plot,SimulationsSummary,missing-method | R Documentation |
Graphical display of the simulation summary
## S4 method for signature 'SimulationsSummary,missing'
plot(
x,
y,
type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit"),
...
)
x |
the |
y |
missing |
type |
the types of plots you want to obtain. |
... |
not used |
This plot method can be applied to SimulationsSummary
objects in order to summarize them graphically. Possible type
of
plots at the moment are those listed in
plot,GeneralSimulationsSummary,missing-method
plus:
Plot showing the average fitted dose-toxicity curve across
the trials, together with 95% credible intervals, and comparison with the
assumed truth (as specified by the truth
argument to
summary,Simulations-method
)
You can specify any subset of these in the type
argument.
A single ggplot
object if a single plot is
asked for, otherwise a gridExtra{gTree}
object.
# nolint start
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
# Initialize the CRM model
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov =
matrix(c(1, -0.5, -0.5, 1),
nrow = 2
),
ref_dose = 56
)
# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
intervals = c(0, 30),
cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
intervals = c(0, 1),
cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)
# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
target = c(0.2, 0.35),
prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
# Initialize the design
design <- Design(
model = model,
nextBest = myNextBest,
stopping = myStopping,
increments = myIncrements,
cohort_size = mySize,
data = emptydata,
startingDose = 3
)
## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)
# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
burnin = 10,
step = 1,
samples = 100
)
time <- system.time(mySims <- simulate(design,
args = NULL,
truth = myTruth,
nsim = 1,
seed = 819,
mcmcOptions = options,
parallel = FALSE
))[3]
# Plot the Summary of the Simulations
plot(summary(mySims, truth = myTruth))
# nolint end
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