plot-GeneralSimulations-missing-method: Plot simulations
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description Usage Arguments Details Value Examples

Summarize the simulations with plots

1 2	## S4 method for signature 'GeneralSimulations,missing' plot(x, y, type = c("trajectory", "dosesTried"), ...)

`x`	the `GeneralSimulations` object we want to plot from
`y`	missing
`type`	the type of plots you want to obtain.
`...`	not used

This plot method can be applied to GeneralSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients

You can specify one or both of these in the type argument.

A single ggplot object if a single plot is asked for, otherwise a gridExtra{gTree} object.

# nolint start

##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations

##Specified your simulations when no DLE samples are used
##Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)),
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients=12)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1).
##The simulations
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)


##plot the simulations
print(plot(mySim))



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##specify the design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1).
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
#
# ##plot the simulations
# print(plot(mySim))
#

# nolint end