sim.tdlmm {dlmtree}

Creates simulated data for TDLM & TDLMM

Description

Method for creating simulated data for TDLM & TDLMM

Usage

sim.tdlmm(
  sim = "A",
  n = 5000,
  error = 10,
  mean.p = 0.5,
  prop.active = 0.05,
  expList = NULL,
  r = 1
)

Arguments

sim

character (A - F) specifying simulation scenario

n

sample size for simulation

error

positive scalar specifying error variance for Gaussian response

mean.p

scalar between zero and one specifying mean probability for simulation scenario A

prop.active

proportion of active exposures for simulation scenario C

expList

named list of exposure data

r

dispersion parameter of negative binomial distribution

Details

sim.tdlmm

Simulation scenarios:

• Scenario A: Binary response with single exposure effect

• Scenario B: Continuous response with main effect of PM2.5 and interaction

• Scenario C: Continuous response to test exposure selection using exposure

• Scenario D: Continuous response to test exposure selection using one exposure of main effect and two interaction effects among four exposures

• Scenario E: Zero-inflated count response with single exposure effect

• Scenario F: Zero-inflated count response with single exposure effect with main effect of PM2.5 and interaction

Value

Simulated data and true parameters

Examples

sim.tdlmm(sim = "A", mean.p = 0.5, n = 1000)