plot,GeneralSimulations,missing-method | R Documentation |
Summarize the simulations with plots
## S4 method for signature 'GeneralSimulations,missing' plot(x, y, type = c("trajectory", "dosesTried"), ...)
x |
the |
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 type
s of
plots at the moment are:
Summary of the trajectory of the simulated trials
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.
##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) ##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)) #
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.