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This function runs a Monte Carlo simulation study given a set of predefined simulation functions,
design conditions, and number of replications. Results can be saved as temporary files in case of interruptions
and may be restored by re-running runSimulation, provided that the respective temp
file can be found in the working directory. runSimulation supports parallel
and cluster computing, global and local debugging, error handling (including fail-safe
stopping when functions fail too often, even across nodes), provides bootstrap estimates of the
sampling variability (optional), and automatic tracking of error and warning messages and their associated
.Random.seed states.
For convenience, all functions available in the R work-space are exported across all computational nodes
so that they are more easily accessible (however, other R objects are not, and therefore
must be passed to the fixed_objects input to become available across nodes).
For an in-depth tutorial of the package please refer to Chalmers and Adkins (2020;
10.20982/tqmp.16.4.p248).
For an earlier didactic presentation of the package refer to Sigal and Chalmers
(2016; 10.1080/10691898.2016.1246953). Finally, see the associated
wiki on Github (https://github.com/philchalmers/SimDesign/wiki)
for tutorial material, examples, and applications of SimDesign to real-world simulation experiments.
runSimulation(
design,
replications,
generate,
analyse,
summarise,
fixed_objects = NULL,
packages = NULL,
filename = NULL,
debug = "none",
load_seed = NULL,
save_results = FALSE,
parallel = FALSE,
ncores = parallel::detectCores(),
cl = NULL,
notification = NULL,
boot_method = "none",
boot_draws = 1000L,
CI = 0.95,
seed = rint(nrow(design), min = 1L, max = 2147483647L),
save_seeds = FALSE,
save = TRUE,
store_results = FALSE,
store_warning_seeds = FALSE,
warnings_as_errors = FALSE,
max_errors = 50L,
allow_na = FALSE,
allow_nan = FALSE,
stop_on_fatal = FALSE,
MPI = FALSE,
save_details = list(),
progress = TRUE,
verbose = TRUE
)# S3 method for SimDesign
summary(object, ...)
# S3 method for SimDesign
print(x, list2char = TRUE, ...)
a tibble or data.frame object containing the Monte Carlo simulation
conditions to be studied, where each row represents a unique condition and each column a factor
to be varied. See createDesign for the standard approach
to create this simulation design object
number of independent replications to perform per condition (i.e., each row in design).
Must be greater than 0
user-defined data and parameter generating function.
See Generate for details. Note that this argument may be omitted by the
user if they wish to generate the data with the analyse step, but for real-world
simulations this is generally not recommended
optional (but highly recommended) user-defined summary function from Summarise
to be used to compute meta-statistical summary information after all the replications have completed within
each design condition.
Omitting this function will return a list of data.frames (or a single data.frame, if only one row in
design is supplied) or, for more general objects (such as lists), a list
containing the results returned form Analyse.
Alternatively, the value NA can be passed to let the generate-analyse-summarise process to run as usual,
where the summarise components are instead included only as a placeholder.
Omitting this input is only recommended for didactic purposes because it leaves out a large amount of
information (e.g., try-errors, warning messages, saving files, etc), can witness memory related issues,
and generally is not as flexible internally.
If users do not wish to supply a summarise function then it
is is recommended to pass NA to this argument while also supplying passing save_results = TRUE to
save the results to the hard-drive during the simulation. This provides a
more RAM friendly alternative to storing all the Generate-Analyse results in the working environment, where
the Analysis results can be summarised at a later time
(optional) an object (usually a named list)
containing additional user-defined objects
that should remain fixed across conditions. This is useful when including
large vectors/matrices of population parameters, fixed data information
that should be used across all conditions and replications (e.g., including a common design matrix
for linear regression models), or simply control constant global elements (e.g., a constant for sample size)
a character vector of external packages to be used during the simulation (e.g.,
c('MASS', 'extraDistr', 'simsem') ). Use this input when parallel = TRUE or
MPI = TRUE to use non-standard functions from additional packages,
otherwise the functions must be made available by using explicit
library or require calls within the provided simulation functions.
Alternatively, functions can be called explicitly without attaching the package with the :: operator
(e.g., extraDistr::rgumbel())
(optional) the name of the .rds file to save the final simulation results to. If the extension
.rds is not included in the file name (e.g. "mysimulation" versus "mysimulation.rds") then the
.rds extension will be automatically added to the file name to ensure the file extension is correct.
Note that if the same file name already exists in the working
directly at the time of saving then a new
file will be generated instead and a warning will be thrown. This helps to avoid accidentally overwriting
existing files. Default is NULL, indicating no file will be saved by default
a string indicating where to initiate a browser() call for editing and debugging, and pairs
particularly well with the load_seed argument for precice debugging.
General options are 'none' (default; no debugging), 'error', which starts the debugger
when any error in the code is detected in one of three generate-analyse-summarise functions,
and 'all', which debugs all the user defined functions regardless of whether an error was thrown
or not. Specific options include: 'generate'
to debug the data simulation function, 'analyse' to debug the computational function, and
'summarise' to debug the aggregation function.
Alternatively, users may place browser calls within the respective functions for
debugging at specific lines, which is useful when debugging based on conditional evaluations (e.g.,
if(this == 'problem') browser()). Note that parallel computation flags
will automatically be disabled when a browser() is detected or when a debugging argument other than
'none' is supplied
used to replicate an exact simulation state, which is primarily useful for debugging purposes.
Input can be a character object indicating which file to load from when the .Random.seeds have
be saved (after a call with save_seeds = TRUE), or an integer vector indicating the actual
.Random.seed values. E.g., load_seed = 'design-row-2/seed-1'
will load the first seed in the second row of the design input, or explicitly passing the
elements from .Random.seed (see SimExtract to extract the seeds associated explicitly
with errors during the simulation, where each column represents a unique seed).
If the input is a character vector then it is important NOT
to modify the design input object, otherwise the path may not point to the correct saved location, while
if the input is an integer vector (or single column tbl object)
then it WILL be important to modify the design input in order to load this
exact seed for the corresponding design row. Default is NULL
logical; save the results returned from Analyse to external
.rds files located in the defined save_results_dirname directory/folder?
Use this if you would like to keep track of the individual parameters returned from the analysis function.
Each saved object will contain a list of three elements containing the condition (row from design),
results (as a list or matrix), and try-errors.
See SimResults for an example of how to read these .rds files back into R
after the simulation is complete. Default is FALSE.
WARNING: saving results to your hard-drive can fill up space very quickly for larger simulations. Be sure to
test this option using a smaller number of replications before the full Monte Carlo simulation is performed.
See also reSummarise for applying summarise functions from saved
simulation results
logical; use parallel processing from the parallel package over each
unique condition?
number of cores to be used in parallel execution. Default uses all available
cluster object defined by makeCluster used to run code in parallel.
If NULL and parallel = TRUE, a local cluster object will be defined which
selects the maximum number cores available
and will be stopped when the simulation is complete. Note that supplying a cl
object will automatically set the parallel argument to TRUE
an optional, empty argument function to be executed upon completion of the simulation. This can be used, for
instance, to trigger email or SMS notifications that indicate the simulation has been completed. For example,
to utilize the RPushbullet package (and assuming users have previously a) registered for a Pushbullet account,
and b) installed the application on their mobile device and computer), use the following:
Prior to defining notification, load the RPushbullet library via library(RPushbullet). If
this is the first time you have used the package then a suitable rpushbullet.json file will not exist on your computer,
and you'll need to supply a suitable token and the devise to push the notification to via the pbSetup() setup
Supply a definition of notification that utilizes the pbPost function. E.g.,
runSimulation(...,
notification = function() pbPost(type = "note", title = "SimDesign", body = "Simulation Complete"))
Alternatively, if users wish to have an email sent upon completion then the following template that uses the sendmailR
package could be used:
sendmailRrunSimulation(...,
notification = function() sendmailR::sendmail(from="<sendmailR@your.computer>",
to="<your.email@address>", subject="SimDesign", msg="Simulation Complete",
control=list(smtpServer="ASPMX.L.GOOGLE.COM"))).
However, note that this may be less reliable since the email message could be directed to a spam folder.
method for performing non-parametric bootstrap confidence intervals
for the respective meta-statistics computed by the Summarise function.
Can be 'basic' for the empirical bootstrap CI, 'percentile'
for percentile CIs, 'norm' for normal approximations CIs, or 'studentized'
for Studentized CIs (should only be used for simulations with lower replications due to its
computational intensity). Default is 'none', which performs no bootstrapping
number of non-parametric bootstrap draws to sample for the summarise
function after the generate-analyse replications are collected. Default is 1000
bootstrap confidence interval level (default is 95%)
a vector of integers to be used for reproducibility.
The length of the vector must be equal the number of rows in design.
This argument calls set.seed or
clusterSetRNGStream for each condition, respectively,
but will not be run when MPI = TRUE.
Default randomly generates seeds within the range 1 to 2147483647 for each condition.
logical; save the .Random.seed states prior to performing each replication into
plain text files located in the defined save_seeds_dirname directory/folder?
Use this if you would like to keep track of every simulation state within each replication and design
condition. This can be used to completely replicate any cell in the simulation if need be.
As well, see the load_seed input
to load a given .Random.seed to exactly replicate the generated data and analysis state (mostly useful
for debugging). When TRUE, temporary files will also be saved
to the working directory (in the same way as when save = TRUE). Default is FALSE
Note, however, that this option is not typically necessary or recommended since the .Random.seed states for simulation
replications that throw errors during the execution are automatically stored within the final simulation
object, and can be extracted and investigated using SimExtract. Hence, this option is only of
interest when all of the replications must be reproducible (which occurs very rarely),
otherwise the defaults to runSimulation should be sufficient
logical; save the temporary simulation state to the hard-drive? This is useful
for simulations which require an extended amount of time, though for shorter simulations
can be disabled to slightly improve computational efficiency. When TRUE, a temp file
will be created in the working directory which allows the simulation state to be saved
and recovered (in case of power outages, crashes, etc). As well, triggering this flag will
save any fatal .Random.seed states when conditions unexpectedly crash (where each seed
is stored row-wise in an external .rds file), which provides a much easier mechanism
to debug issues (see load_seed for details). Upon completion, this temp file will be removed.
To recover your simulation at the last known location (having patched the issues in the previous execution code)
simply re-run the code you used to
initially define the simulation and the external file will automatically be detected and read-in.
Default is TRUE
logical; store the complete tables of simulation results
in the returned object? This is FALSE by default to help avoid RAM
issues (see save_results as a more suitable alternative). However, if the Design
object is omitted from the call to runSimulation(), or the number of rows in Design
is exactly 1, then this argument is automatically set to TRUE as RAM storage will no
longer be an issue.
To extract these results
pass the returned object to SimExtract(..., what = 'results'), which will return a named list
of all the simulation results for each condition if nrow(Design) > 1; otherwise, if
nrow(Design) == 1 or Design was missing the results object will be stored as-is
logical; in addition to storing the .Random.seed states whenever error messages
are raised, also store the .Random.seed states when warnings are raised? This is disabled by default
since warnings are generally less problematic than errors, and because many more warnings messages may be raised
throughout the simulation (potentially causing RAM related issues when constructing the final simulation object as
any given simulation replicate could generate numerous warnings, and storing the seeds states could add up quickly).
Set this to TRUE when replicating warning messages is important, however be aware that too many warnings
messages raised during the simulation implementation could cause RAM related issues.
logical; treat warning messages as error messages during the simulation? Default is FALSE, therefore warnings are only collected and not used to restart the data generation step, and the seeds associated with the warning message conditions are not stored within the final simulation object
the simulation will terminate when more than this number of consecutive errors are thrown in any
given condition, causing the simulation to continue to the next unique design condition.
This is included to avoid getting stuck in infinite re-draws, and to indicate that something fatally problematic
is going wrong in the generate-analyse phases. Default is 50
logical; should NAs be allowed in the analyse step as a valid result from the simulation
analysis? Default is FALSE
logical; should NaNs be allowed in the analyse step as a valid result from the simulation
analysis? Default is FALSE
logical; should the simulation be terminated immediately when
the maximum number of consecutive errors (max_errors) is reached? If FALSE,
the simulation will continue as though errors did not occur, however a column
FATAL_TERMINATION will be included in the resulting object indicating the final
error message observed, and NA placeholders will be placed in all other row-elements. Default is
FALSE
logical; use the foreach package in a form usable by MPI to run simulation
in parallel on a cluster? Default is FALSE
a list pertaining to information regarding how and where files should be saved
when the save or save_results flags are triggered.
safelogical; trigger whether safe-saving should be performed. When TRUE files
will never be overwritten accidentally, and where appropriate the program will either stop or generate
new files with unique names. Default is TRUE
compnamename of the computer running the simulation. Normally this doesn't need
to be modified, but in the event that a manual node breaks down while running a simulation the
results from the temp files may be resumed on another computer by changing the name of the
node to match the broken computer. Default is the result of evaluating unname(Sys.info()['nodename'])
out_rootdirroot directory to save all files to. Default uses the current working directory
save_results_dirnamea string indicating the name of the folder to save
result objects to when save_results = TRUE. If a directory/folder does not exist
in the current working directory then a unique one will be created automatically. Default is
'SimDesign-results_' with the associated compname appended
save_seeds_dirnamea string indicating the name of the folder to save
.Random.seed objects to when save_seeds = TRUE. If a directory/folder does not exist
in the current working directory then one will be created automatically. Default is
'SimDesign-seeds_' with the associated compname appended
logical; display a progress bar (using the pbapply package) for each simulation condition?
This is useful when simulations conditions take a long time to run (see also the notifications argument).
Default is TRUE
logical; print messages to the R console? Default is TRUE
SimDesign object returned from runSimulation
additional arguments
SimDesign object returned from runSimulation
logical; for tibble object re-evaluate list elements
as character vectors for better printing of the levels? Note that this
does not change the original classes of the object, just how they are printed.
Default is TRUE
a tibble from the dplyr package (also of class 'SimDesign')
with the original design conditions in the left-most columns,
simulation results in the middle columns, and additional information in the right-most columns (see below).
The right-most column information for each condition are:
REPLICATIONS to indicate the number of Monte Carlo replications,
SIM_TIME to indicate how long (in seconds) it took to complete
all the Monte Carlo replications for each respective design condition,
COMPLETED to indicate the date in which the given simulation condition completed,
SEED for the integer values in the seed argument, and, if applicable,
ERRORS and WARNINGS which contain counts for the number of error or warning
messages that were caught (if no errors/warnings were observed these columns will be omitted).
Note that to extract the specific error and warnings messages see
SimExtract. Finally,
if boot_method was a valid input other than 'none' then the final right-most columns will contain the labels
BOOT_ followed by the name of the associated meta-statistic defined in summarise() and
and bootstrapped confidence interval location for the meta-statistics.
To conserve RAM, temporary objects (such as data generated across conditions and replications)
are discarded; however, these can be saved to the hard-disk by passing the appropriate flags.
For longer simulations it is recommended to use the save_results flag to write the analysis results
to the hard-drive.
The use of the save_seeds option can be evoked to save R's .Random.seed state to allow
for complete reproducibility of each replication within each condition. These
individual .Random.seed terms can then be read in with the
load_seed input to reproduce the exact simulation state at any given replication. Most often though,
save_seeds is less useful since problematic seeds are automatically stored in the final
simulation object to allow for easier replicability of potentially problematic errors (which incidentally
can be extracted using SimExtract(res, 'error_seeds') and passed to the load_seed argument). Finally,
providing a vector of seeds is also possible to ensure
that each simulation condition is macro reproducible under the single/multi-core method selected.
Finally, when the Monte Carlo simulation is complete
it is recommended to write the results to a hard-drive for safe keeping, particularly with the
filename argument provided (for reasons that are more obvious in the parallel computation
descriptions below). Using the filename argument supplied is safer than using, for instance,
saveRDS directly because files will never accidentally be overwritten,
and instead a new file name will be created when a conflict arises; this type of implementation safety
is prevalent in many locations in the package to help avoid unrecoverable (yet surprisingly common) mistakes
during the process of designing and executing Monte Carlo simulations.
In the event of a computer crash, power outage, etc, if save = TRUE was used (the default)
then the original code used to execute runSimulation() need only be re-run to resume the simulation.
The saved temp file will be read into the function automatically, and the simulation will continue
one the condition where it left off before the simulation state was terminated. If users wish to remove this temporary
simulation state entirely so as to start anew then simply pass SimClean(temp = TRUE) in the R console to remove any
previously saved temporary objects.
When running simulations in parallel (either with parallel = TRUE or MPI = TRUE)
R objects defined in the global environment will generally not be visible across nodes.
Hence, you may see errors such as Error: object 'something' not found if you try to use an object
that is defined in the workspace but is not passed to runSimulation.
To avoid this type or error, simply pass additional objects to the
fixed_objects input (usually it's convenient to supply a named list of these objects).
Fortunately, however, custom functions defined in the global environment are exported across
nodes automatically. This makes it convenient when writing code because custom functions will
always be available across nodes if they are visible in the R workspace. As well, note the
packages input to declare packages which must be loaded via library() in order to make
specific non-standard R functions available across nodes.
The strategy for organizing the Monte Carlo simulation work-flow is to
Define a suitable Design object (a tibble or data.frame)
containing fixed conditional
information about the Monte Carlo simulations. Each row or this design object pertains
to a unique set of simulation to study, while each column the simulation factor under investigation (e.g., sample size,
distribution types, etc). This is often expedited by using the
createDesign function, and if necessary the argument subset
can be used to remove redundant or non-applicable rows
Define the three step functions to generate the data (Generate; see also
https://CRAN.R-project.org/view=Distributions for a list of distributions in R),
analyse the generated data by computing the respective parameter estimates, detection rates,
etc (Analyse), and finally summarise the results across the total
number of replications (Summarise).
Pass the design object and three defined R functions to runSimulation, and declare the
number of replications to perform with the replications input. This function will return a suitable
tibble object with the complete simulation results and execution details
Analyze the output from runSimulation, possibly using ANOVA techniques
(SimAnova) and generating suitable plots and tables
Expressing the above more succinctly, the functions to be called have the following form, with the exact functional arguments listed:
Design <- createDesign(...)Generate <- function(condition, fixed_objects = NULL) {...} Analyse <- function(condition, dat, fixed_objects = NULL) {...} Summarise <- function(condition, results, fixed_objects = NULL) {...} res <- runSimulation(design=Design, replications, generate=Generate,
analyse=Analyse, summarise=Summarise)The condition object above represents a single row from the design object, indicating
a unique Monte Carlo simulation condition. The condition object also contains two
additional elements to help track the simulation's state: an ID variable, indicating
the respective row number in the design object, and a REPLICATION element
indicating the replication iteration number (an integer value between 1 and replication).
This setup allows users to easily locate the rth replication (e.g., REPLICATION == 500)
within the jth row in the simulation design (e.g., ID == 2). The
REPLICATION input is also useful when temporarily saving files to the hard-drive
when calling external command line utilities (see examples on the wiki).
For a template-based version of the work-flow, which is often useful when initially defining a simulation,
use the SimFunctions function. This function will write a template simulation
to one/two files so that modifying the required functions and objects can begin immediately.
This means that users can focus on their Monte Carlo simulation details right away rather
than worrying about the repetitive administrative code-work required to organize the simulation's execution flow.
Finally, examples, presentation files, and tutorials can be found on the package wiki located at https://github.com/philchalmers/SimDesign/wiki.
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
SimFunctions, createDesign,
Generate, Analyse, Summarise,
SimExtract,
reSummarise, SimClean, SimAnova, SimResults,
aggregate_simulations, Attach,
SimShiny
# NOT RUN {
#-------------------------------------------------------------------------------
# Example 1: Sampling distribution of mean
# This example demonstrate some of the simpler uses of SimDesign,
# particularly for classroom settings. The only factor varied in this simulation
# is sample size.
# skeleton functions to be saved and edited
SimFunctions()
#### Step 1 --- Define your conditions under study and create design data.frame
Design <- createDesign(N = c(10, 20, 30))
#~~~~~~~~~~~~~~~~~~~~~~~~
#### Step 2 --- Define generate, analyse, and summarise functions
# help(Generate)
Generate <- function(condition, fixed_objects = NULL) {
dat <- with(condition, rnorm(N, 10, 5)) # distributed N(10, 5)
dat
}
# help(Analyse)
Analyse <- function(condition, dat, fixed_objects = NULL) {
ret <- mean(dat) # mean of the sample data vector
ret
}
# help(Summarise)
Summarise <- function(condition, results, fixed_objects = NULL) {
ret <- c(mu=mean(results), SE=sd(results)) # mean and SD summary of the sample means
ret
}
#~~~~~~~~~~~~~~~~~~~~~~~~
#### Step 3 --- Collect results by looping over the rows in design
# run the simulation
Final <- runSimulation(design=Design, replications=1000,
generate=Generate, analyse=Analyse, summarise=Summarise)
Final
# reproduce exact simulation
Final_rep <- runSimulation(design=Design, replications=1000, seed=Final$SEED,
generate=Generate, analyse=Analyse, summarise=Summarise)
Final_rep
#~~~~~~~~~~~~~~~~~~~~~~~~
#### Extras
# }
# NOT RUN {
# compare SEs estimates to the true SEs from the formula sigma/sqrt(N)
5 / sqrt(Design$N)
# To store the results from the analyse function either
# a) omit a definition of of summarise(), or
# b) pass save_results = TRUE to runSimulation() and read the results in with SimResults()
# Note that the latter method should be adopted for longer simulations
# e.g., the a) approach
res <- runSimulation(design=Design, replications=1000,
generate=Generate, analyse=Analyse)
str(res)
head(res[[1]])
# or b) approach
Final <- runSimulation(design=Design, replications=1000, save_results=TRUE,
generate=Generate, analyse=Analyse, summarise=Summarise)
res <- SimResults(Final)
str(res)
head(res[[1]]$results)
# obtain empirical bootstrapped CIs during an initial run
# the simulation was completed (necessarily requires save_results = TRUE)
res <- runSimulation(design=Design, replications=1000, boot_method = 'basic',
generate=Generate, analyse=Analyse, summarise=Summarise)
res
# alternative bootstrapped CIs that uses saved results via reSummarise().
# Default directory save to:
dirname <- paste0('SimDesign-results_', unname(Sys.info()['nodename']), "/")
res <- reSummarise(summarise=Summarise, dir=dirname, boot_method = 'basic')
res
# remove the saved results from the hard-drive if you no longer want them
SimClean(results = TRUE)
# }
# NOT RUN {
#-------------------------------------------------------------------------------
# Example 2: t-test and Welch test when varying sample size, group sizes, and SDs
# skeleton functions to be saved and edited
SimFunctions()
# }
# NOT RUN {
# in real-world simulations it's often better/easier to save
# these functions directly to your hard-drive with
SimFunctions('my-simulation')
# }
# NOT RUN {
#### Step 1 --- Define your conditions under study and create design data.frame
Design <- createDesign(sample_size = c(30, 60, 90, 120),
group_size_ratio = c(1, 4, 8),
standard_deviation_ratio = c(.5, 1, 2))
Design
#~~~~~~~~~~~~~~~~~~~~~~~~
#### Step 2 --- Define generate, analyse, and summarise functions
Generate <- function(condition, fixed_objects = NULL) {
N <- condition$sample_size # alternatively, could use Attach() to make objects available
grs <- condition$group_size_ratio
sd <- condition$standard_deviation_ratio
if(grs < 1){
N2 <- N / (1/grs + 1)
N1 <- N - N2
} else {
N1 <- N / (grs + 1)
N2 <- N - N1
}
group1 <- rnorm(N1)
group2 <- rnorm(N2, sd=sd)
dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)), DV = c(group1, group2))
dat
}
Analyse <- function(condition, dat, fixed_objects = NULL) {
welch <- t.test(DV ~ group, dat)
ind <- t.test(DV ~ group, dat, var.equal=TRUE)
# In this function the p values for the t-tests are returned,
# and make sure to name each element, for future reference
ret <- c(welch = welch$p.value, independent = ind$p.value)
ret
}
Summarise <- function(condition, results, fixed_objects = NULL) {
#find results of interest here (e.g., alpha < .1, .05, .01)
ret <- EDR(results, alpha = .05)
ret
}
#~~~~~~~~~~~~~~~~~~~~~~~~
#### Step 3 --- Collect results by looping over the rows in design
# first, test to see if it works
res <- runSimulation(design=Design, replications=5,
generate=Generate, analyse=Analyse, summarise=Summarise)
res
# }
# NOT RUN {
# complete run with 1000 replications per condition
res <- runSimulation(design=Design, replications=1000, parallel=TRUE,
generate=Generate, analyse=Analyse, summarise=Summarise)
res
View(res)
## save final results to a file upon completion (not run)
runSimulation(design=Design, replications=1000, parallel=TRUE, filename = 'mysim',
generate=Generate, analyse=Analyse, summarise=Summarise)
## Debug the generate function. See ?browser for help on debugging
## Type help to see available commands (e.g., n, c, where, ...),
## ls() to see what has been defined, and type Q to quit the debugger
runSimulation(design=Design, replications=1000,
generate=Generate, analyse=Analyse, summarise=Summarise,
parallel=TRUE, debug='generate')
## Alternatively, place a browser() within the desired function line to
## jump to a specific location
Summarise <- function(condition, results, fixed_objects = NULL) {
#find results of interest here (e.g., alpha < .1, .05, .01)
browser()
ret <- EDR(results[,nms], alpha = .05)
ret
}
runSimulation(design=Design, replications=1000,
generate=Generate, analyse=Analyse, summarise=Summarise,
parallel=TRUE)
## EXTRA: To run the simulation on a MPI cluster, use the following setup on each node (not run)
# library(doMPI)
# cl <- startMPIcluster()
# registerDoMPI(cl)
# Final <- runSimulation(design=Design, replications=1000, MPI=TRUE,
# generate=Generate, analyse=Analyse, summarise=Summarise)
# saveRDS(Final, 'mysim.rds')
# closeCluster(cl)
# mpi.quit()
## Similarly, run simulation on a network linked via ssh
## (two way ssh key-paired connection must be possible between master and slave nodes)
##
## define IP addresses, including primary IP
# primary <- '192.168.2.20'
# IPs <- list(
# list(host=primary, user='phil', ncore=8),
# list(host='192.168.2.17', user='phil', ncore=8)
# )
# spec <- lapply(IPs, function(IP)
# rep(list(list(host=IP$host, user=IP$user)), IP$ncore))
# spec <- unlist(spec, recursive=FALSE)
#
# cl <- parallel::makeCluster(type='PSOCK', master=primary, spec=spec)
# res <- runSimulation(design=Design, replications=1000, parallel = TRUE,
# generate=Generate, analyse=Analyse, summarise=Summarise, cl=cl)
#~~~~~~~~~~~~~~~~~~~~~~~~
###### Post-analysis: Analyze the results via functions like lm() or SimAnova(), and create
###### tables(dplyr) or plots (ggplot2) to help visualize the results.
###### This is where you get to be a data analyst!
library(dplyr)
res %>% summarise(mean(welch), mean(independent))
res %>% group_by(standard_deviation_ratio, group_size_ratio) %>%
summarise(mean(welch), mean(independent))
# quick ANOVA analysis method with all two-way interactions
SimAnova( ~ (sample_size + group_size_ratio + standard_deviation_ratio)^2, res,
rates = TRUE)
# or more specific ANOVAs
SimAnova(independent ~ (group_size_ratio + standard_deviation_ratio)^2,
res, rates = TRUE)
# make some plots
library(ggplot2)
library(tidyr)
dd <- res %>%
select(group_size_ratio, standard_deviation_ratio, welch, independent) %>%
pivot_longer(cols=c('welch', 'independent'), names_to = 'stats')
dd
ggplot(dd, aes(factor(group_size_ratio), value)) + geom_boxplot() +
geom_abline(intercept=0.05, slope=0, col = 'red') +
geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
facet_wrap(~stats)
ggplot(dd, aes(factor(group_size_ratio), value, fill = factor(standard_deviation_ratio))) +
geom_boxplot() + geom_abline(intercept=0.05, slope=0, col = 'red') +
geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
facet_grid(stats~standard_deviation_ratio) +
theme(legend.position = 'none')
# }
# NOT RUN {
# }
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