PPC2.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 performs Projected Principal Component Analysis (PPC) on a given data set to reduce dimensionality. It calculates the estimated values for the loadings, specific variances, and the covariance matrix.

Usage

PPC2.SFM(data, m, A, D)

Arguments

data	The total data set to be analyzed.
m	The number of principal components.
A	The true factor loadings matrix.
D	The true uniquenesses matrix.

Value

A list containing:

Ap2	Estimated factor loadings.
Dp2	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 <- PPC2.SFM(data, m, A, D)
print(results)

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