SimShiny: Generate a basic Monte Carlo simulation GUI template

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Generate a basic Monte Carlo simulation GUI template

Description

This function generates suitable stand-alone code from the shiny package to create simple web-interfaces for performing single condition Monte Carlo simulations. The template generated is relatively minimalistic, but allows the user to quickly and easily edit the saved files to customize the associated shiny elements as they see fit.

Usage

SimShiny(filename = NULL, dir = getwd(), design, ...)

Arguments

filename	an optional name of a text file to save the server and UI components (e.g., 'mysimGUI.R'). If omitted, the code will be printed to the R console instead
dir	the directory to write the files to. Default is the working directory
design	design object from runSimulation
...	arguments to be passed to runSimulation. Note that the design object is not used directly, and instead provides options to be selected in the GUI

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")}

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

See Also

runSimulation

Examples

## Not run: 

Design <- createDesign(sample_size = c(30, 60, 90, 120),
                       group_size_ratio = c(1, 4, 8),
                       standard_deviation_ratio = c(.5, 1, 2))

Generate <- function(condition, fixed_objects) {
    N <- condition$sample_size
    grs <- condition$group_size_ratio
    sd <- condition$standard_deviation_ratio
    if(grs < 1){
        N2 <- N / (1/grs + 1)
        N1 <- N - N2
    } else {
        N1 <- N / (grs + 1)
        N2 <- N - N1
    }
    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=sd)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)), DV = c(group1, group2))
    dat
}

Analyse <- function(condition, dat, fixed_objects) {
    welch <- t.test(DV ~ group, dat)
    ind <- t.test(DV ~ group, dat, var.equal=TRUE)

    # In this function the p values for the t-tests are returned,
    #  and make sure to name each element, for future reference
    ret <- c(welch = welch$p.value, independent = ind$p.value)
    ret
}

Summarise <- function(condition, results, fixed_objects) {
    #find results of interest here (e.g., alpha < .1, .05, .01)
    ret <- EDR(results, alpha = .05)
    ret
}

# test that it works
# Final <- runSimulation(design=Design, replications=5,
#                       generate=Generate, analyse=Analyse, summarise=Summarise)

# print code to console
SimShiny(design=Design, generate=Generate, analyse=Analyse,
         summarise=Summarise, verbose=FALSE)

# save shiny code to file
SimShiny('app.R', design=Design, generate=Generate, analyse=Analyse,
         summarise=Summarise, verbose=FALSE)

# run the application
shiny::runApp()
shiny::runApp(launch.browser = TRUE) # in web-browser


## End(Not run)

SimDesign documentation built on Sept. 11, 2024, 8 p.m.