dirichlet.c {bspmma} | R Documentation |
Mixture of Conditional Dirichlet Model
Description
MCMC generation of posterior distributions for the
conditional Dirichlet mixing distribution model, using
-cycle Gibbs sampler
Usage
dirichlet.c(data, ncycles=10, M=1,d=c(.1,.1,0,1000),
start=NULL)
Arguments
data |
is a two-column matrix with a row for each study in the meta-analysis. The first column is the log of estimate of relative risk, often a log(odds ratio). The second column is the true or estimated standard error of the log(odds ratio). |
ncycles |
is the number of cycles of the Markov chain. |
M |
is the precision parameter of the Dirichlet process prior. |
d |
is a vector of length four with the values of the
hyperparameters, in order, the location and scale of the Gamma
inverse prior, mean and variance multiplier for the normal prior
on |
start |
is an optional vector containing starting values for the
parameters, |
Details
This function generates MCMC output for the posterior distribution for
the parameters
where
is the number of
studies in the meta-analysis,
and
,
in the conditional Dirichlet mixing model for random-effects
meta-analysis. Notation is taken from Burr (2012),
Model
.
The MCMC algorithm for estimating the posterior under this model is
given in Burr and Doss (2005). The chain is a
-cycle Gibbs sampler which cycles through
the vector of
's and the pair
,
, and
the main part of the computational burden is in the first part of the
cycle, the generation of the vector of
's.
If starting values are not specified via the argument start
,
the default values are used, which are based on the data. The study
estimates are the starting values for the ,
and the mean and standard deviation of the study estimates are the
starting values for
and
, respectively.
Value
call |
the call that resulted in this object |
ncycles |
the number of cycles in the Markov chain |
M |
the value of the precision parameter for the conditional Dirichlet model |
prior |
the vector length four of hyperparameters |
chain |
A matrix with |
start.user |
logical, TRUE if the user supplied initial values of
the parameter vector, FALSE if input argument |
start |
vector of initial parameter values used in the MCMC algorithm, whether this was the default or was user-supplied |
References
Burr, Deborah (2012). “bspmma: An R package for Bayesian semi-parametric models for meta-analysis.” Journal of Statistical Software 50(4), 1–23. http://www.jstatsoft.org/v50/i04/.
Burr, D. and Doss, H. (2005). “A Bayesian semiparametric model for random-effects meta-analysis.” Journal of the American Statistical Association 100 242–251.
Sethuraman, J. (1994). “A constructive definition of Dirichlet priors.” Statistica Sinica 4, 639–650.
Examples
## Not run:
data(breast.17) # the dataset
breast.data <- as.matrix(breast.17) # put data in matrix object
set.seed(1) # initialize the seed at 1 for test purposes
breast.c1 <- dirichlet.c(breast.data, ncycles=4000, M=5)
breast.c2 <- dirichlet.c(breast.data,ncycles=4000, M=1000)
## End(Not run)