DIRMR (version 0.5.0)

DEM: 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)

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

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

Author

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)