EMCluster (version 0.2-16)

Single Step: 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)

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.

Arguments

x

the data matrix, dimension \(n\times p\).

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 \(K\times p\).

LTSigma

the lower triangular matrices of dispersion, \(K\times p(p+1)/2\).

norm

if returning normalized Gamma.

Gamma

containing posterior probabilities if normalized, otherwise containing component densities weighted by mixing proportion, dimension \(n\times K\).

assign.class

if assigning class id.

Author

Wei-Chen Chen and Ranjan Maitra.

Details

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

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