[R-sig-ME] MCMC fitting in glmmADMB

Ben Bolker bbolker at gmail.com
Sat Oct 18 17:23:36 CEST 2014

  You can use mcmcControl(mcsave=...), as illustrated below.


om <- glmmadmb(SiblingNegotiation~FoodTreatment*SexParent+
nrow(om$mcmc) ## 20

om2 <- glmmadmb(SiblingNegotiation~FoodTreatment*SexParent+
nrow(om2$mcmc)  ## 10

On Thu, Oct 16, 2014 at 7:18 AM, maren <maren.rebke at avitec-research.de>

> 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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