LFM (version 0.2.0)

GulPC_LFM: 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)

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.

Arguments

data

A matrix of input data.

m

The number of principal components.

A

The true factor loadings matrix.

D

The true uniquenesses matrix.

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)