I have been trying to fit a zibinomial distribution to a set of data I have.

The response variable is in a single column and represents the percentages of a certain incidence (ranging obviously between 0 and 1).

The data is assumed to be zero-inflated as > 40% of the data is made up of zeroes.

I have tried to fit the following:

analysis.1<-MCMCglmm(response~1,random=~animal,rcov=~units,family="zibinomial",nitt=50000,burnin=5000,thin=25,data=source1,pedigree=ped.dat)



I get the same error message as reported in a previous question regarding the "zibinomial" :

"Error in rowSums(data[, match(response.names[0:1 + nt], names(data))]) :
  error in evaluating the argument 'x' in selecting a method for function 'rowSums': Error in `[.data.frame`(data, , match(response.names[0:1 + nt], names(data))) :
  undefined columns selected"



I looked up the answer submitted to that question and tried creating  "success" and "failure" variables and fit the following:

MCMCglmm(cbind(success,failure)~ -1,random=~us(trait):animal,rcov=~us(trait):units,family=rep("zibinomial",2),nitt=50000,burnin=1000,thin=25,data=snap)

Now I'm getting the following error message:

"Error in MCMCglmm(cbind(success, failure) ~ - 1, random = ~us(trait):animal,  :
  binomial data must be non-negative integers"

I'm sure I don't have any negative integers in my data set.

I was wondering if it would be possible to get more clarification/example/reference on fitting zibinomial in MCMCglmm.



Also would it be possible to fit more than one pedigree relationship matrix? The current example is for an animal model but what if we had both an animal effect and a sire effect and we want to fit two pedigree relationship matrices instead of just one?



Any help on that issue is more than appreciated.



Thanks,



Mak



Makram J. Geha,PhD
Quantitative Geneticist
Dow AgroSciences, LLC.

	[[alternative HTML version deleted]]


