Simulate outcomes from a rule-based design

Usage

# S4 method for class 'RuleDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object: the RuleDesign object we want to simulate data from
nsim: the number of simulations (default: 1)
seed: see set_seed
truth: a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.
args: data frame with arguments for the truth function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.
parallel: should the simulation runs be parallelized across the clusters of the computer? (not default)
nCores: how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
...: not used

Value

an object of class GeneralSimulations

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = emptydata,
  startingDose = 5
)

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Perform the simulation
## For illustration purpose only 10 simulation is produced (nsim=10).
threeSims <- simulate(myDesign,
  nsim = 10,
  seed = 35,
  truth = myTruth,
  parallel = FALSE
)

# nolint end