DECME: DECME

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

DECME

Description

The DECME algorithm can significantly improve the speed of processing large-scale data sets. It can reduce the algorithm's memory requirements, enabling the algorithm to handle larger data sets.

Usage

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

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