OPC_LFM: Apply the OPC method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the OPC method to the Laplace factor model

Description

This function computes Online Principal Component Analysis (OPC) for the provided input data, estimating factor loadings and uniquenesses. It calculates mean squared errors and sparsity for the estimated values compared to true values.

Usage

OPC_LFM(data, m = m, A, D, p)

Arguments

data	A matrix of input data.
m	The number of principal components.
A	The true factor loadings matrix.
D	The true uniquenesses matrix.
p	The number of variables.

Value

A list containing:

Ao	Estimated factor loadings.
Do	Estimated uniquenesses.
MSEA	Mean squared error for factor loadings.
MSED	Mean squared error for uniquenesses.
tau	The sparsity.

Examples

library(SOPC)
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 <- OPC_LFM(data, m, A, D, p)
print(results)

LFM documentation built on April 16, 2025, 9:07 a.m.