SPC.SFM: Apply the SPC method to the Skew factor model

In SFM: A Package for Analyzing Skew Factor Models

Apply the SPC method to the Skew factor model

Description

This function performs Sparse Principal Component Analysis (SPC) on the input data. It estimates factor loadings and uniquenesses while calculating mean squared errors and loss metrics for comparison with true values.

Usage

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

Arguments

data	The data used in the SPC analysis.
A	The true factor loadings matrix.
D	The true uniquenesses matrix.
m	The number of common factors.
p	The number of variables.

Value

A list containing:

As	Estimated factor loadings, a matrix of estimated factor loadings from the SPC analysis.
Ds	Estimated uniquenesses, a vector of estimated uniquenesses corresponding to each variable.
MSESigmaA	Mean squared error of the estimated factor loadings (As) compared to the true loadings (A).
MSESigmaD	Mean squared error of the estimated uniquenesses (Ds) compared to the true uniquenesses (D).
LSigmaA	Loss metric for the estimated factor loadings (As), indicating the relative error compared to the true loadings (A).
LSigmaD	Loss metric for the estimated uniquenesses (Ds), indicating the relative error compared to the true uniquenesses (D).
tau	Proportion of zero factor loadings in the estimated loadings matrix (As).

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 <- SPC.SFM(data, A, D, m, p)
print(results)

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