Append Input Parameters to persimon_app Simulation Results

Description

This internal function combines simulation results with success rates and input parameters. It prepares the data for storage in the database by appending all relevant information from a single simulation run.

Usage

appendInputParams(df_results, df_success, input)

Arguments

Details

This function performs several steps:

1. Appends '_success' to the column names of df_success (except n1, n2, n3).

2. Combines df_results with the modified df_success.

3. Generates a unique run code for the simulation.

4. Creates a data frame of input parameters.

5. Repeats the input parameters to match the number of rows in the results.

6. Combines all the above into a single data frame.

Value

A data frame combining simulation results, success rates, and input parameters. The returned data frame includes:

• All columns from df_results

• Success rate columns from df_success (appended with '_success')

• Input parameters (M1, S1, M2, S2, M3, S3, Sk1, Sk2, Sk3, n_simulations, nboot, conf_level)

• A unique RunCode for the simulation

Nonparametric Bootstrap Test with Pooled Resampling for Informative Hypothesis Testing

Description

Performs a nonparametric bootstrap test with pooled resampling based on the methods described by Dwivedi et al. (2017) and using the restriktor::iht function as proposed by Vanbrabant and Rosseel (2020).

Usage

bootFbar(
  data,
  formula,
  grp,
  constraints,
  nboot = 1000,
  conf.level = 0.95,
  seed = NULL,
  na_rm = FALSE
)

Arguments

Value

A list containing the following components:

-pvalueA, pvalueB, pvalueF: P-values for the different test types.

-ciA, ciB, ciF: Confidence intervals for the test statistics.

-TsA, TsB, TsF: Bootstrap test statistics.

-modelo: The linear model object.

-ihto: The initial iht computation result.

References

Dwivedi, A. K., Mallawaarachchi, I., & Alvarado, L. A. (2017). Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Statistics in Medicine, 36(14), 2187–2205. https://doi.org/10.1002/sim.7263

Vanbrabant, L., & Rosseel, Y. (2020). An Introduction to Restriktor: Evaluating informative hypotheses for linear models. In R. van de Schoot & M. Miocevic (Eds.), Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (1st ed., pp. 157 -172). Routledge. https://doi.org/10.4324/9780429273872-14

Examples

bootFbar(data = iris, formula = Sepal.Length ~ -1 + Species,
         grp = "Species",
         constraints = 'Speciessetosa < Speciesversicolor < Speciesvirginica',
         nboot = 10, conf.level = 0.95, seed = NULL, na_rm = FALSE)

Internal Bootstrap Resampling for F-bar Test

Description

This internal function performs bootstrap resampling specific to the F-bar test within the context of the nonparametric bootstrap test with pooled resampling. It is used by bootFbar to generate bootstrap samples and compute intermediate statistics.

Usage

bootstrap_fbar_sample(
  data,
  formula,
  grp,
  constraints,
  grp_val,
  grp_sizes,
  pre_calc,
  only_equality_constraints
)

Arguments

Details

The function creates bootstrap samples for each group in the data, then binds these samples together. It applies the restriktor::iht function on these samples to compute the test statistics. Error handling is implemented to manage potential issues in iht computation.

Value

A list containing the test statistics for each constraint type (B, A, F) based on the bootstrap sample. Returns NA values if the iht computation fails.

Generate UI Parameters for persimon_app Simulation

Description

This internal function creates a list of 'shiny' input elements for the simulation parameters. It defines the UI components for means, standard deviations, skewness, sample sizes, number of simulations, number of bootstrap iterations, and confidence level.

Usage

getUIParams()

Details

This function is used internally by the 'shiny' app to generate the UI elements for parameter input.

Value

A list of 'shiny' input elements including:

Performance Simulation On Type I Error or Statistical Power

Description

Simulates performance (Type I Error or Statistical Power) for a range of statistical tests, including bootFbar, default setting restriktor::iht, and parametric bootstrap restriktor::iht. The function is set up to replicate the table structure in Dwivedi et al. (2017) Supplemental Tables 2 & 3.

Usage

persimon(
  M1 = 5,
  S1 = 1,
  M2 = 5,
  S2 = 1,
  M3 = 5,
  S3 = 1,
  Sk1 = NULL,
  Sk2 = NULL,
  Sk3 = NULL,
  n1 = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 15),
  n2 = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 15),
  n3 = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 15),
  n_simulations = 10000,
  nboot = 1000,
  conf.level = 0.95
)

Arguments

Details

The persimon function generates data for three groups with specified mean, standard deviation, and skewness, and then applies a range of statistical tests to this data, simulating the process across a specified number of iterations to assess performance in terms of Type I Error or Statistical Power. The output mirrors the format of the supplemental tables in Dwivedi et al. (2017).

Value

A list of data frames. One with the proportions of rejecting the null hypothesis for each test and sample size combination, and the other with the number of models which did not produce errors for each combination.

References

Dwivedi, A. K., Mallawaarachchi, I., & Alvarado, L. A. (2017). Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Statistics in Medicine, 36(14), 2187–2205. https://doi.org/10.1002/sim.7263

Examples

set.seed(135)
persimon(M1 = 5, S1 = 1, M2 = 5, S2 = 1, M3 = 5, S3 = 1,
         n1 = 6, n2 = 6, n3 = 6, n_simulations = 2, nboot = 4,
         conf.level = 0.95)

Run Shiny Application for bootFbar Performance Simulation

Description

This function launches a 'shiny' application that allows users to run performance simulations for the bootFbar method and other statistical tests. The app provides an interface to set simulation parameters, run simulations, view results, and store them in a 'PostgreSQL' database.

Usage

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

Arguments

Details

The 'shiny' application provides a user interface for setting simulation parameters, running simulations using the persimon function, and visualizing the results. It allows users to:

• Set parameters for the simulation (means, standard deviations, sample sizes, etc.)

• Run simulations with the specified parameters

• View simulation results and success rates in interactive tables

• Store simulation results in a PostgreSQL database

• Download stored results from the database

• View relevant citations for the methods used

The application uses the mmints package for database interactions and citation handling.

Value

A 'shiny' application object.

See Also

persimon for the underlying simulation function.

Examples

if(interactive()){
persimon_app(dbname = "my_database", datatable = "simulation_results",
             host = "localhost", port = 5432,
             user = "username", password = "password")
}

Run Persimon Simulation with User Inputs

Description

This internal function executes the persimon simulation using parameters provided by the user through the persimon_app 'shiny' app interface.

Usage

runSimulation(input)

Arguments

Details

This function serves as a wrapper around the persimon function, translating user inputs from the persimon_app 'shiny' app into the format required by persimon. It uses helper functions from the mmints package to process certain inputs.

Value

A list containing two data frames:

See Also

persimon for the underlying simulation function.