Simulate outcomes from a rule-based design
Simulate outcomes from a rule-based design
## S4 method for signature 'RuleDesign' simulate(object, nsim = 1L, seed = NULL, truth, args = NULL, parallel = FALSE, nCores = min(parallel::detectCores(), 5), ...)
object |
the |
nsim |
the number of simulations (default: 1) |
seed |
see |
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 |
data frame with arguments for the |
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 |
an object of class GeneralSimulations
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.