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