DPXEM: DPXEM

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

DPXEM

Description

The DPXEM method is mainly used for clustering analysis of large-scale datasets. It distributes the dataset across different computing nodes, processes the data in parallel, and updates model parameters. Through parallel processing, the DPXEM algorithm can significantly improve the speed of processing large-scale datasets.

Usage

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

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