SOPC.SFM: SOPC Estimation Function

In SFM: A Package for Analyzing Skew Factor Models

SOPC Estimation Function

Description

This function processes Skew Factor Model (SFM) data using the Sparse Online Principal Component (SOPC) method.

Usage

SOPC.SFM(data, m, p, A, D)

Arguments

data	A numeric matrix containing the data used in the SOPC analysis.
m	An integer specifying the number of subsets or common factors.
p	An integer specifying the number of variables in the data.
A	A numeric matrix representing the true factor loadings.
D	A numeric matrix representing the true uniquenesses.

Value

A list containing the following metrics:

Aso	Estimated factor loadings matrix.
Dso	Estimated uniquenesses matrix.
MSEA	Mean squared error of the estimated factor loadings (Aso) compared to the true loadings (A).
MSED	Mean squared error of the estimated uniquenesses (Dso) compared to the true uniquenesses (D).
LSA	Loss metric for the estimated factor loadings (Aso), indicating the relative error compared to the true loadings (A).
LSD	Loss metric for the estimated uniquenesses (Dso), indicating the relative error compared to the true uniquenesses (D).
tauA	Proportion of zero factor loadings in the estimated loadings matrix (Aso), representing the sparsity.

Examples

library(SOPC)
library(matrixcalc)
library(MASS)
library(psych)
library(sn)
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)
r <- rsn(n*p,0,1)
epsilon=matrix(r,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- SOPC.SFM(data, m, p, A, D)
print(results)

SFM documentation built on April 15, 2025, 5:09 p.m.