PPC1.SFM: Apply the PPC method to the Skew factor model

In SFM: A Package for Analyzing Skew Factor Models

Apply the PPC method to the Skew factor model

Description

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

Usage

PPC1.SFM(data, 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:

Ap	Estimated factor loadings.
Dp	Estimated uniquenesses.
MSESigmaA	Mean squared error for factor loadings.
MSESigmaD	Mean squared error for uniquenesses.
LSigmaA	Loss metric for factor loadings.
LSigmaD	Loss metric for uniquenesses.

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 <- PPC1.SFM(data, m, A, D, p)
print(results)

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