SimDesign (version 2.2)

reSummarise: Run a summarise step for results that have been saved to the hard drive

Description

When runSimulation() uses the option save_results = TRUE the R replication results from the Generate-Analyse functions are stored to the hard drive. As such, additional summarise components may be required at a later time, whereby the respective .rds files must be read back into R to be summarised. This function performs the reading of these files, application of a provided summarise function, and final collection of the respective results.

Usage

reSummarise(
  summarise,
  dir = NULL,
  files = NULL,
  fixed_objects = NULL,
  boot_method = "none",
  boot_draws = 1000L,
  CI = 0.95
)

Arguments

summarise

a summarise function to apply to the read-in files. See runSimulation for details

dir

directory pointing to the .rds files to be read-in that were saved from runSimulation(..., save_results=TRUE). If NULL, it is assumed the current working directory contains the .rds files

files

(optional) names of files to read-in. If NULL all files located within dir will be used

fixed_objects

(optional) see runSimulation for details

boot_method

method for performing non-parametric bootstrap confidence intervals for the respective meta-statistics computed by the Summarise function. See runSimulation for details

boot_draws

number of non-parametric bootstrap draws to sample for the summarise function after the generate-analyse replications are collected. Default is 1000

CI

bootstrap confidence interval level (default is 95%)

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. 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. 10.1080/10691898.2016.1246953

Examples

# NOT RUN {
# }
# NOT RUN {
Design <- data.frame(N = c(10, 20, 30))

Generate <- function(condition, fixed_objects = NULL) {
    dat <- with(condition, rnorm(N, 10, 5)) # distributed N(10, 5)
    dat
}

Analyse <- function(condition, dat, fixed_objects = NULL) {
    ret <- mean(dat) # mean of the sample data vector
    ret
}

\dontrun{
# run the simulation
runSimulation(design=Design, replications=50,
              generate=Generate, analyse=Analyse,
              summarise=NA, save_results=TRUE,
              save_details = list(save_results_dirname='simresults'))

Summarise <- function(condition, results, fixed_objects = NULL){
    ret <- c(mu=mean(results), SE=sd(results))
    ret
}

SimClean('SimDesign-results.rds')

res <- reSummarise(Summarise, dir = 'simresults/')
res

Summarise2 <- function(condition, results, fixed_objects = NULL) {
    mean(results)
}

res2 <- reSummarise(Summarise2, dir = 'simresults/')
res2

SimClean('simresults/')
}
# }