GulPC_TFM: Apply the GulPC method to the Truncated factor model

In TFM: Sparse Online Principal Component for Truncated Factor Model

Apply the GulPC method to the Truncated 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_TFM(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

## Not run: 
library(SOPC)
library(relliptical)
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)
trnor <- relliptical(n*p,0,1)
epsilon=matrix(trnor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- GulPC_TFM(data, m, A, D)
print(results)
## End(Not run)

TFM documentation built on April 16, 2025, 5:10 p.m.