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

meE {mclust} | R Documentation

## EM algorithm starting with M-step for a parameterized Gaussian mixture model

### Description

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

### Usage

    meE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meX(data, prior = NULL, warn = NULL, ...)
    meEII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meEVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meVVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...)
    meXII(data, prior = NULL, warn = NULL, ...)
    meXXI(data, prior = NULL, warn = NULL, ...)
    meXXX(data, prior = 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.
`z` | A matrix whose `[i,k]`th entry is 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. The default assumes no prior.
`control` | A list of control parameters for EM. The defaults are set by the call `emControl()`.
`Vinv` | An estimate of the reciprocal hypervolume of the data region, when the model is to include a noise term. Set to a negative value or zero if a noise term is desired, but an estimate is unavailable — in that case function `hypvol` will be used to obtain the estimate. 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 given by `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).
`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.
`Attributes:` | `"info"` Information on the iteration. `"WARNING"` An appropriate warning if problems are encountered in the computations.

### See Also

`em`, `me`, `estep`, `mclust.options`

### Examples

    meVVV(data = iris[,-5], z = unmap(iris[,5]))

[Package mclust version 5.4.9 Index]