Overview
SimEngine is an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments.
Getting started
The goal of many statistical simulations is to compare the behavior of two or more statistical methods; we use this framework to demonstrate the SimEngine workflow. Most statistical simulations of this type include three basic phases: (1) generate data, (2) run one or more methods using the generated data, and (3) compare the performance of the methods.
To briefly illustrate how these phases are implemented using , we use a simple example of estimating the rate parameter of a distribution. To anchor the simulation in a real-world situation, one can imagine that a sample of size from this Poisson distribution models the number of patients admitted daily to a hospital over the course of consecutive days. Suppose that the data consist of independent and identically distributed observations drawn from a Poisson() distribution. Since the parameter of the Poisson distribution is equal to both the mean and the variance, one may ask whether the sample mean (denoted ) or the sample variance (denoted ) is a better estimator of .
1) Load the package and create a simulation object
After loading the package, the first step is to create a simulation
object (an R object of class sim_obj) using the
new_sim()
function. The simulation object contains all
data, functions, and results related to the simulation.
2) Code a function to generate data
Many simulations involve a function that creates a dataset designed
to mimic a real-world data-generating mechanism. Here, we write and test
a simple function to generate a sample of n
observations
from a Poisson distribution with
.
3) Code the methods (or other functions)
With SimEngine, any functions declared (or loaded
via source()
) are automatically stored in the simulation
object when the simulation runs. In this example, we test the sample
mean and sample variance estimators of the
parameter. For simplicity, we write this as a single function and use
the type
argument to specify which estimator to use.
4) Set the simulation levels
Often, we wish to run the same simulation multiple times. We refer to
each run as a simulation replicate. We may wish to vary certain
features of the simulation between replicates. In this example, perhaps
we choose to vary the sample size and the estimator used to estimate
.
We refer to the features that vary as simulation levels; in the
example below, the simulation levels are the sample size
(n
) and the estimator (estimator
). We refer to
the values that each simulation level can take on as level
values; in the example below, the n
level values are
10
, 100
, and 1000
, and the
estimator
level values are "M"
(for “sample
mean”) and "V"
(for “sample variance”). By default,
SimEngine runs one simulation replicate for each
combination of level values — in this case, six combinations — although
the user will typically want to increase this; 1,000 or 10,000
replicates per combination is typical.
sim %<>% set_levels(
estimator = c("M", "V"),
n = c(10, 100, 1000)
)
Note that we make extensive use of the pipe operators
(%>%
and %<>%
) from the
magrittr package; if you have never used pipes, see the
magrittr documentation.
5) Create a simulation script
The simulation script is a user-written function that assembles the
pieces above (generating data, analyzing the data, and returning
results) to code the flow of a single simulation replicate. Within a
script, the current simulation level values can be referenced using the
special variable L
. For instance, in the running example,
when the first simulation replicate is running, L$estimator
will equal "M"
and L$n
will equal
10
. In the next replicate, L$estimator
will
equal "M"
and L$n
will equal 100
,
and so on, until all level value combinations are run. The simulation
script will automatically have access to any functions or objects that
have been declared in the global environment.
sim %<>% set_script(function() {
dat <- create_data(n=L$n)
lambda_hat <- est_lambda(dat=dat, type=L$estimator)
return (list("lambda_hat"=lambda_hat))
})
The simulation script should always return a list containing one or
more key-value pairs, where the keys are syntactically valid names. The
values may be simple data types (numbers, character strings, or boolean
values) or more complex data types (lists, dataframes, model objects,
etc.); see the Advanced Usage documentation for how to handle complex
data types. Note that in this example, the estimators could have been
coded instead as two different functions and then called from within the
script using the use_method()
function.
6) Set the simulation configuration
The set_config()
function controls options related to
the entire simulation, such as the number of simulation replicates to
run for each level value combination and the parallelization type, if
desired (see the Parallelization documentation). Packages needed for the
simulation should be specified using the packages
argument
of set_config()
(rather than using library()
or require()
). We set num_sim
to 100, and so
SimEngine will run a total of 600 simulation replicates
(100 for each of the six level value combinations).
sim %<>% set_config(
num_sim = 100,
packages = c("ggplot2", "stringr")
)
#>
#> Attaching package: 'ggplot2'
#> The following object is masked from 'package:SimEngine':
#>
#> vars
7) Run the simulation
All 600 replicates are run at once and results are stored in the simulation object.
8) View and summarize results
Once the simulation replicates have finished running, the
summarize()
function can be used to calculate common
summary statistics, such as bias, variance, mean squared error (MSE),
and confidence interval coverage.
sim %>% summarize(
list(stat="bias", name="bias_lambda", estimate="lambda_hat", truth=20),
list(stat="mse", name="mse_lambda", estimate="lambda_hat", truth=20)
)
#> level_id estimator n n_reps bias_lambda mse_lambda
#> 1 1 M 10 100 -0.0170000 2.02670000
#> 2 2 V 10 100 2.0398889 147.39792963
#> 3 3 M 100 100 0.0358000 0.25405400
#> 4 4 V 100 100 0.2552697 7.69693198
#> 5 5 M 1000 100 0.0000200 0.01927814
#> 6 6 V 1000 100 -0.1457773 0.69827047
In this example, we see that the MSE of the sample variance is much
higher than that of the sample mean and that MSE decreases with
increasing sample size for both estimators, as expected. From the
n_reps
column, we see that 100 replicates were successfully
run for each level value combination. Results for individual simulation
replicates can also be directly accessed via the
sim$results
dataframe.
head(sim$results)
#> sim_uid level_id rep_id estimator n runtime lambda_hat
#> 1 1 1 1 M 10 0.0004301071 21.2
#> 2 7 1 2 M 10 0.0002913475 19.8
#> 3 8 1 3 M 10 0.0002927780 19.0
#> 4 9 1 4 M 10 0.0002868176 22.3
#> 5 10 1 5 M 10 0.0002839565 21.2
#> 6 11 1 6 M 10 0.0002782345 20.0
Above, the sim_uid
uniquely identifies a single
simulation replicate and the level_id
uniquely identifies a
level value combination. The rep_id
is unique within a
given level value combination and identifies the index of that replicate
within the level value combination. The runtime
column
shows the runtime of each replicate (in seconds).
9) Update a simulation
After running a simulation, a user may want to update it by adding
additional level values or replicates; this can be done with the
update_sim()
function. Prior to running
update_sim()
, the functions set_levels()
and/or set_config()
are used to declare the updates that
should be performed. For example, the following code sets the total
number of replicates to 200 (i.e., adding 100 replicates to those that
have already been run) for each level value combination, and adds one
additional level value for n
.
sim %<>% set_config(num_sim = 200)
sim %<>% set_levels(
estimator = c("M", "V"),
n = c(10, 100, 1000, 10000)
)
After the levels and/or configuration are updated,
update_sim()
is called.
sim %<>% update_sim()
#> Done. No errors or warnings detected.
Another call to summarize()
shows that the additional
replicates were successfully:
sim %>% summarize(
list(stat="bias", name="bias_lambda", estimate="lambda_hat", truth=20),
list(stat="mse", name="mse_lambda", estimate="lambda_hat", truth=20)
)
#> level_id estimator n n_reps bias_lambda mse_lambda
#> 1 1 M 10 200 -0.01500000 1.92640000
#> 2 2 V 10 200 0.75911111 118.55581235
#> 3 3 M 100 200 0.02630000 0.22649900
#> 4 4 V 100 200 -0.04132424 6.64234842
#> 5 5 M 1000 200 -0.00612000 0.02069753
#> 6 6 V 1000 200 -0.07187527 0.66624237
#> 7 7 M 10000 200 0.00080950 0.00198466
#> 8 8 V 10000 200 -0.01427990 0.08795799