Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors.
For example:
res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5)
res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5)
res3 = MCMClogit(y~x,b0=5,B0=0.0001,data=mydat, burnin=500, mcmc=5500, seed=91011, thin=5)

Each result produces an object of class mcmc.
In order to use the Gelman-Rubin diagnostic test via coda, I need to "combine" these 3 mcmc objects appropriately.  I thought that this would be possible using as.mcmc.list() as the function description says:
The function ‘mcmc.list’ is used to represent parallel runs of the same chain, with different starting values and random seeds. The list must be balanced: each chain in the list must have the same iterations and the same variables.
So I try the following:
res123=as.mcmc.list(res1, res2, res3)
class(res123)
[1] "mcmc.list"

Then I try gelman.diag() from coda -
gelman.diag(temp123)
Error in gelman.diag(temp123) : You need at least two chains

So, how may I combine the separate mcmc objects so that I have multiple chains?
Many thanks,
Alan Kelly




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