EFM (version 1.3.0)

FanPC.EFM: Apply the FanPC method to the Elliptical 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.EFM(data, m, A, D, p)

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.

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.

Examples

# \donttest{
library(matrixcalc)
library(MASS)

n <- 100
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 <- matrix(mvrnorm(n, mu0, sigma0), nrow = n)
A <- matrix(runif(p * m, -1, 1), nrow = p)
r <- rnorm(n * p, 0, 1)
epsilon <- matrix(r, nrow = n)
D <- diag(as.vector(apply(epsilon, 2, function(x) sum(x^2))))
data <- mu + F_matrix %*% t(A) + epsilon
results <- FanPC.EFM(data, m, A, D, p)
print(results)
# }