30-em.one: One EM Step for GBD

In snoweye/pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

One EM Step for GBD

Description

One EM step only for model-based clustering of finite mixture Gaussian models with unstructured dispersions. This is a core function of em.step.

Usage

  em.onestep(PARAM)

Arguments

PARAM

an original set of parameters generated by set.global.

Details

A global variable called X.spmd should exist in the .pmclustEnv environment, usually the working environment. The X.spmd is the data matrix to be clustered, and this matrix has a dimension N.spmd by p.

The PARAM will be a local variable for the current iteration inside em.onestep, and this variable is a list containing all parameters related to models. This function also updates in the parameters by the EM algorithm, and return a new PARAM for the next iteration. The details of list elements are initially generated by set.global.

Value

This function is one EM step. The global variables will be updated and a new PARAM will be returned. See the help page of PARAM or PARAM.org for details.

Author(s)

Wei-Chen Chen [e-mail] and George Ostrouchov.

References

Programming with Big Data in R Website: https://pbdr.org/

See Also

set.global, e.step, m.step.

Examples

## Not run: 
# This is a core function for em.step()
# see the source code for details.

## End(Not run)

snoweye/pmclust documentation built on Sept. 12, 2023, 5:42 a.m.