Maximization step in the EM algorithm for a parameterized Gaussian mixture model.

```
mstepE( data, z, prior = NULL, warn = NULL, ...)
mstepV( data, z, prior = NULL, warn = NULL, ...)
mstepEII( data, z, prior = NULL, warn = NULL, ...)
mstepVII( data, z, prior = NULL, warn = NULL, ...)
mstepEEI( data, z, prior = NULL, warn = NULL, ...)
mstepVEI( data, z, prior = NULL, warn = NULL, control = NULL, ...)
mstepEVI( data, z, prior = NULL, warn = NULL, ...)
mstepVVI( data, z, prior = NULL, warn = NULL, ...)
mstepEEE( data, z, prior = NULL, warn = NULL, ...)
mstepEEV( data, z, prior = NULL, warn = NULL, ...)
mstepVEV( data, z, prior = NULL, warn = NULL, control = NULL,...)
mstepVVV( data, z, prior = NULL, warn = NULL, ...)
mstepEVE( data, z, prior = NULL, warn = NULL, control = NULL, ...)
mstepEVV( data, z, prior = NULL, warn = NULL, ...)
mstepVEE( data, z, prior = NULL, warn = NULL, control = NULL, ...)
mstepVVE( data, z, prior = NULL, warn = NULL, control = 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.

- 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")`

.- control
Values controlling termination for models

`"VEI"`

and`"VEV"`

that have an iterative M-step. This should be a list with components named*itmax*and*tol*. These components can be of length 1 or 2; in the latter case,`mstep`

will use the second value, under the assumption that the first applies to an outer iteration (as in the function`me`

). The default uses the default values from the function`emControl`

, which sets no limit on the number of iterations, and a relative tolerance of`sqrt(.Machine$double.eps)`

on successive iterates.- ...
Catches unused arguments in indirect or list calls via

`do.call`

.

`mstep`

,
`me`

,
`estep`

,
`mclustVariance`

,
`priorControl`

,
`emControl`

.

```
# \donttest{
mstepVII(data = iris[,-5], z = unmap(iris[,5]))# }
```

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