Implements the expectation step of EM algorithm for parameterized Gaussian mixture models.

`estep( modelName, data, parameters, 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.

parameters

A names list giving the parameters of the model. The components are as follows:

`pro`

Mixing proportions for the components of the mixture. 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`

An estimate of the reciprocal hypervolume of the data region. If set to NULL or a negative value, the default is determined by applying function

`hypvol`

to the data. Used only when`pro`

includes an additional mixing proportion for a noise component.

warn

A logical value indicating whether or not a warning should be issued
when computations fail. The default is `warn=FALSE`

.

…

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).

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.

The input parameters.

The log-likelihood for the data in the mixture model.

`"WARNING"`

: an appropriate warning if problems are
encountered in the computations.

`estepE`

, …,
`estepVVV`

,
`em`

,
`mstep`

,
`mclust.options`

`mclustVariance`

```
# NOT RUN {
msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
names(msEst)
estep(modelName = msEst$modelName, data = iris[,-5],
parameters = msEst$parameters)
# }
```

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