mcmc  R Documentation 
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.
mcmc(data, model, options, ...) ## S4 method for signature 'GeneralData,GeneralModel,McmcOptions' mcmc( data, model, options, program = c("JAGS"), verbose = FALSE, fromPrior = data@nObs == 0L, ... ) ## S4 method for signature 'DataMixture,GeneralModel,McmcOptions' mcmc( data, model, options, fromPrior = data@nObs == 0L & data@nObsshare == 0L, ... ) ## 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, ...)
data 
The data input, an object of class 
model 
The model input, an object of class 
options 
MCMC options, an object of class

... 
unused 
program 
the program which shall be used: currently only “JAGS” is supported 
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. 
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.
The posterior samples, an object of class
Samples
.
mcmc(data = GeneralData, model = GeneralModel, options = McmcOptions)
: Standard method which uses JAGS
mcmc(data = DataMixture, model = GeneralModel, options = McmcOptions)
: Method for DataMixture with different fromPrior default
mcmc(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.
mcmc(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
mcmc(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 inversegamma 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.
# 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),placebo=FALSE) ## 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,c=0) ## 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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