Hi,
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
(http://glmmadmb.r-forge.r-project.org/glmmADMB.pdf):
fit_zinbinom1_bs_mcmc <-
glmmadmb(NCalls~(FoodTreatment+ArrivalTime)*SexParent+BroodSize+(1|Nest),data=Owls,zeroInflation=TRUE,family="nbinom1",mcmc=TRUE,mcmc.opts=mcmcControl(mcmc=10,mcmcpars="beta"))
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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