me.Rd
Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.
me(data, modelName, z, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)
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. 

modelName  A character string indicating the model. The help file for

z  A matrix whose 
prior  Specification of a conjugate prior on the means and variances.
See the help file for 
control  A list of control parameters for EM. The defaults are set by the call

Vinv  If the model is to include a noise term, 
warn  A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set in 
...  Catches unused arguments in indirect or list calls via 
A list including the following components:
A character string identifying the model (same as the input argument).
The number of observations in the data.
The dimension of the data.
The number of mixture components.
A matrix whose [i,k]
th entry is the
conditional probability of the ith observation belonging to
the kth component of the mixture.
pro
A vector whose kth component is the mixing proportion for the kth component of the mixture model. If the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components.
mean
The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
variance
A list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for mclustVariance
for details.
Vinv
The estimate of the reciprocal hypervolume of the data region used in the computation when the input indicates the addition of a noise component to the model.
The log likelihood for the data in the mixture model.
The list of control parameters for EM used.
The specification of a conjugate prior on the means and variances used,
NULL
if no prior is used.
"info"
Information on the iteration.
"WARNING"
An appropriate warning if problems are encountered
in the computations.
meE
, ...,
meVVV
,
em
,
mstep
,
estep
,
priorControl
,
mclustModelNames
,
mclustVariance
,
mclust.options