R: Single E- and M-step

Single Step {EMCluster} | R Documentation

## Single E- and M-step

### Description

These functions are single E- and M-step of EM algorithm for model-based clustering of finite mixture multivariate Gaussian distribution with unstructured dispersion.

### Usage

    e.step(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL,
              norm = TRUE)
    m.step(x, emobj = NULL, Gamma = NULL, assign.class = FALSE)

### Arguments

`x` | the data matrix, dimension n * p.
`emobj` | the desired model which is a list mainly contains `pi`, `Mu`, and `LTSigma`, usually a returned object from `init.EM`.
`pi` | the mixing proportion, length K.
`Mu` | the centers of clusters, dimension K * p.
`LTSigma` | the lower triangular matrices of dispersion, K * p(p+1)/2.
`norm` | if returning normalized `Gamma`.
`Gamma` | containing posterior probabilities if normalized, otherwise containing component densities weighted by mixing proportion, dimension n * K.
`assign.class` | if assigning class id.

### Details

These two functions are mainly used in debugging for development and post process after model fitting.

### Value

The `e.step` returns a list contains `Gamma`, the posterior probabilities if `norm=TRUE`, otherwise it contains component densities. This is one E-step and `Gamma` is used to update `emobj` in the M-step next.

The `m.step` returns a new `emobj` according to the `Gamma` from the E-step above.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra.

### References

https://www.stat.iastate.edu/people/ranjan-maitra

### See Also

`init.EM`.

### Examples

    library(EMCluster, quietly = TRUE)
    x2 <- da2$da

    emobj <- list(pi = da2$pi, Mu = da2$Mu, LTSigma = da2$LTSigma)
    eobj <- e.step(x2, emobj = emobj)
    emobj <- m.step(x2, emobj = eobj)
    emobj

[Package EMCluster version 0.2-13 Index]