Single Step {EMCluster}  R Documentation 
These functions are single E and Mstep of EM algorithm for modelbased clustering of finite mixture multivariate Gaussian distribution with unstructured dispersion.
e.step(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL,
norm = TRUE)
m.step(x, emobj = NULL, Gamma = NULL, assign.class = FALSE)
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,

norm 
if returning normalized 
Gamma 
containing posterior probabilities if normalized,
otherwise containing component densities weighted by
mixing proportion, dimension 
assign.class 
if assigning class id. 
These two functions are mainly used in debugging for development and post process after model fitting.
The e.step
returns a list contains Gamma
, the posterior
probabilities if norm=TRUE
, otherwise it contains component densities.
This is one Estep and Gamma
is used to update emobj
in
the Mstep next.
The m.step
returns a new emobj
according to the Gamma
from the Estep above.
WeiChen Chen wccsnow@gmail.com and Ranjan Maitra.
https://www.stat.iastate.edu/people/ranjanmaitra
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