Perform multiple simulations from a dataframe containing parameters

Usage

iterate_batch_runs(
  batch_run_df,
  input_model_object,
  pred_model = FALSE,
  wt_based_dosing = FALSE,
  wt_name = "WT",
  ev_df,
  model_dur = FALSE,
  model_rate = FALSE,
  sampling_times,
  divide_by = 1,
  debug = FALSE,
  append_id_text = "m1-",
  show_progress = TRUE,
  gradient = FALSE,
  parallel_sim = FALSE,
  parallel_n = 200
)

Arguments

batch_run_df: Input batch run dataframe containing "Name", "Reference", "Lower", "Upper"
input_model_object: mrgmod object
pred_model: Default FALSE. set to TRUE to set ev_df CMT to 0
wt_based_dosing: When TRUE, will try to use weight-based dosing
wt_name: Name of weight parameter in model to multiply dose amt by
ev_df: ev() dataframe containing dosing info
model_dur: Default FALSE, set to TRUE to model duration inside the code
model_rate: Default FALSE, set to TRUE to model rate inside the code
sampling_times: A vector of sampling times (note: not a tgrid object)
divide_by: Divide the TIME by this value, used for scaling x-axis
debug: When TRUE, outputs debugging messages
append_id_text: A string prefix to be inserted for each ID
show_progress: When TRUE, shows shiny progress messages
gradient: When TRUE, perform gradient runs in between lower/upper
parallel_sim: Default TRUE, uses the future and mrgsim.parallel packages !Not implemented live!
parallel_n: The number of subjects required before parallelization is used !Not implemented live!

Value

a df mrgsolve output totaling 2 * params + 1 (reference) runs, or 8 * params + 1 if gradient option is used