SFM: The SFM function is to generate Skew Factor Models data.

In SFM: A Package for Analyzing Skew Factor Models

The SFM function is to generate Skew Factor Models data.

Description

The function supports various distribution types for generating the data, including: Skew-Normal Distribution, Skew-Cauchy Distribution, Skew-t Distribution.

Usage

SFM(n, p, m, xi, omega, alpha, distribution_type)

Arguments

n	Sample size.
p	Sample dimensionality.
m	Number of factors.
xi	A numerical parameter used exclusively in the "Skew-t" distribution, representing the distribution's xi parameter.
omega	A numerical parameter representing the omega parameter of the distribution, which affects the degree of skewness in the distribution.
alpha	A numerical parameter representing the alpha parameter of the distribution, which influences the shape of the distribution.
distribution_type	The type of distribution.

Value

A list containing:

data	A matrix of generated data.
A	A matrix representing the factor loadings.
D	A diagonal matrix representing the unique variances.

Examples

library(MASS)
library(SOPC)
library(sn)
library(matrixcalc)
library(psych)
n <- 100
p <- 10
m <- 5
xi <- 5
omega <- 2
alpha <- 5
distribution_type <- "Skew-Normal Distribution"
X <- SFM(n, p, m, xi, omega, alpha, distribution_type)

SFM documentation built on April 15, 2025, 5:09 p.m.