IPC: Apply the IPC method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the IPC method to the Laplace factor model

Description

This function performs Incremental Principal Component Analysis (IPC) on the provided data. It updates the estimated factor loadings and uniquenesses as new data points are processed, calculating mean squared errors and loss metrics for comparison with true values.

Usage

IPC(data, m, eta)

Arguments

data	The data used in the IPC analysis.
m	is the number of principal component
eta	is the proportion of online data to total data

Value

Ai,Di

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
results <- IPC(data, m, eta=0.1)
print(results)

LFM documentation built on Dec. 6, 2025, 5:06 p.m.