate returned as an attribute ranges in value between 0 and 1. The closer this estimate is to zero, the more likely it is that the corresponding EM result (and BIC) are contaminated by roundoff error.

References

C. Fraley and A. E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis.Technical Report No. 329, Dept. of Statistics, U. of Washington (February 1998).

R. Kass and A. E. Raftery, Bayes Factors. Journal of the American Statistical Association90:773-795 (1995).

See Also

summary.emclust1, emclust, mhtree, me

Examples

data(iris)
emclust1(iris[,1:4], nclus=2:3, modelid = c("VVV","EEV"))

data(chevron)
noisevec _ rep(0, nrow(chevron))
noisevec[chevron[,2]>60] _ 1
bicvals _ emclust1(chevron, noise=noisevec, nclus=0:5)
sumry _ summary(bicvals, chevron)
plot(chevron, col=ztoc(sumry$z), pch=ztoc(sumry$z))