DEM: DEM

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

DEM

Description

The DEM method is mainly applied to statistical analysis of large-scale datasets, where the dataset is distributed across different computing nodes to process data in parallel and update model parameters.

Usage

DEM(data,df1,M,maxiter)

Arguments

data	The real data sets with missing data used in the method
df1	The real data sets used in the method
M	The number of Blocks
maxiter	The maximum number of iterations

Value

Y011	The response variable value after projection for each block
Yhat	The estimated response variable value after projection for each block
Ymean	The mean of response variable value after projection for each block
Yhatmean	The mean of response variable value after projection for each block

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)
DEM(data,df1,M=2,maxiter=15)

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