reSummarise | R Documentation |
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"
)
summarise |
a summarise function to apply to the read-in files.
See |
dir |
directory pointing to the .rds files to be
read-in that were saved from |
files |
(optional) names of files to read-in. If |
results |
(optional) the results of Alternatively, if |
Design |
(optional) if |
fixed_objects |
(optional) see |
boot_method |
method for performing non-parametric bootstrap confidence intervals
for the respective meta-statistics computed by the |
boot_draws |
number of non-parametric bootstrap draws to sample for the |
CI |
bootstrap confidence interval level (default is 95%) |
prefix |
character indicating prefix used for stored files |
Phil Chalmers rphilip.chalmers@gmail.com
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")}
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
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