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

emE {mclust} | R Documentation

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

### Description

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

### Usage

    emE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emX(data, prior = NULL, warn = NULL, ...)
    emEII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emEVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emVVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
    emXII(data, prior = NULL, warn = NULL, ...)
    emXXI(data, prior = NULL, warn = NULL, ...)
    emXXX(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.
`parameters` | The parameters of the model: `pro` Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of Gaussian components if the mixture model includes a Poisson noise term. `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` An estimate of the reciprocal hypervolume of the data region. The default is determined by applying function `hypvol` to the data. Used only when `pro` includes an additional mixing proportion for a noise component.
`prior` | The default assumes no prior, but this argument allows specification of a conjugate prior on the means and variances through the function `priorControl`.
`control` | A list of control parameters for EM. The defaults are set by the call `emControl()`.
`warn` | A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is given 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).
`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

`me`, `mstep`, `mclustVariance`, `mclust.options`.

### Examples

    msEst <- mstepEEE(data = iris[,-5], z = unmap(iris[,5]))
    names(msEst)

    emEEE(data = iris[,-5], parameters = msEst$parameters)

[Package mclust version 5.4.9 Index]