R: E-step for parameterized Gaussian mixture models.

estep {mclust} | R Documentation

## E-step for parameterized Gaussian mixture models.

### Description

Implements the expectation step of EM algorithm for parameterized Gaussian mixture models.

### Usage

      estep( modelName, data, parameters, 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.
`parameters` | A names list giving the parameters of the model. The components are as follows: `pro` Mixing proportions for the components of the mixture. 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` An estimate of the reciprocal hypervolume of the data region. If set to NULL or a negative value, the default is determined by applying function `hypvol` to the data. Used only when `pro` includes an additional mixing proportion for a noise component.
`warn` | A logical value indicating whether or not a warning should be issued when computations fail. The default is `warn=FALSE`.
`...` | 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` | The input parameters.
`loglik` | The log-likelihood for the data in the mixture model.
`Attributes` | `"WARNING"`: an appropriate warning if problems are encountered in the computations.

### References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### See Also

`estepE`, ..., `estepVVV`, `em`, `mstep`, `mclust.options` `mclustVariance`

### Examples

    ## Not run:
    msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
    names(msEst)

    estep(modelName = msEst$modelName, data = iris[,-5],
          parameters = msEst$parameters)
    ## End(Not run)

[Package mclust version 5.3 Index]