DMOPXEM: DMOPXEM

In DIRMR: Distributed Imputation for Random Effects Models with Missing Responses

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)

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

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

Author(s)

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)

DIRMR documentation built on April 3, 2025, 6:03 p.m.