plot,PseudoSimulationsSummary,missing-method | R Documentation |

Graphical display of the simulation summary

## S4 method for signature 'PseudoSimulationsSummary,missing' plot( x, y, type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit"), ... )

`x` |
the |

`y` |
missing |

`type` |
the types of plots you want to obtain. |

`...` |
not used |

This plot method can be applied to `PseudoSimulationsSummary`

objects in order to summarize them graphically. This can be used when only DLE responses are involved
in the simulations. This also applied to results with or without samples generated during the simulations

A single `ggplot`

object if a single plot is
asked for, otherwise a `gridExtra{gTree}`

object.

##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 <- function(dose) { model@prob(dose, 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) ##Then produce a summary of your simulations MYSUM <- summary(mySim, truth=myTruth) ##plot the summary of the simulations print(plot(MYSUM)) ##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 options<-McmcOptions(burnin=100,step=2,samples=200) ##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) # ##Then produce a summary of your simulations # MYSUM <- summary(mySim, # truth=myTruth) # ##plot the summary of the simulations # print(plot(MYSUM))

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