
Dear all,

I would appreciate some help on the following:
I am trying to run MCMCglmm with zipoisson distribution AND separate random effects for a fixed binary factor

Some background:
I want to test for the effect of clonality (cln -> binary (yes or no)), environmental factors (e.g. fire and rain) and their interaction on species abundances (ab), where I use sampled location (pl.id) as random factor.

my data looks smth like this:
pl.id  - spp - ab - cln - fire.....
001 - 01 - 28 - n - 10
.....
001 - 35 - 11 - y - 10
.....
.....
770 - 01 - 10 - n - 17
.....
770 - 35 - 12 - y - 17
[so I've got 770 plots and 35 species]

Species abundance is poisson distributed but severely zero inflated as each species only occurs in a subset of plots. 
Moreover, I have reason to believe that clonality (a species level trait) further contributes to zero inflation (so I add trait:cln).
Otherwise I am not interested in the effect of environmental vars or random factors on the zero-inflation process (so I use at.level;(trait,1):...).

which comes down to [please correct me if I am wrong]:

mdl <- MCMCglmm(
ab ~ trait - 1 + trait:cln + at.level(trait,1):(cln*(fire + rain)),
random=~idh(at.level(trait,1)):pl.id,
rcov = ~idh(trait):units, 
prior=pr1, family = "zipoisson",
.....data=dat)

where pr1 is e.g. a flat prior for B and half-Cauchy prior for G

However, I would like to account for variation explained by species for clonals and non-clonals separately.
Without zero-inflation this would look something like: 

random=~pl.id + idh(cln):spp

But now I want to include this structure in "mdl", where the random effects are fitted to test only for their potential effects on the poisson processes
Intuitively I would suspect a syntax like this:


random=~idh(at.level(trait,1)):pl.id + idh(at.level(trait,1)):idh(cln):spp or perhaps
random=~idh(at.level(trait,1)):pl.id + idh(at.level(trait,1)):cln:spp 


I fiddled around quite a bit but generally got this message:

Error in buildZ(rmodel.terms[r], data = data) : object (idh(cln) and pl.id) not found

So my questions are:
1) Is it conceptually a good idea? I am not overly familiar with MCMCglmm but I couldn't find any posts on similar problems and wondered whether this should be a warning sign.
2) Am I bound to get severe convergence issues (just asking for an educated guess)? 
3) What would the syntax of such a random structure be.
4) and should I be aware of any specific considerations regarding the prior?

Would be great if anyone could enlighten me a bit or perhaps share experiences if you are trying to fit a similar model 

Thanks!
Evelien Jongepier
 		 	   		  
	[[alternative HTML version deleted]]


