Implements the EM algorithm for a parameterized Gaussian mixture model, starting with the expectation step.

```
emE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emX(data, prior = NULL, warn = NULL, ...)
emEII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emEVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emVVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
emXII(data, prior = NULL, warn = NULL, ...)
emXXI(data, prior = NULL, warn = NULL, ...)
emXXX(data, prior = NULL, warn = NULL, ...)
```

A list including the following components:

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

- z
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.- 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.`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.

`"info"`

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.

- parameters
The parameters of the model:

`pro`

Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of Gaussian components if the mixture model includes a Poisson noise term.

`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`

An estimate of the reciprocal hypervolume of the data region. The default is determined by applying function

`hypvol`

to the data. Used only when`pro`

includes an additional mixing proportion for a noise component.

The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
`priorControl`

.

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

.

A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given in `mclust.options("warn")`

.

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

.

`me`

,
`mstep`

,
`mclustVariance`

,
`mclust.options`

.

```
# \donttest{
msEst <- mstepEEE(data = iris[,-5], z = unmap(iris[,5]))
names(msEst)
emEEE(data = iris[,-5], parameters = msEst$parameters)# }
```

Run the code above in your browser using DataLab