run_facts {rfacts} | R Documentation |
Run FACTS
Description
Run FACTS trial simulations.
Usage
run_facts(
facts_file,
output_path = tempfile(),
log_path = output_path,
n_burn = NULL,
n_mcmc = NULL,
n_weeks_files = 10000,
n_patients_files = 10000,
n_mcmc_files = 0,
n_mcmc_thin = NULL,
flfll_seed = NULL,
flfll_offset = NULL,
n_sims,
...
)
Arguments
facts_file |
Character, name of a FACTS file.
Usually has a |
output_path |
Character, directory path to the files to generate. |
log_path |
Character, path to the log file generated by FLFLL. |
n_burn |
Number of burn-in iterations for the MCMC. |
n_mcmc |
Number of MCMC iterations used in inference. |
n_weeks_files |
Number of |
n_patients_files |
Number of |
n_mcmc_files |
Number of |
n_mcmc_thin |
Number of thinning iterations for the MCMC. |
flfll_seed |
Positive integer, random number generator seed for FLFLL.
This seed is only used for stochastic preprocessing steps for generating
the |
flfll_offset |
Integer, offset for the random number generator. |
n_sims |
Positive integer, number of simulations per param file. |
... |
Named arguments to the appropriate FACTS engine function.
Use |
Details
run_facts()
calls run_flfll()
and then run_engine()
.
For finer control over trial simulation, you can call these
latter two functions individually.
Value
Character, path to the directory with FACTS output.
See Also
run_flfll()
, run_engine()
, get_facts_engine()
Examples
# Can only run if system dependencies are configured:
if (file.exists(Sys.getenv("RFACTS_PATHS"))) {
facts_file <- get_facts_file_example("contin.facts") # example FACTS file
out <- run_facts(
facts_file,
n_sims = 4,
verbose = FALSE
)
# What results files do we have?
head(get_csv_files(out))
# Read all the "patients*.csv" files with `read_patients(out)`.
# For each scenario, we have files named
# patients00001.csv, patients00002.csv, patients00003.csv,
# and patients00004.csv.
read_patients(out)
}