R: EM Methods and Algorithms

.em.method {phyclust} | R Documentation

## EM Methods and Algorithms

### Description

The varied EM algorithms are implemented in C. The first element is the default value. This is a read-only object and the elemental order is followed in C.

### Usage

    .em.method

### Format

A character vector contains implemented EM algorithms in C.

### Details

`EM` (default) stands for the standard EM algorithm, `ECM` stands for Expectation/Conditional Maximization algorithm, and `AECM` stands for Alternating ECM algorithm. The performance is roughly about `AECM` > `EM` ~ `ECM` which are dependent on the separations of data set.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com

### References

Phylogenetic Clustering Website: https://snoweye.github.io/phyclust/

Dempster, A. and Laird, N. and Rubin, D. (1977) “Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm”, Journal of the Royal Statistical Society Series B, 39:3, 1-38.

Meng, X.-L. and Rubin, D. (1993) “Maximum likelihood estimation via the ECM algorithm: A general framework”, Biometrika, 80:2, 511-567.

Meng, X.-L. and van Dyk, D. (1997) “The EM Algorithm — an Old Folk-song Sung to a Fast New Tune (with discussion)”, Journal of the Royal Statistical Society Series B, 59, 511-567.

### See Also

`.show.option`, `.EMC`, `.EMControl`, `phyclust`.

### Examples

    ## Not run:
    library(phyclust, quiet = TRUE)

    .em.method

    ## End(Not run)

[Package phyclust version 0.1-30 Index]