Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.

```
me(modelName, data, z, prior = NULL, control = emControl(),
Vinv = NULL, warn = NULL, …)
```

modelName

A character string indicating the model. The help file for
`mclustModelNames`

describes the available models.

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.

z

A matrix whose `[i,k]`

th entry is an initial estimate of the
conditional probability of the ith observation belonging to
the *k*th component of the mixture.

prior

Specification of a conjugate prior on the means and variances.
See the help file for `priorControl`

for further information.
The default assumes no prior.

control

A list of control parameters for EM. The defaults are set by the call
`emControl()`

.

Vinv

If the model is to include a noise term, `Vinv`

is an estimate of the
reciprocal hypervolume of the data region. If set to a negative value
or 0, the model will include a noise term with the reciprocal hypervolume
estimated by the function `hypvol`

.
The default is not to assume a noise term in the model through the
setting `Vinv=NULL`

.

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 `mclust.options("warn")`

.

…

Catches unused arguments in indirect or list calls via `do.call`

.

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 *i*th observation belonging to
the *k*th component of the mixture.

`pro`

A vector whose

*k*th component is the mixing proportion for the*k*th 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

*k*th 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`

```
# NOT RUN {
me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
# }
```

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