TFM (version 0.3.0)

PC1_TFM: Apply the PC method to the Truncated factor model

Description

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

Usage

PC1_TFM(data, m, A, D)

Value

A list containing:

A1

Estimated factor loadings.

D1

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.

Arguments

data

The total data set to be analyzed.

m

The number of principal components to retain in the analysis.

A

The true factor loadings matrix.

D

The true uniquenesses matrix.

Examples

if (FALSE) {
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 <- PC1_TFM(data, m, A, D)
print(results)}