[R-sig-ME] MCMC fitting in glmmADMB

maren maren.rebke at avitec-research.de
Thu Oct 16 13:18:49 CEST 2014


I fit a zero-inflated Poisson model with random effects using the 
package glmmADMB, which worked perfectly well. Now I am trying to get 
credible intervals by running a Markov chain using mcmc=TRUE, which also 
works fine in general.

The problem is, that I have many parameters as well as several random 
effects in my model and it seems that I need to run long chains to get 
proper estimates. Therefore the automatically stored file eventually 
gets very big and my computer cannot handle it anymore. Therefore I 
would like to store only the samples of the estimates for the fixed 
effects (only beta) and not the rest. Is that possible somehow?

I am not sure, but would it help to specify parameters via mcmcpars? I 
tried to include mcmcpars in the owl example in section 2.2 from the 
vignette of the package 

fit_zinbinom1_bs_mcmc <- 

But unfortunately, I get an error message stating "unused argument 
(mcmcpars="beta")". As I wasn't sure if I have to state the fixed 
effects by using "beta" or the names of the parameters directly, I also 
tried including mcmcpars="BroodSize" but got the same error.

Is it not possible to define mcmcpars in glmmADMB? Is the definition of 
mcmcpars at all what I need and if so, how do I do it correctly?

Otherwise, is it possible to state that only the samples after a certain 
burnin period should be saved? Or can I play around with the jump sizes 
to reach faster convergence? As far as I understood those are rescaled 
depending on the acceptance rate at the moment. The automatic rescaling 
can be switched off by stating mcnoscale=TRUE, which is working. But I 
am not sure how I can then adjust the jump size and what the default is.

Thank you very much for taking the time to read this long email.

Best wishes,

Maren Rebke

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