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

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

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,
  results = NULL,
  Design = NULL,
  fixed_objects = NULL,
  boot_method = "none",
  boot_draws = 1000L,
  CI = 0.95,
  prefix = "results-row"
)

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
results	(optional) the results of runSimulation when no summarise function was provided. Can be either a tibble or matrix (indicating that exactly one design condition was evaluated), or a list of matrix/tibble objects indicating that multiple conditions were performed with no summarise evaluation. Alternatively, if store_results = TRUE in the runSimulation() execution then the final SimDesign object may be passed, where the generate-analyse information will be extracted from the object instead
Design	(optional) if results input used, and design condition information important in the summarise step, then the original design object from runSimulation should be included
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%)
prefix	character indicating prefix used for stored files

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")}

Examples

Design <- createDesign(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 <- c(mean=mean(dat), median=median(dat)) # mean/median of sample data
    ret
}

Summarise <- function(condition, results, fixed_objects = NULL){
    apply(results, 2, mean)
}

## Not run: 
# run the simulation
runSimulation(design=Design, replications=50,
              generate=Generate, analyse=Analyse,
              summarise=Summarise, save_results=TRUE,
              save_details = list(save_results_dirname='simresults'))


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

Summarise2 <- function(condition, results, fixed_objects = NULL){
    ret <- c(mean_ests=apply(results, 2, mean),
             SE=apply(results, 2, sd))
    ret
}

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

SimClean('simresults/')


## End(Not run)

###
# Similar, but with results stored within the final object

res <- runSimulation(design=Design, replications=50, store_results = TRUE,
                     generate=Generate, analyse=Analyse, summarise=Summarise)
res

# same summarise but with bootstrapping
res2 <- reSummarise(Summarise, results = res, boot_method = 'basic')
res2

SimDesign documentation built on Oct. 17, 2023, 5:07 p.m.