crmPack (version 1.0.6)

simulate,RuleDesign-method: Simulate outcomes from a rule-based design

Description

Simulate outcomes from a rule-based design

Usage

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

Value

an object of class GeneralSimulations

Arguments

object

the RuleDesign object we want to simulate data from

nsim

the number of simulations (default: 1)

seed

see setSeed

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

Examples

# 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(),
                       cohortSize = 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),
                           refDose = 50)
  
## define the true function
myTruth <- function(dose)
{
  model@prob(dose, 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)