run_nm | R Documentation |
Run nm objects. Uses system_nm()
to submit the cmd()
value of object.
run_nm(
m,
threads = Inf,
ignore.stdout = FALSE,
quiet = getOption("quiet_run"),
intern = getOption("intern"),
force = FALSE,
cache_ignore_cmd = FALSE,
cache_ignore_ctl = FALSE,
cache_ignore_data = FALSE
)
m |
An nm object. |
threads |
Numeric. Number of jobs to run concurrently (default =
|
ignore.stdout |
Logical (default= |
quiet |
Logical (default= |
intern |
Logical. |
force |
Logical (default = |
cache_ignore_cmd |
Logical (default = |
cache_ignore_ctl |
Logical (default = |
cache_ignore_data |
Logical (default = |
In grid environment it is recommended to run nm_tran()
via the
RStudio 'Addin' prior to executing this code.
By default, when highlighting code and evaluating it via an RStudio app,
run_nm()
will not execute and will just return the nm
object.
For vector nm
objects of length more than 1, all runs will be launched at
the same time with a gap of getOption("job_time_spacing")
seconds (default = 0).
This could overwhelm resources if not in a grid environment.
run_nm
is a variant of run_nm_single()
containing a threads
argument
that will submit run_nm()
's in batches and wait for them to complete. If
you need all the runs to complete ensure you use a wait_finish()
statement afterwards as R console will only be blocked for until the last
batch has been submitted which will be before all runs have completed.
The job_time_spacing
argument
m
with job_info
fields populated.
nm_tran()
## requires NONMEM to be installed
## Not run:
m1 <- new_nm(
run_id = "m1",
based_on = "staging/Models/ADVAN2.mod",
data_path = "DerivedData/data.csv"
) %>%
cmd("execute {ctl_name} -dir={run_dir}") %>%
fill_input() %>%
run_nm()
## End(Not run)
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