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Mclust(data, G=NULL, modelNames=NULL, prior=NULL, control=emControl(),
initialization=NULL, warn=FALSE, ...)
G=1:9
.mclustModelNames
describes the available models.
The default is c("E", "V")
for univariate data and
priorControl
.emControl()
.hc
. For multivariate data, the default is to compute
a hieraz
, and loglikelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:map(z)
: The classification corresponding to z
.do.call
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631.
C. Fraley and A. E. Raftery (2005, revised 2009). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.
C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.
priorControl
,
emControl
,
mclustBIC
,
mclustModelNames
,
mclustOptions
irisMclust <- Mclust(iris[,-5])
plot(irisMclust)
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