22_emcluster: EM Algorithm for model-based clustering

In snoweye/EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution

EM Algorithm for model-based clustering

Description

These are core functions of EMCluster performing EM algorithm for model-based clustering of finite mixture multivariate Gaussian distribution with unstructured dispersion.

Usage

emcluster(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL,
          lab = NULL, EMC = .EMC, assign.class = FALSE)
shortemcluster(x, emobj = NULL, pi = NULL, Mu = NULL,
               LTSigma = NULL, maxiter = 100, eps = 1e-2)
simple.init(x, nclass = 1)

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.
lab	labeled data for semi-supervised clustering, length n.
EMC	the control for the EM iterations.
assign.class	if assigning class id.
maxiter	maximum number of iterations.
eps	convergent tolerance.
nclass	the desired number of clusters, K.

Details

The emcluster mainly performs EM iterations starting from the given parameters emobj without other initializations.

The shortemcluster performs short-EM iterations as described in init.EM.

Value

The emcluster returns an object emobj with class emret which can be used in post-process or other functions such as e.step, m.step, assign.class, em.ic, and dmixmvn.

The shortemcluster also returns an object emobj with class emret which is the best of several random initializations.

The simple.init utilizes rand.EM to obtain a simple initial.

Author(s)

Wei-Chen Chen [e-mail] and Ranjan Maitra.

References

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

See Also

init.EM, e.step, m.step, .EMControl.

Examples

library(EMCluster, quietly = TRUE)
set.seed(1234)
x1 <- da1$da

emobj <- simple.init(x1, nclass = 10)
emobj <- shortemcluster(x1, emobj)
summary(emobj)

ret <- emcluster(x1, emobj, assign.class = TRUE)
summary(ret)

snoweye/EMCluster documentation built on Sept. 12, 2023, 4:57 a.m.