Run simulations — run_sim • pharmr.extra

Run simulations

Usage

run_sim(
  fit = NULL,
  data = NULL,
  model = NULL,
  id = irxutils::get_random_id("sim_"),
  force = FALSE,
  t_obs = NULL,
  dictionary = list(ID = "ID", DV = "DV", EVID = "EVID", AMT = "AMT", CMT = "CMT", MDV =
    "MDV"),
  regimen = NULL,
  covariates = NULL,
  tool = c("auto", "nonmem", "nlmixr2"),
  n_subjects = NULL,
  n_iterations = 1,
  variables = c("ID", "TIME", "DV", "EVID", "IPRED", "PRED"),
  add_pk_variables = TRUE,
  output_file = "simtab",
  update_table = TRUE,
  seed = 12345,
  verbose = TRUE
)

Arguments

fit: a Pharmpy modelfit object.
data: dataset (data.frame). Optional, can also be included in model object (if specified as pharmpy model object).
model: either a Pharmpy model object, or a string containing NONMEM model code. If the latter, run_sim() will attempt to load the model into Pharmpy.
id: run id, e.g. run1. This will be the folder in which the NONMEM model is run. If no folder is specified, it will create a folder run1 in the current working directory, and will increment the run number for each subsequent run.
force: if run folder (id) exists, should existing results be removed before rerunning NONMEM? Default FALSE.
t_obs: a vector of observations times. If specified, will override the observations in each subject in the input dataset.
dictionary: a dataset dictionary.
regimen: if specified, will replace the regimens for each subject with a custom regimen. Can be specified in two ways. The simplest way is to just specify a list with elements dose, interval, n, and route (and t_inf / rate for infusions). E.g. regimen = list(dose = 500, interval = 12, n = 5, route = "oral"). Alternatively, regimens can be specified as a data.frame. The data.frame specified all dosing times (dose, time columns) and route and t_inf / rate. The data.frame may also optionally contain a regimen column that specifies a name for the regimen. This can be used to simulate multiple regimens.
covariates: if specified, will replace subjects with subjects specified in a data.frame. In the data.frame, the column names should correspond exactly to any covariates included in the model. An ID column is required, and for time-varying covariates, a TIME column is also required (otherwise it will be assumed covariates are not changing over time).
tool: the tool to run the model in, either nonmem, or nlmixr.
n_subjects: number of subjects to simulate, when using sampled data (i.e. requires covariates argument)
n_iterations: number of iterations of the entire simulation to perform. The dataset for the simulation will stay the same between each iterations.
variables: vector of variables to output.
add_pk_variables: calculate basic PK variables that can be extracted in post-processing, such as CMAX_OBS, TMAX_OBS, AUC_SS.
output_file: TODO
update_table: should any existing $TABLE records be removed, and a new simtab be created? This is default. If FALSE, it will leave $TABLEs as specifed in the model. However, in the return object, only the first table is returned back. If FALSE, the add_pk_variables argument will be ignored.
seed: TODO
verbose: verbose output?

Value

data.frame with simulation results