PXEM {DIRMR} | R Documentation |
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)
[Package DIRMR version 0.5.0 Index]