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

Source: R/reSummarise.R

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.

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.

Note that if the simulation contained only one row then the new summarise function can be defined as either summarise <- function(results, fixed_objects), if fixed_objects is required, or summarise <- function(results),

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

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. 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. doi:10.1080/10691898.2016.1246953

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


Design <- createDesign(N = c(10, 20, 30))

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

Analyse <- function(condition, dat, fixed_objects) {
    ret <- c(mean=mean(dat), median=median(dat)) # mean/median of sample data
    ret
}

Summarise <- function(condition, results, fixed_objects){
    colMeans(results)
}

if (FALSE) { # \dontrun{
# 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){
    ret <- c(mean_ests=colMeans(results), SE=colSDs(results))
    ret
}

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

SimClean(dir='simresults/')

} # }

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

res <- runSimulation(design=Design, replications=50, store_results = TRUE,
                     generate=Generate, analyse=Analyse, summarise=Summarise)
#> 
#> 
Design: 1/3;   Replications: 50;   RAM Used: 160.8 Mb;   Total Time: 0.00s 
#>  Conditions: N=10
#> 

#> 
Design: 2/3;   Replications: 50;   RAM Used: 160.8 Mb;   Total Time: 0.01s 
#>  Conditions: N=20
#> 

#> 
Design: 3/3;   Replications: 50;   RAM Used: 160.9 Mb;   Total Time: 0.02s 
#>  Conditions: N=30
#> 

#> 
#> Simulation complete. Total execution time: 0.03s
res
#> # A tibble: 3 × 8
#>       N    mean  median REPLICATIONS SIM_TIME RAM_USED       SEED COMPLETED     
#>   <dbl>   <dbl>   <dbl>        <dbl> <chr>    <chr>         <int> <chr>         
#> 1    10 10.511  10.533            50 0.01s    160.8 Mb  273992574 Fri Jun 20 18…
#> 2    20  9.9258  9.9572           50 0.01s    160.9 Mb 1935411040 Fri Jun 20 18…
#> 3    30 10.121  10.126            50 0.01s    160.9 Mb 1053697725 Fri Jun 20 18…

# same summarise but with bootstrapping
res2 <- reSummarise(Summarise, results = res, boot_method = 'basic')
res2
#> # A tibble: 3 × 7
#>       N  mean median BOOT_mean_2.5 BOOT_mean_97.5 BOOT_median_2.5
#>   <dbl> <dbl>  <dbl>         <dbl>          <dbl>           <dbl>
#> 1    10 10.5   10.5          10.1            11.0           10.1 
#> 2    20  9.93   9.96          9.64           10.2            9.61
#> 3    30 10.1   10.1           9.85           10.4            9.75
#> # ℹ 1 more variable: BOOT_median_97.5 <dbl>