Maximization step in the EM algorithm for parameterized Gaussian mixture models.

`mstep(data, modelName, z, prior = NULL, warn = NULL, ...)`

A list including the following components:

- modelName
A character string identifying the model (same as the input argument).

- parameters
`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.

`"info"`

For those models with iterative M-steps
(`"VEI"`

and `"VEV"`

), information on the iteration.

`"WARNING"`

An appropriate warning if problems are
encountered in the computations.

- 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

`mclustModelNames`

describes the available models.- z
A matrix whose

`[i,k]`

th entry is the conditional probability of the ith observation belonging to the*k*th component of the mixture. In analyses involving noise, this should not include the conditional probabilities for the noise component.- 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

`mclust.options("warn")`

.- ...
Catches unused arguments in indirect or list calls via

`do.call`

.

`mstepE`

, ...,
`mstepVVV`

,
`emControl`

,
`me`

,
`estep`

,
`mclust.options`

.

```
# \donttest{
mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))# }
```

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