Hi All,
 
I am trying to model a mixed model where the variance component of the random effect is modelled as a continuous mixture. As hyperdistribution for the variance components I use a gamma distribution. In my application, there are typically a lot of small variances and a few higher ones (thus a high CV of the gamma distribution). When I analyse a simulated dataset with a high CV, I get the following error:
 
'update error for node ... algorithm UpdaterSlice.Left error can not sample node  too many iterations'
 
This can be avoided when truncating the gamma distribution for the very small values (e.g. var~dgamma(alpha,beta)I(0.00001,)). However, this results in an underestimation of the CV of this distribution, which is of primary interest to me. Is there another way around this?
 
all suggestions welcome
 
thanks
 
Stefan
 
 
 

Prof. Dr. Stefan Van Dongen
Department of Biology - Group of Evolutionary Biology
University of Antwerp
Groenenborgerlaan 171
B-2020 Antwerp
Belgium

personal page: http://www.ua.ac.be/stefan.vandongen

email: [log in to unmask]
Tel: + 32 (0)3 265 33 36
Fax: + 32 (0)3 265 34 74

 
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