- Data.Cur
a non-negative integer vector of each observed current data.
- Data.Hist
a non-negative integer vector of each observed historical data.
- CompStat
a list of four elements that represents the
"compatibility(sufficient) statistics" for \(\lambda\).
Default is NULL
so the fitting will be based on
the data. If the CompStat
is provided then the inputs in
Data.Cur
and Data.Hist
will be ignored.
n0
is the number of observations in the historical data.
mean0
is the sample mean of the historical data.
n1
is the number of observations in the current data.
mean1
is the sample mean of the current data.
- prior
a list of the hyperparameters in the prior for both \(\lambda\) and \(\delta\).
A Gamma distribution is used as the prior of \(\lambda\), and a Beta distribution is used as the prior of \(\delta\).
lambda.shape
is the shape (hyper)parameter in the prior distribution \(Gamma(shape, scale)\) for \(\lambda\).
lambda.scale
is the scale (hyper)parameter in the prior distribution \(Gamma(shape, scale)\) for \(\lambda\).
delta.alpha
is the hyperparameter \(\alpha\) in the prior distribution \(Beta(\alpha, \beta)\) for \(\delta\).
delta.beta
is the hyperparameter \(\beta\) in the prior distribution \(Beta(\alpha, \beta)\) for \(\delta\).
- MCMCmethod
sampling method for \(\delta\) in MCMC. It can be either 'IND' for independence proposal; or 'RW' for
random walk proposal on logit scale.
- rw.logit.delta
the stepsize(variance of the normal distribution) for the random walk proposal of logit \(\delta\).
Only applicable if MCMCmethod = 'RW'.
- ind.delta.alpha
specifies the first parameter \(\alpha\) when
independent proposal \(Beta(\alpha, \beta)\) for \(\delta\) is used. Only applicable if MCMCmethod = 'IND'
- ind.delta.beta
specifies the first parameter \(\beta\) when
independent proposal \(Beta(\alpha, \beta)\) for \(\delta\) is used. Only applicable if MCMCmethod = 'IND'
- nsample
specifies the number of posterior samples in the output.
- control.mcmc
a list of three elements used in posterior sampling.
delta.ini
is the initial value of \(\delta\) in MCMC sampling.
burnin
is the number of burn-ins. The output will only show MCMC samples after bunrin.
thin
is the thinning parameter in MCMC sampling.