# Run a summarise step for results that have been saved to the hard drive 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. ```r 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 ## Examples ```r 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) } ## 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){ ret <- c(mean_ests=colMeans(results), SE=colSDs(results)) ret } res2 <- reSummarise(Summarise2, dir = 'simresults/') res2 SimClean(dir='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 ``` ## 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. 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. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953") ## Author(s) Phil Chalmers rphilip.chalmers@gmail.com

reSummarise function