LFM (version 0.2.0)

OPC_LFM: 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)

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.

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.

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)