| run_method {simulator} | R Documentation |
Run one or more methods on simulated data.
Description
Given a Method object or list of Method objects,
this function runs the method(s) on the draws passed through object.
The output of each method is saved to file.
Usage
run_method(object, methods, out_loc = "out", parallel = NULL)
Arguments
object |
an object of class |
methods |
a list of |
out_loc |
(optional) a length-1 character vector that gives location (relative to model's path) that method outputs are stored.This can be useful for staying organized when multiple simulations are based on the same Model and Draws objects. |
parallel |
either |
Details
This function creates objects of class Output and saves each to
file (at dir/model_name/<out_loc>/r<index>_<method_name>.Rdata. If parallel
is not NULL, then it must be a list containing socket_names, which can
either be a positive integer specifying the number of copies to run on
localhost or else a character vector of machine names (e.g.,
"mycluster-0-0"). The list parallel can also contain
libraries, a character vector of R packages that will be needed on the
slaves and save_locally, a logical that indicates whether the files
generated should be saved on the slaves (i.e., locally) or on the master.
Before running each method on index i, the RNG state is restored to what it
was at the end of calling simulate_from_model on this index.
This is only relevant for randomized methods. The choice to do this ensures
that one will get identical results regardless of the order in which methods
and indices are run in. When ExtendedMethod objects are
passed, these are run after all Method objects have been run. This is
because each ExtendedMethod object depends on the output of its base
method. Furthermore, before an ExtendedMethod is called, the RNG
state is restored to what it was after the base method had been called.
See Also
generate_model simulate_from_model
Examples
## Not run:
# suppose previously we had run the following:
sim <- new_simulation(name = "normal-example",
label = "Normal Mean Estimation",
dir = tempdir()) %>%
generate_model(make_my_example_model, n = 20) %>%
simulate_from_model(nsim = 50, index = 1:3)
# then we could add
sim <- run_method(sim, my_example_method)
## End(Not run)