DIRMR (version 0.5.0)

PXEM: 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)

Value

Y01

The response variable value after projection

Yhat

The estimated response variable value after projection

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

Author

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)