12_d.param: A Set of Parameters in Model-Based Clustering.

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

Description

This set of parameters are used in initialization, EM iterations, and final convergent results. All share the same structure in a list variable.

Format

A list variable contains several parameters for computing.

Details

The elements of PARAM or PARAM.org are

N	number of observations
p	dimension of each observation, total number of variables
K	number of clusters
ETA	mixing proportion
log.ETA	log of mixing proportion
MU	centers, dim = \code{p*K}
SIGMA	dispersions, a list containing K elements, each element is a matrix, dim = \code{p*p}
U	Choleski of SIGMA, the same size of SIGMA
U.check	checks of each elements of U, length K
logL	log likelihood
min.N.CLASS	minimum number of elements in a cluster (restrictions)

The model parameters are ETA, MU, and SIGMA, while log.ETA, U, U.check, and min.N.CLASS are only used in computing.

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.

Examples

## Not run: 
# Use set.global() to generate one of this.
# X.spmd should be pre-specified before calling set.global().

## End(Not run)

pmclust documentation built on Feb. 11, 2021, 5:05 p.m.