dgp_dicp: Simple data-generating process for illustrating tramicp

In tramicp: Model-Based Causal Feature Selection for General Response Types

Simple data-generating process for illustrating tramicp

Description

Simple data-generating process for illustrating tramicp

Usage

dgp_dicp(
  n = 1000,
  K = 6,
  nenv = 2,
  bx3 = stats::rnorm(1),
  ge = stats::rnorm(nenv),
  ae = stats::rnorm(nenv),
  mod = "polr",
  interacting = FALSE,
  rm_censoring = TRUE,
  cfb = c(-3, 1.35),
  cfx = stats::rnorm(2),
  bx2x1 = stats::rnorm(1)
)

Arguments

n	Sample size
K	Number of outcome classes or order of Bernstein polynomial
nenv	Number of environments
bx3	Effect of Y on X3
ge	Environment specific effect
ae	Environment specific effect
mod	Type of model
interacting	Toggle baseline interaction with env
rm_censoring	Remove censoring from simulated responses
cfb	Baseline coefs
cfx	Shift coefs
bx2x1	coef from x2 to x1

Details

Simulates from X2 -> X1 -> Y -> X3, with E affecting X1, X2, X3, but not Y.

Value

data.frame with simulated data

tramicp documentation built on April 4, 2025, 3:25 a.m.