Implements the EM algorithm for fitting Gaussian mixture models parameterized by eigenvalue decomposition, when observations have weights, starting with the maximization step.

```
me.weighted(data, modelName, z, weights = NULL, prior = NULL,
control = emControl(), Vinv = 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 estimated mixture model.

The BIC value for the estimated 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.

- 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 an initial estimate of the conditional probability of the ith observation belonging to the*k*th component of the mixture.- weights
A vector of positive weights, where the

`[i]`

th entry is the weight for the ith observation. If any of the weights are greater than one, then they are scaled so that the maximum weight is one.- 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 by

`warn`

using`mclust.options`

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

`do.call`

.

T. Brendan Murphy, Luca Scrucca

This is a more efficient version made available with mclust \(ge 6.1\) using Fortran code internally.

`me`

,
`meE`

, ...,
`meVVV`

,
`em`

,
`mstep`

,
`estep`

,
`priorControl`

,
`mclustModelNames`

,
`mclustVariance`

,
`mclust.options`

```
w = rexp(nrow(iris))
w = w/mean(w)
c(summary(w), sum = sum(w))
z = unmap(sample(1:3, size = nrow(iris), replace = TRUE))
MEW = me.weighted(data = iris[,-5], modelName = "VVV",
z = z, weights = w)
str(MEW,1)
```

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