Hello altogether!

I just have one major question and one minor one, which I coudn't solve for myself yet and I have to admit that both of these are really becoming really a pain in the neck. I am not a statistician, so I do not come from a backround where such "normal" problems do get discussed a lot, accordingly I am a little bit stuck here. Any help would be really appreciated. And sorry in advance for the long text in the following... :-(

1.) (Major Q.) I want to fit a (bayesian) hurdle poisson model (glmm with no false zeros possible in this outcome variable) making use of winbugs/openbugs via two separate models (one binomial, one truncated poisson), but as I can see until now the implementation via openbugs (which allows truncation, winbugs not - I think?!?) with a truncated pois-distribution (as coded as RESPONSES[i] ~ dpois(mu[i])T(1,36) ) brings the simluation time to almost something where I feel I am faster by hand (5134 sec. for 10000 iterations burn in).
I think that I may do some mistakes regarding the uninformative priors (maybe to wide?) or the starting values (maybe to far away?) or is the "T(x,x)"-Coding wrong? Implementation via winbugs and openbugs just as a poisson glmm (ignoring the truncation at one) is going fast and reveals nearly the same estimates as in glmer for a poisson glmm (winbugs-coding here is nearly the same as in chapter 4 of zuur et al 2012 for a poisson glmm with one random group-factor).
Problem: Do I going to have a type-I-error-increase because of ignoring the truncation? Would it be statistical okay to just incorporate the response-variable for the poisson-model as (RESPONSES-1), pushing the whole variable one value lower, or does this implements something like noise into the mean and sd-estimates or into something else I cannot see from my "non-statistician"-perspective?!?! Do I overestimate something at an overall level, because I manually separate the data into "all" with RESPONSES Zero vs. One and a minor data-set where the "Ones" of the former data set are fitted as a truncated poisson?
Do I have to aim for the master goal, striktly speaking to aim at the whole model all in once into one coding, making use of the zero-ones-trick (an approach which maybe could give the opportunity to implement a car-structure (time) later on for the whole datat set, too)?
I tried to work out how to set up something like a (literally-speaking) "hurdle"-model as the one in pscl-package, but also accounting for a random intercept (glm to glmm-"upgrade"), which is something I could not find something similar of but gibs-sampling... I consultated zuur 2012, kery 2010, ntzoufras 2009, neelon 2010 (journal article in "Statistical Modelling"), gelman 2007 and more, but to summarize, I think many of these books and many of the things I found on the net or in the mailing list posts do not think of such an amateur question like mine, so I didn't found an answer... Could anybody give me a hint? Neelon et al. seem to incorporate all in once, but they are using a response variable what I think is a kind of proportion and I didn't find out how to cope with my own data in such a "coding environment" yet...

2.) (Minor Q.) When I try to implement a predictive check via a replicated data set in the same model (like proposed by zuur, kery and ntzoufras), I end up in a winbugs- or openbugs- crash at about 70% of trials (all stucks at gen.inits) and I really think I have to specify inits myself for the replicates, but firstly I do not find any examples doing so (or I am blind) and secondly I am very insecure about how to choose good inits here...anybody a hint for this? maybe it also depends on how I choose the priors?

Thanks to everyone for any help, I really am a bit exhausted after reading every bit of paper I could get a grip on in the last month to learn R, LMMs and GLMMS from the scratch for myself alone, but at the monent I stuck at these two problems for about two weeks and I do not know where to look any further.

Kind Regards, Ulf

P.S. Would an implementation into JAGS or STAN be faster than winbugs or openbugs regarding the slow mixing and simulation time?
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