Obtain posterior samples for all model parameters

Description

This is the function to actually run the MCMC machinery to produce posterior samples from all model parameters and required derived values. It is a generic function, so that customized versions may be conveniently defined for specific subclasses of GeneralData, GeneralModel, and McmcOptions input.

Usage

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mcmc(data, model, options, ...)

## S4 method for signature 'GeneralData,GeneralModel,McmcOptions'
mcmc(data, model, options,
  program = c("JAGS", "OpenBUGS", "WinBUGS"), verbose = FALSE,
  fromPrior = data@nObs == 0L, ...)

## S4 method for signature 'Data,LogisticNormal,McmcOptions'
mcmc(data, model, options,
  verbose = FALSE, ...)

## S4 method for signature 'Data,LogisticIndepBeta,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,Effloglog,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,EffFlexi,McmcOptions'
mcmc(data, model, options, ...)

Arguments

data

The data input, an object of class GeneralData

model

The model input, an object of class GeneralModel

options

MCMC options, an object of class McmcOptions

program

the program which shall be used: either “JAGS” (default), “OpenBUGS” or “WinBUGS”

verbose

shall progress bar and messages be printed? (not default)

fromPrior

sample from the prior only? Defaults to checking if nObs is 0. For some models it might be necessary to specify it manually here though.

...

unused

Details

Reproducible samples can be obtained by setting the seed via set.seed before in the user code as usual. However, note that because the RNG sampler used is external to R, running this MCMC function will not change the seed position – that is, the repeated call to this function will then result in exactly the same output.

Value

The posterior samples, an object of class Samples.

Methods (by class)

  • data = GeneralData,model = GeneralModel,options = McmcOptions: Standard method which uses JAGS/BUGS

  • data = Data,model = LogisticNormal,options = McmcOptions: The fast method for the LogisticNormal class

  • data = Data,model = LogisticIndepBeta,options = McmcOptions: Obtain posterior samples for the model parameters based on the pseudo 'LogisticsIndepBeta' DLE model. The joint prior and posterior probability density function of the intercept φ_1 (phi1) and the slope φ_2 (phi2) are given in Whitehead and Williamson (1998) and TsuTakawa (1975). However, since asymptotically, the joint posterior probability density will be bivariate normal and we will use the bivariate normal distribution to generate posterior samples of the intercept and the slope parameters. For the prior samples of of the intercept and the slope a bivariate normal distribution with mean and the covariance matrix given in Whitehead and Williamson (1998) is used.

  • data = DataDual,model = Effloglog,options = McmcOptions: Obtain the posterior samples for the model parameters in the Efficacy log log model. Given the value of ν, the precision of the efficacy responses, the joint prior or the posterior probability of the intercept θ_1 (theta1) and the slope θ_2 (theta2) is a bivariate normal distribtuion. The ν (nu), the precision of the efficacy responses is either a fixed value or has a gamma distribution. If a gamma distribution is used, the samples of nu will be first generated. Then the mean of the of the nu samples will be used the generate samples of the intercept and slope parameters of the model

  • data = DataDual,model = EffFlexi,options = McmcOptions: Obtain the posterior samples for the estimates in the Efficacy Flexible form. This is the mcmc procedure based on what is described in Lang and Brezger (2004) such that samples of the mean efficacy responses at all dose levels, samples of sigma2 sigma^2, the variance of the efficacy response and samples of sigma2betaW sigma^2_{beta_W}, the variance of the random walk model will be generated. Please refer to Lang and Brezger (2004) for the procedures and the form of the joint prior and posterior probability density for the mean efficay responses. In addition, both sigma2 and sigma2betaW acan be fixed or having an inverse-gamma prior and posterior distribution. Therefore, if the inverse gamma distribution(s) are used, the parameters in the distribution will be first updated and then samples of sigma2 and sigma2betaW will be generated using the updated parameters.

Examples

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# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

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


# Sample from the posterior distribution
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

samples <- mcmc(data = myData, model = model, options=options)



# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

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


# Sample from the posterior distribution
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

samples <- mcmc(data = myData, model = model, options=options)


##obtain mcmc DLE samples given the data, LogisticIndepBeta (DLE model) and mcmc simulations options
## data must be of 'Data' class
data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
##obtain mcmc efficacy samples given the data, 'Effloglog' model (efficacy model) and
## mcmc simulations options data must be of '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))
## model must be of 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)
##obtain mcmc efficacy samples given the data, 'EffFlexi' model (efficacy model) and 
## mcmc simulations options
## data must be of '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))
## model must be of 'EffFlexi' 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)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)

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