Hello,

I'm running a model where the response is normally distributed and has an excess 
of zeros. Because I was familiar with the lmer package I used it to run a GLMM 
on this response, and addressed the problem of the excess of zeros by running 
two models: one with the response as a binary one, using the complete data set, 
and another excluding all the zeros and using the remaining response values in a 
Gaussian model. This seemed to work fine. However, a reviewer suggested using 
the whole data set and a zero-inflated poisson error structure in the MCMCglmm 
package. I don’t know if this is appropriate as my response are rates (grams of 
meat consumed per hr of observation) and not discrete values.

In any case, I tried running the model and received the following error message:
Error in MCMCglmm(Meat  ~ GG + Rank + Supp + Sex+  : 
  please use idh(trait):units or us(trait):units or trait:units for error 
structures involving multinomial data with more than 2 categories or 
zero-infalted/altered/hurdle models

The model I ran was:
res<-MCMCglmm(Meat  ~ GG + Rank + Supp + Sex+  DAI, random=~D_1+ D_2, 
data=xdata, family="zipoisson")

I understand from the message that I have to determine the variance structure of 
the random effects, by specifying the variance function in the model. However, 
I’ve tried several times to do it and nothing seems to work. I’m not interested 
in complex interactions among effects which is why I tried the simpler variance 
structure with a fixed intercept, but this didn’t work either:

res<-MCMCglmm(GG + Rank + Supp + Sex+DAI, random=~idf(1):D_1+idh(1):D_2, 
data=xdata, family="zipoisson")

Error message:
Error in substr(fformula, 1, closeB - 1) : invalid substring argument(s)

I would like to know if anybody could give me some feedback on whether using the 
MCMCglmm package with a zero-inflated poisson error structure would be a better 
approach than what I did, and if so, if somebody could give me some advice on 
how to proceed with the variance structure of the random effects.

Thank you very much!
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