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