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DSFM (version 1.0.1)

SFM: 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)

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.

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.

Examples

Run this code
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)

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