OPC_TFM: Apply the OPC method to the Truncated factor model

In TFM: Sparse Online Principal Component for Truncated Factor Model

Apply the OPC method to the Truncated 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_TFM(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

## Not run: 
library(SOPC)
library(relliptical)
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)
trnor <- relliptical(n*p,0,1)
epsilon=matrix(trnor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- OPC_TFM(data, m, A, D, p)
print(results)
## End(Not run)

TFM documentation built on April 16, 2025, 5:10 p.m.