emclust {mclust} | R Documentation |
Bayesian Information Criterion for various models and numbers of clusters computed from hierarchical clustering followed by EM for several parameterizations of Gaussian mixture models possibly with Poisson noise.
emclust(data, nclus, modelid, k, equal=F, noise, Vinv)
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 |
A vector of character strings indicating the models to be fitted.
The allowed values or modelid and their interpretation are as follows:
"EI" : uniform spherical, "VI" : spherical, "EEE" : uniform variance,
"VVV" : unconstrained variance, "EEV" : uniform shape and volume,
"VEV" : uniform shape.
The default is to fit all of the models.
|
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, emclust fits Gaussian mixture models in which it is
assumed there is no noise. If noise is specified, emclust will fit a
Gaussian mixture with a Poisson term for noise in the EM phase.
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
noise is specified). Default : determined by function hypvol
|
Bayesian Information Criterion for the six mixture models and specified numbers of clusters. Auxiliary information returned as attributes.
The hierarchical clustering phase uses the unconstrained model. 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.
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).
summary.emclust
, emclust1
, mhtree
, me
data(iris) bicvals _ emclust(iris[,1:4], nclus=1:3, modelid=c("VVV","EEV","VEV")) data(chevron) noisevec _ rep(0, nrow(chevron)) noisevec[chevron[,2]>60] _ 1 bicvals _ emclust(chevron, noise=noisevec) sumry _ summary(bicvals, chevron) plot(chevron, col=ztoc(sumry$z), pch=ztoc(sumry$z))