DLFM (version 0.2.2)

DSAPC: The distributed stochastic approximation principal component for handling online data sets with highly correlated data across multiple nodes.

Description

The distributed stochastic approximation principal component for handling online data sets with highly correlated data across multiple nodes.

Usage

DSAPC(data, m, eta, n1, K)

Value

Asa, Dsa (lists containing results from each node)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

eta

is the proportion of online data to total data

n1

is the length of each data subset

K

is the number of nodes

Examples

library(LaplacesDemon)
library(MASS)
n=1000
p=10
m=5
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
lanor <- rlaplace(n*p,0,1)
epsilon=matrix(lanor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
DSAPC(data=data, m=3, eta=0.8, n1=128, K=2)