ECME: ECME

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

ECME

Description

The ECME method calculates the conditional expectation of each hidden variable based on known data and current parameter estimates. Then, based on the known data, the conditional expectation of the hidden variables, and the current parameter estimates, the likelihood function is maximized to update the parameter estimates.

Usage

ECME(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)
ECME(data,df1,maxiter=15)

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