Description Usage Arguments Value Note See Also Examples
Maximization step in the EM algorithm for parameterized Gaussian mixture models.
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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. The default assumes no prior. 
warn 
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is given by 
... 
Catches unused arguments in indirect or list calls via 
A list including the following components:
modelName 
A character string identifying the model (same as the input argument). 
parameters 

Attributes: 

This function computes the Mstep only for MVN mixtures, so in
analyses involving noise, the conditional probabilities input should
exclude those for the noise component.
In contrast to me
for the EM algorithm, computations in mstep
are carried out unless failure due to overflow would occur. To impose
stricter tolerances on a single mstep
, use me
with the
itmax component of the control
argument set to 1.
mstepE
, ...,
mstepVVV
,
emControl
,
me
,
estep
,
mclust.options
.
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