This is the main high-level wrapper function used to run
a set of ss3sim simulations.
The data frame passed to simdf is parsed into a list and used to control
ss3sim_base(). Alternatively, you can call ss3sim_base() directly
with your own lists.
run_ss3sim(
iterations,
simdf = NULL,
parallel = FALSE,
parallel_iterations = FALSE,
...
)A numeric vector specifying which iterations are desired.
For example 1:100.
The same number of iterations will be ran for each scenario.
If any iterations already have a folder from a previous run, they will be
skipped even if they do not contain viable results.
A data frame of instructions with one row per scenario.
See setup_scenarios_defaults() for default values that will be
used for a generic simulation to get you started.
These default values will only work with the stored cod model because
some of the columns in simdf need to have values that
match the fleet structure of the operating model. If you are not using the
default cod model, please remember to add om_dir and em_dir columns
to simdf with file paths to the locations of your operating model and
estimation method. Essentially, simdf is a way to pass scenario-specific
information to the arguments of ss3sim_base(), whereas the ... method
will only work for things like seed that are universal to all scenarios
in a simulation.
A logical argument that controls whether the scenarios are
run in parallel. You will need to register multiple cores first with a
package such as doParallel and have the foreach package
installed. For example, the following code will register two cores
and must be called before running run_ss3sim():
A logical argument specifying if you wish to
run iterations in parallel. If you set parallel = TRUE and
parallel_iterations = TRUE then iterations for a given scenario will
be sent to multiple processors. All iterations for a given scenario must
finish before the next scenario is started.
This will be useful if you want to run one scenario fast or if you want
to be able to look at the results for each scenario as they finish in
another instance of R. The argument will be ignored if parallel = FALSE.
Anything else to pass to ss3sim_base(). This could
include bias_adjust. Also, you can pass
additional options to the executable through the argument
extras.
The output will appear in your current R working directory.
Folders will be named based on the "scenario" column
of simdf or based on the date-time stamp
(i.e., mmddhhmmss) generated automatically at the start of the simulation.
The resulting folders will look like the following if you run
your simulation at noon on January 01:
0101120000/1/om
0101120000/1/em
0101120000/2/om
...
The operating model folder, which is passed as a file path
using simdf[["om_dir"]], should contain the following files:
forecast.ss,
yourmodel.ctl,
yourmodel.dat,
ss.par, and
starter.ss.
The files should be the formatted versions that are returned from
Stock Synthesis after the model is optimized, i.e., .ss_new files.
It is important to use these formatted files because many functions in
ss3sim and r4ss depend on the location of keywords present
in the comments and other standardized formatting. Once you have these
files from a successfully optimized model, rename the .ss_new files
to match the names listed above, though you can change yourmodel to
whatever name is listed for the control and data files in starter.ss.
The estimation model folder should also contain these files, except
ss.par and yourmodel.dat files, which are unnecessary.
See the vignette titled modifying-models for details on modifying an
existing Stock Synthesis model to run with ss3sim. Alternatively,
consider modifying the built-in model configuration based on north sea cod.
Note that due to the way that Stock Synthesis is being used as an OM, you may see the following error from ADMB in the console: Error – base = 0 in function prevariable& pow(const prevariable& v1, CGNU_DOUBLE u) However, this is not a problem because ADMB is not used to optimize the OM, and thus, the error can safely be ignored.
ss3sim_base() can be called directly by passing lists to each
individual argument rather than using the data-frame approach
of run_ss3sim(simdf = ).
The lists correspond to each function called by ss3sim_base().
Each element is itself a list of arguments for the given function.
Either way allows users to pass arguments to each of the
change_*() or sample_*() functions.
Note that if you do not include an argument,
then ss3sim_base() will assume the value of that argument is NULL.
if (FALSE) { # \dontrun{
# A run with deterministic process error for model checking
# by passing user_recdevs to ss3sim_base through run_ss3sim:
recdevs_det <- matrix(0, nrow = 101, ncol = 2)
df <- data.frame(setup_scenarios_defaults(),
"scenarios" = "determinate"
)
run_ss3sim(
iterations = 1:2, simdf = df,
bias_adjust = FALSE, user_recdevs = recdevs_det
)
get_results_all(user_scenarios = "determinate", overwrite = TRUE)
ts <- utils::read.csv("ss3sim_ts.csv")
expect_equivalent(
unlist(ts$rec_dev[ts$year %in% 1:10 & ts$iteration == 2]),
recdevs_det[1:10, 2]
)
} # }