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Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, and a given efficacy pseudo model

Usage

plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

# S4 method for class 'ModelTox,Samples,ModelEff,Samples'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

# S4 method for class 'ModelTox,missing,ModelEff,missing'
plotGain(DLEmodel, Effmodel, data, size = c(8L, 8L), shape = c(16L, 17L), ...)

Arguments

DLEmodel

the dose-DLE model of ModelTox class object

DLEsamples

the DLE sample of Samples class object

Effmodel

the dose-efficacy model of ModelEff class object

Effsamples

the efficacy sample of of Samples class object

data

the data input of DataDual class object

...

not used

size

(integer)
a vector of length two defining the sizes of the shapes used to identify the doses with, respectively, p(DLE = 0.3) and the maximum gain

shape

(integer)
a vector of length two defining the shapes used to identify the doses with, respectively, p(DLE = 0.3) and the maximum gain

Value

This returns the ggplot object for the plot

Functions

  • plotGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples ): Standard method

  • plotGain( DLEmodel = ModelTox, DLEsamples = missing, Effmodel = ModelEff, Effsamples = missing ): Standard method

Examples

# nolint start

## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
#> Used default patient IDs!
#> Used best guess cohort indices!
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(eff=c(1.223,2.513),eff_dose=c(25,300),nu=c(a=1,b=0.025),data=data,const=0)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)
#> Used default patient IDs!
#> Used best guess cohort indices!

##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)


DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
##plot the three curves of mean values of the DLEsamples, Effsamples and
##gain value samples (obtained within this plotGain function) at all dose levels
plotGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
         Effmodel=Effmodel,Effsamples=Effsamples,
         data=data)

# nolint end
# nolint start
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
#> Used default patient IDs!
#> Used best guess cohort indices!
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(eff=c(1.223,2.513),eff_dose=c(25,300),nu=c(a=1,b=0.025),data=data)
##plot the three curves of using modal estimates of model parameters at all dose levels
plotGain(DLEmodel=DLEmodel,
         Effmodel=Effmodel,
         data=data)

# nolint end