Launch Shiny App for likelihoodAsy rstar Analysis

Description

This function launches a Shiny application that facilitates the setup and execution of likelihoodAsy rstar analysis. The app allows users to upload a dataset, specify a model and parameters of interest, and perform the analysis with the option to compute confidence intervals for r* statistics.

Usage

LA_rstar()

Value

A Shiny app object that can be run locally.

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

if (interactive()) {
  LA_rstar()
}

Create Diagnostic Plots for Simulation Results

Description

This internal function creates diagnostic plots for the results of the simulation studies, including boxplots for GLM estimates and p-values.

Usage

create_plots(data)

Arguments

Value

A list with ggplot2 objects for the estimate and p-value plots.

Format Citation

Description

This internal function formats a citation object into a readable string. The function extracts relevant information such as the title, author, year, address, and URL from the citation object and formats it into a standardized citation format.

Usage

format_citation(cit)

Arguments

Value

A character string with the formatted citation.

Compute r* Statistics for Generalized Linear Models

Description

The rstar_glm function computes r* statistics for hypothesis testing on coefficients of interest in generalized linear models (GLMs). It supports logistic, linear, and Poisson regression models. For logistic models, the outcome must be binary.

Usage

rstar_glm(
  .formula,
  .data,
  .model = c("logistic", "linear", "poisson"),
  .psidesc = "Coefficient of Interest",
  .psival = 0,
  .fpsi = 2,
  .rstar.ci = FALSE,
  trace = FALSE,
  ...
)

## S3 method for class 'logistic'
rstar_glm(
  .formula,
  .data,
  .model = c("logistic", "linear", "poisson"),
  .psidesc = "Coefficient of Interest",
  .psival = 0,
  .fpsi = 2,
  .rstar.ci = FALSE,
  trace = FALSE,
  ...
)

## S3 method for class 'linear'
rstar_glm(
  .formula,
  .data,
  .model = c("logistic", "linear", "poisson"),
  .psidesc = "Coefficient of Interest",
  .psival = 0,
  .fpsi = 2,
  .rstar.ci = FALSE,
  trace = FALSE,
  ...
)

## S3 method for class 'poisson'
rstar_glm(
  .formula,
  .data,
  .model = c("logistic", "linear", "poisson"),
  .psidesc = "Coefficient of Interest",
  .psival = 0,
  .fpsi = 2,
  .rstar.ci = FALSE,
  trace = FALSE,
  ...
)

## Default S3 method:
rstar_glm(
  .formula,
  .data,
  .model = c("logistic", "linear", "poisson"),
  .psidesc = "Coefficient of Interest",
  .psival = 0,
  .fpsi = 2,
  .rstar.ci = FALSE,
  trace = FALSE,
  ...
)

Arguments

Value

A list with the object returned from likelihoodAsy::rstar (rs), the object returned from likelihoodAsy::rstar.ci (rs_ci), and the object returned from stats::glm (fit_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

# Logistic model
rstar_glm(law ~ DriversKilled + VanKilled + drivers + kms,
          .data = Seatbelts,
          .model = "logistic") |> suppressWarnings()

# Poisson model
rstar_glm(count ~ spray,
          .data = InsectSprays,
          .model = "poisson") |> suppressWarnings()

# Linear model
rstar_glm(mpg ~ wt + hp,
          .data = mtcars,
          .model = "linear") |> suppressWarnings()

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,
  ...
)

Arguments

Value

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

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

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()

Simulate Data and Fit GLM and r* Models

Description

This function generates simulated data for main and control groups, fits a generalized linear model (GLM) and an r* model, and returns the results.

Usage

sim_rstar_glm(
  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,
  ...
)

Arguments

Value

A list with fitted GLM and r* models, and the simulated data.

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

sim_result <- sim_rstar_glm(
  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 = 0.5, model = "logistic"
) |> suppressWarnings()

Shiny App for Running r* GLM Simulations with PostgreSQL Integration

Description

This function launches a Shiny application for setting up and running simulations based on the rstar_glm function. The app allows users to input parameters for the simulation, run the simulation, view results, and save results to a PostgreSQL database.

Usage

sim_rstar_glm_pgsql(dbname, datatable, host, port, user, password)

Arguments

Value

A Shiny app object that can be run locally.

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

if (interactive()) {
  sim_rstar_glm_pgsql(
    dbname = "mydb",
    datatable = "simulation_results",
    host = "localhost",
    port = 5432,
    user = "myuser",
    password = "mypassword"
  )
}