MCEM: MCEM

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

MCEM

Description

The MCEM method is an algorithm that utilizes the Monte Carlo method to solve the difficult E-step integral in the EM algorithm. It avoids complex numerical integration calculations by converting the integral in the E-step into a numerical integral.

Usage

MCEM(data, df1, maxiter)

Arguments

data	The real data sets with missing data used in the method
df1	The real data sets used in the method
maxiter	The maximum number of iterations

Value

Y01	The response variable value after projection
Yhat	The estimated response variable value after projection

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)
MCEM(data,df1,maxiter=15)

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