Execute a visual predictive check (VPC) simulation using mrgsolve

df_mrgsim_replicate() is a wrapper function for mrgsolve::mrgsim_df() that returns a data.frame containing replicates iterations of data

df_mrgsim_replicate(
  data,
  model,
  replicates,
  time_vars = c(TIME = "TIME", NTIME = "NTIME"),
  output_vars = c(PRED = "PRED", IPRED = "IPRED", DV = "DV"),
  num_vars = NULL,
  char_vars = NULL,
  irep_name = "SIM",
  seed = 123456789,
  ...
)

Arguments

data

Input dataset. Must contain required variables for mrgsim_df() other than those handled by other arguments.

model

mrgsolve model object.

replicates

Number of replicates. Either an integer, or something coercible to an integer.

time_vars

Names of actual and nominal time variables. Must be named character vector. Defaults are "TIME" and "NTIME".

output_vars

Names of model outputs from model. Must be named character vector. Defaults are "PRED", "IPRED", and "DV". Currently both "IPRED" and "DV" are required in model@capture.

num_vars

Numeric variables in data or simulation output to recover. Must be a character vector of variable names from the simulation output to carry_out and return in output. Defaults are "CMT", "EVID", "MDV", "NTIME".

char_vars

Character variables in data or simulation output to recover. Must be a character vector of variable names from the simulation output to recover and return in output.

irep_name

Name of replicate variable in data. Must be a string. Default is "SIM".

seed

Random seed. Default is 123456789.

...

Additional arguments passed to mrgsolve::mrgsim_df().

Value

A data.frame with data x replicates rows (unless obsonly=TRUE) and the output variables in output_vars, num_vars, and char_vars.

Examples

model <- model_mread_load(model = "model")
#> Loading model from cache.
simout <- df_mrgsim_replicate(data = data_sad, model = model, replicates = 100,
output_vars = c(DV = "ODV"),
num_vars = c("CMT", "LLOQ", "EVID", "MDV", "WTBL", "FOOD"),
char_vars = c("USUBJID", "PART"),
irep_name = "SIM")