DMOEM function

The DMOEM is an overrelaxation algorithm in distributed manner, which is used to solve the parameter estimation of Poisson mixture model.

DMOEM(
  y,
  M,
  K,
  seed,
  alpha0,
  lambda0,
  MOEMalpha0,
  MOEMlambda0,
  omega,
  T,
  epsilon
)

Arguments

• y: is a data matrix
• M: is the number of subsets
• K: is the number of Poisson distribution
• seed: is the recommended way to specify seeds
• alpha0: is the initial value of the mixing weight under the EM algorithm
• lambda0: is the initial value of the mean under the EM algorithm
• MOEMalpha0: is the initial value of the mixing weight under the monotonically overrelaxed EM algorithm
• MOEMlambda0: is the initial value of the mean under the monotonically overrelaxed EM algorithm
• omega: is the overrelaxation factor
• T: is the number of iterations
• epsilon: is the threshold value

Returns

DMOEMtime,DMOEMalpha,DMOEMlambda

Examples

library(stats)
set.seed(637351)
K=5 
alpha1=c(rep(1/K,K)) 
lambda1=c(1,2,3,4,5) 
n=300 
U=sample(c(1:n),n,replace=FALSE)
y= c(rep(0,n)) 
for(i in 1:n){
if(U[i]<=0.2*n){
y[i] = rpois(1,lambda1[1])} 
else if(U[i]>0.2*n & U[i]<=0.4*n){
y[i] = rpois(1,lambda1[2])} 
else if(U[i]>0.4*n & U[i]<=0.6*n){
y[i] = rpois(1,lambda1[3])} 
else if(U[i]>0.6*n & U[i]<=0.8*n){
y[i] = rpois(1,lambda1[4])}
else if(U[i]>0.8*n ){
y[i] = rpois(1,lambda1[5])} 
}
M=5
seed=637351
set.seed(123) 
e=sample(c(1:n),K)
alpha0= MOEMalpha0=e/sum(e)
lambda0= MOEMlambda0=c(1.5,2.5,3.5,4.5,5.5)
omega=0.8
T=10
epsilon=0.005
DMOEM(y,M,K,seed,alpha0,lambda0,MOEMalpha0,MOEMlambda0,omega,T,epsilon)