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

In SFM: A Package for Analyzing Skew Factor Models

Apply the FanPC method to the Skew factor model

Description

This function performs Factor Analysis via Principal Component (FanPC) on a given data set. It calculates the estimated factor loading matrix (AF), specific variance matrix (DF), and the mean squared errors.

Usage

FanPC.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:

AF	Estimated factor loadings.
DF	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(sn)
library(psych)
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 <- FanPC.SFM(data, m, A, D, p)
print(results)

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