dispatch_trial_runs: Simulate single trial after setting seed
In adaptr: Adaptive Trial Simulator

dispatch_trial_runs

R Documentation

Simulate single trial after setting seed

Description

Helper function to dispatch the running of several trials to lapply() or parallel::parLapply(), setting seeds correctly if a base_seed was used when calling run_trials(). Used internally in calls by the run_trials() function.

Usage

dispatch_trial_runs(is, trial_spec, seeds, sparse, cores, cl = NULL)

Arguments

`is`	vector of integers, the simulation numbers/indices.
`trial_spec`	trial specification as provided by `setup_trial()`, `setup_trial_binom()` or `setup_trial_norm()`.
`sparse`	single logical, as described in `run_trial()`; defaults to `TRUE` when running multiple simulations, in which case only the data necessary to summarise all simulations are saved for each simulation. If `FALSE`, more detailed data for each simulation is saved, allowing more detailed printing of individual trial results and plotting using `plot_history()` (`plot_status()` does not require non-sparse results).
`cores`	`NULL` or single integer. If `NULL`, a default value/cluster set by `setup_cluster()` will be used to control whether simulations are run in parallel on a default cluster or sequentially in the main process; if a cluster/value has not been specified by `setup_cluster()`, `cores` will then be set to the value stored in the global `"mc.cores"` option (if previously set by `⁠options(mc.cores = <number of cores>⁠`), and `1` if that option has not been specified. If the resulting number of `cores = 1`, computations will be run sequentially in the primary process, and if `cores > 1`, a new parallel cluster will be setup using the `parallel` library and removed once the function completes. See `setup_cluster()` for details.
`cl`	`NULL` (default) for running sequentially, otherwise a `parallel` cluster for parallel computation if `cores > 1`.