mcmc {crmPack} | R Documentation |
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
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, ...)
Arguments
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. |
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
.
Functions
-
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\phi_1
(phi1) and the slope\phi_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\nu
, the precision of the efficacy responses, the joint prior or the posterior probability of the intercept\theta_1
(theta1) and the slope\theta_2
(theta2) is a bivariate normal distribtuion. The\nu
(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 sigma2sigma^2
, the variance of the efficacy response and samples of sigma2betaWsigma^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
# 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)