EM: EM

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

EM

Description

The EM method is an iterative algorithm used for maximum likelihood estimation or maximum posterior probability estimation of parameters in probabilistic models with hidden variables. It is essentially a method for estimating parameters, based on existing sample data, to estimate parameter values that are consistent with the model.

Usage

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

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