EM Algorithm {EMCluster}  R Documentation 
These are core functions of EMCluster performing EM algorithm for modelbased clustering of finite mixture multivariate Gaussian distribution with unstructured dispersion.
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 = 1e2)
simple.init(x, nclass = 1)
x 
the data matrix, dimension 
emobj 
the desired model which is a list mainly contains 
pi 
the mixing proportion, length 
Mu 
the centers of clusters, dimension 
LTSigma 
the lower triangular matrices of dispersion,

lab 
labeled data for semisupervised clustering,
length 
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, 
The emcluster
mainly performs EM iterations starting from the given
parameters emobj
without other initializations.
The shortemcluster
performs shortEM iterations as described in
init.EM
.
The emcluster
returns an object emobj
with class emret
which can be used in postprocess 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.
WeiChen Chen wccsnow@gmail.com and Ranjan Maitra.
https://www.stat.iastate.edu/people/ranjanmaitra
init.EM
, e.step
, m.step
,
.EMControl
.
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)