run_sim_rstar_glm: Run Multiple Iterations of Simulation and Summarize Results

Description

This function runs multiple iterations of simulation for the sim_rstar_glm function and summarizes the results, including rejection rates, bias, empirical standard error, mean squared error, and root mean squared error.

Usage

run_sim_rstar_glm(
  n_sims,
  alpha_level = 0.05,
  n_main,
  n_covariates,
  true_coef_main,
  n_control = NULL,
  true_coef_control = NULL,
  treatment_effect = NULL,
  model = c("logistic", "linear", "poisson"),
  skewness_main = NULL,
  skewness_control = NULL,
  Sigma_main = NULL,
  Sigma_control = NULL,
  ...
)

Value

A list with the results of each simulation and a summary of the results.

Arguments

n_sims: Number of simulations to run.
alpha_level: Significance level for hypothesis tests.
n_main: Number of observations in the main group.
n_covariates: Number of covariates.
true_coef_main: True coefficients for the main group.
n_control: Number of observations in the control group.
true_coef_control: True coefficients for the control group.
treatment_effect: Treatment effect size.
model: Type of model: "logistic", "linear", or "poisson".
skewness_main: Skewness for the main group covariates.
skewness_control: Skewness for the control group covariates.
Sigma_main: Covariance matrix for the main group covariates.
Sigma_control: Covariance matrix for the control group covariates.
...: Additional arguments passed to sim_rstar_glm.

References

Pierce, D. A., & Bellio, R. (2017). Modern Likelihood-Frequentist Inference. International Statistical Review / Revue Internationale de Statistique, 85(3), 519–541. doi:10.1111/insr.12232

Bellio R, Pierce D (2020). likelihoodAsy: Functions for Likelihood Asymptotics. R package version 0.51, https://CRAN.R-project.org/package=likelihoodAsy.

Examples

Run this code

sim_summary <- run_sim_rstar_glm(
  n_sims = 2, alpha_level = 0.05,
  n_main = 100, n_covariates = 2, true_coef_main = c(0.5, -0.3),
  n_control = 100, true_coef_control = c(0.2, -0.1),
  treatment_effect = 1, model = "linear"
) |> suppressWarnings()

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