GulPC_LFM: Apply the GulPC method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the GulPC method to the Laplace factor model

Description

This function performs General Unilateral Loading Principal Component (GulPC) analysis on a given data set. It calculates the estimated values for the first layer and second layer loadings, specific variances, and the mean squared errors.

Usage

GulPC_LFM(data, m, A, D)

Arguments

data	A matrix of input data.
m	The number of principal components.
A	The true factor loadings matrix.
D	The true uniquenesses matrix.

Value

A list containing:

AU1	The first layer loading matrix.
AU2	The second layer loading matrix.
DU3	The estimated specific variance matrix.
MSESigmaD	Mean squared error for uniquenesses.
LSigmaD	Loss metric for uniquenesses.

Examples

library(SOPC)
library(LaplacesDemon)
library(MASS)
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)
lanor <- rlaplace(n*p,0,1)
epsilon=matrix(lanor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- GulPC_LFM(data, m, A, D)
print(results)

LFM documentation built on April 16, 2025, 9:07 a.m.