% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Simulations-methods.R
\docType{methods}
\name{plot,PseudoDualSimulationsSummary,missing-method}
\alias{plot,PseudoDualSimulationsSummary,missing-method}
\title{Plot the summary of Pseudo Dual Simulations summary}
\usage{
\S4method{plot}{PseudoDualSimulationsSummary,missing}(x, y, type = c("nObs",
  "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit", "meanEffFit"),
  ...)
}
\arguments{
\item{x}{the \code{\linkS4class{PseudoDualSimulationsSummary}} object we want to plot from}

\item{y}{missing}

\item{type}{the types of plots you want to obtain.}

\item{\dots}{not used}
}
\value{
A single \code{\link[ggplot2]{ggplot}} object if a single plot is
asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
}
\description{
This plot method can be applied to \code{\linkS4class{PseudoDualSimulationsSummary}} objects in order
to summarize them graphically. Possible \code{type} of plots at the moment are those listed in
\code{\link{plot,PseudoSimulationsSummary,missing-method}} plus: 
\describe{\item{meanEffFit}{Plot showing the fitted dose-efficacy curve. If no samples are involved, only the
average fitted dose-efficacy curve across the trials will be ploted. If samples (DLE and efficacy) are involved, 
the average fitted dose-efficacy curve across the trials, together with the 95\% credibility interval; and comparison 
with the assumed truth (as specified by the \code{trueEff} argument to 
\code{\link{summary,PseudoDualSimulations-method}})}}
You can specify any subset of these in the \code{type} argument.
}
\examples{
##obtain the plot of the summary for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200\% allowable increase for dose below 300 and 200\% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (for illustration)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 ## this would need to be increased in the real
                 ## application:
                 mcmcOptions=McmcOptions(burnin=10, step=1, samples=50),
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)

##Then plot the summary of the simulations
print(plot(MYSUM))



##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueNu=1/0.025,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# 
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))



##OR if the 'EffFlexi' class is used 
## for the efficacy model

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##The simulations
# ##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(object=design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueSigma2=0.025,
#                 trueSigma2betaW=1,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))
}
\keyword{methods}

