DLFM (version 0.2.2)

IPC: Incremental principal component method

Description

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

Usage

IPC(data, m, eta)

Value

Ai,Di

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