PXEM: PXEM

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

PXEM

Description

The PXEM method is an algorithm that accelerates the convergence rate of the EM algorithm. By introducing additional parameters, improving the model, and expanding it, it has better parameter estimation results compared to the EM method.

Usage

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

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