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

me.weighted(modelName, data, z, weights = NULL, prior = NULL,
control = emControl(), Vinv = NULL, warn = NULL, ...)

## Arguments

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. |

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` . |

## Value

A list including the following components:

modelNameA character string identifying the model (same as the input argument).

zA 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.

loglikThe log likelihood for the data in the mixture model.

Attributes:`"info"`

Information on the iteration.

`"WARNING"`

An appropriate warning if problems are encountered
in the computations.

## Author

Thomas Brendan Murphy

## See also

`me`

,
`meE`

, ...,
`meVVV`

,
`em`

,
`mstep`

,
`estep`

,
`priorControl`

,
`mclustModelNames`

,
`mclustVariance`

,
`mclust.options`

## Examples