DLFM (version 0.2.2)

SAPC: The stochastic approximation principal component can handle online data sets with highly correlated.

Description

The stochastic approximation principal component can handle online data sets with highly correlated.

Usage

SAPC(data, m, eta)

Value

Asa,Dsa

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

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
SAPC(data=data,m=3,eta=0.8)