fit: Fit method for the Samples class
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description Usage Arguments Value Functions Examples

Note this new generic function is necessary because the fitted function only allows the first argument object to appear in the signature. But we need also other arguments in the signature.

fit(object, model, data, ...)

## S4 method for signature 'Samples,Model,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,DualEndpoint,DataDual'
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)

## S4 method for signature 'Samples,LogisticIndepBeta,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,Effloglog,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,EffFlexi,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

`object`	the `Samples` object
`model`	the `Model` object
`data`	the `Data` object
`...`	unused
`points`	at which dose levels is the fit requested? default is the dose grid
`quantiles`	the quantiles to be calculated (default: 0.025 and 0.975)
`middle`	the function for computing the middle point. Default: `mean`

the data frame with required information (see method details)

fit,Samples,Model,Data-method: This method returns a data frame with dose, middle, lower and upper quantiles for the dose-toxicity curve
fit,Samples,DualEndpoint,DataDual-method: This method returns a data frame with dose, and middle, lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix "Biomarker") curves, for all grid points (Note that currently only the grid points can be used, because the DualEndpointRW models only allow that)
fit,Samples,LogisticIndepBeta,Data-method: This method return a data frame with dose, middle lower and upper quantiles for the dose-DLE curve using DLE samples for “LogisticIndepBeta” model class
fit,Samples,Effloglog,DataDual-method: This method returns a data frame with dose, middle, lower, upper quantiles for the dose-efficacy curve using efficacy samples for “Effloglog” model class
fit,Samples,EffFlexi,DataDual-method: This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for “EffFlexi” model class

# nolint start

# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           ref_dose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))


model <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                           DLEweights=c(3,3),
                           DLEdose=c(25,300),
                           data=data)
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data,model,options)

fitted <- fit(object=samples, model=model, data=data)

# nolint end

# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples<-mcmc(data,model,options)

fit(object=samples, model=model,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
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)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
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)
## model must be from 'ModelEff' e.g using 'Effloglog' class
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)

## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)