Dear all,
I'm not 100% sure if this question is best directed at the r-list, or a mailing
list concerned with Bayesian analysis, so please accept my apologies if another
audience may be more appropriate.
I have been using the rjags package to run Jags models with multiple chains and
store the results in a Coda based mcmc list. For instance, having created a
jags model and done initial adapting and updating, I run the following command:
coda_odp_gini_only <- coda.samples(odp_gini_only,
variable.names=c("beta","sigma2.u2",
"deviance"),
n.iter=itercount, thin=thincount)
This create an object with 4 separate chains as requested in my initial call to
Jags which created the object "odp_gini_only". I then use the Coda package to
look at the results stored in "coda_odp_gini_only". Prior to running any
analysis with Coda, I used the command,
coda.options(combine.plots=TRUE, combine.stats=TRUE)
to ask for results that combine the four separate chains. Sure enough, if I
enter "summary(coda_odp_gini_only)", I am given a single set of output
combining the four chains. However, if I enter
"HPDinterval(coda_odp_gini_only)" I receive 4 sets of HPD figures, one for each
chain. Is it possible to combine the four chains together to receive a single
set of HPD estimates?
In a similar vein, is it possible to use the Coda object to estimate the
proportion of a given parameters distribution which is above (or below) a given
value, for instance, the proportion of the distribution of beta[1] greater than
zero? Again, in doing so, is it possible to combine the results of the four
chains into a single estimate?
Kind regards, and many thanks in advance for any advice anyone can offer me.
Paul
Dr Paul Norris
Lecturer in Social Policy
University of Edinburgh
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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