DIRMR (version 0.5.0)

DMOPXEM: DMOPXEM

Description

In DMOPXEM method, data is allocated to different computing nodes for parallel processing. Each node independently executes the EM algorithm and updates the local model parameters. Then, each node passes the local model parameters to other nodes for the merging and updating of global model parameters.

Usage

DMOPXEM(data,df1,M,omega,maxiter)

Value

Y011

The response variable value after projection for each block

Yhat

The estimated response variable value after projection for each block

Ymean

The mean of response variable value after projection for each block

Yhatmean

The mean of response variable value after projection for each block

Arguments

data

The real data sets with missing data used in the method

df1

The real data sets used in the method

M

The number of Blocks

omega

A variable of this method

maxiter

The maximum number of iterations

Author

Guangbao Guo,Yu Li

Examples

set.seed(99)
library(MASS)
library(mvtnorm)
n=50;p=6;q=5;M=2;omega=0.15;ratio=0.1;maxiter=15;nob=round(n-(n*ratio))
dd.start=1;sigma2_e.start=1
X0=matrix(runif(n*p,0,2),ncol=p)
beta=matrix(rnorm(p*1,0,3),nrow=p)
Z0=matrix(runif(n*q,2,3),ncol=q)
e=matrix(rnorm(n*1,0,sigma2_e.start),n,1)
b=matrix(rnorm(q*1,0,1),q,1)
Y0=X0
df1=data.frame(Y=Y0,X=X0,Z=Z0)
misra=function(data,ratio){
  nob=round(n-(n*ratio))
  data[sample(n,n-nob),1]=NA
  return(data)}
data=misra(data=df1,ratio=0.1)
DMOPXEM(data,df1,M=2,omega=0.15,maxiter=15)