R: EM algorithm starting with M-step for parameterized MVN...

me {mclust} | R Documentation

## EM algorithm starting with M-step for parameterized MVN mixture models

### Description

Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.

### Usage

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

### Arguments

`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 kth component of the mixture.
`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 in `mclust.options("warn")`.
`...` | Catches unused arguments in indirect or list calls via `do.call`.

### Value

A list including the following components:

`modelName` | A character string identifying the model (same as the input argument).
`n` | The number of observations in the data.
`d` | The dimension of the data.
`G` | The number of mixture components.
`z` | A matrix whose `[i,k]`th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
`parameters` | `pro` A vector whose kth component is the mixing proportion for the kth 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 kth 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.
`loglik` | The log likelihood for the data in the mixture model.
`control` | The list of control parameters for EM used.
`prior` | The specification of a conjugate prior on the means and variances used, `NULL` if no prior is used.
`Attributes:` | `"info"` Information on the iteration. `"WARNING"` An appropriate warning if problems are encountered in the computations.

### See Also

`meE`, ..., `meVVV`, `em`, `mstep`, `estep`, `priorControl`, `mclustModelNames`, `mclustVariance`, `mclust.options`

### Examples

    me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))

[Package mclust version 5.4.9 Index]