modalityMediationDataGen: Data Generation for High-Dimensional Mediation Model

In HDMAADMM: ADMM for High-Dimensional Mediation Models

Data Generation for High-Dimensional Mediation Model

Description

Data Generation for High-Dimensional Mediation Model

Usage

modalityMediationDataGen(
  n = 100,
  p = 50,
  sigmaY = 1,
  sizeNonZero = c(3, 3, 4),
  alphaMean = c(6, 4, 2),
  alphaSd = 0.1,
  betaMean = c(6, 4, 2),
  betaSd = 0.1,
  sigmaM1 = NULL,
  gamma = 3,
  generateLaplacianMatrix = FALSE,
  seed = 20231201
)

Arguments

n	The number of subjects for the high-dimensional mediation model)
p	The number of high-dimensional mediators.
sigmaY	The argument "sigmaY" represents the standard deviation (SD) of the error distribution for the dependent variable.
sizeNonZero	The number of nonzero mediators. Here, we provide simulated scenarios that could produce large, medium, and small mediated effects, generating from a normal distribution.
alphaMean, alphaSd	The mean and SD vector of the effect between the mediator and independent variable.
betaMean, betaSd	The mean and SD vector of the effect between the mediator and dependent variable.
sigmaM1	The covariance matrix of the error distribution among mediators. Default is diag(p).
gamma	The true value of direct effect.
generateLaplacianMatrix	A logical value to specify whether to generate Laplacian matrix for network penalty.
seed	The random seed. Default is NULL to use the current seed.

Value

A object with three elements.

• MediData: The simulated data for high-dimensional mediation model.

• MediPara: The true value for mediated effect and direct effect.

• Info : The output includes random seed, parameter setting, and Laplacian matrix for generating mediation model.

Examples

simuData <- modalityMediationDataGen(seed = 20231201)

HDMAADMM documentation built on May 29, 2024, 12:08 p.m.