generate_all_data: Data simulated from a directed acyclic graph (DAG) model

In abima: Adaptive Bootstrap Inference for Mediation Analysis with Enhanced Statistical Power

Data simulated from a directed acyclic graph (DAG) model

Description

Simulate data from a directed acyclic graph (DAG) model.

Usage

generate_all_data(
  n = 200,
  alpha_S = 0,
  beta_M = 0,
  tau_S = 1,
  alpha_vec = rep(1, 3),
  beta_vec = rep(1, 3),
  M.family = stats::gaussian(),
  Y.family = stats::gaussian(),
  sigma_M = 0.5,
  sigma_Y = 0.5
)

Arguments

n	a sample size
alpha_S	a parameter in mediator model M~S+X for S
beta_M	a parameter in outcome model Y~S+M+X for M
tau_S	a parameter in outcome model Y~S+M+X for S
alpha_vec	a parameter vector in mediator model M~S+X for X
beta_vec	a parameter vector in outcome model Y~S+M+X for X
M.family	an error distribution and link function to be used in the mediator model. See family for details of family functions. The default family is gaussian.
Y.family	an error distribution and link function to be used in the outcome model. See family for details of family functions. The default family is gaussian.
sigma_M	the variance of the error term in the mediator model M~S+X
sigma_Y	the variance of the error term in the outcome model Y~S+M+X

Value

A list with the following components:

S	exposure
M	mediator
Y	outcome
X	confounder

Examples

out <- generate_all_data(n = 200)
summary(out$S)
summary(out$M)
summary(out$Y)
summary(out$X)

abima documentation built on Oct. 26, 2024, 1:08 a.m.