Dear everyone, I have a model with 2 fixed factors, 1 random factor and a binary response variable. I ran a MCMCglmm with family=?categorical? and the prior for the residual being R=list(V=1, nu=0.002). In the summary of the model I got high post.mean values (around 50 for fixed effects and >1000 for random effects and sometimes up to 14000). I ran the same model with R=list(V=1, fix=1) which means that the variance of the residual is fixed to 1, so the residual becomes a fixed factor (if I understand correctly). In that case my post.mean values are smaller (between zero and 24). My questions are: 1) Are the large values in the first case normal? 2) How do I know which prior is the more appropriate for the residual? Bests, Camille ----- Camille Madec PhD student Plant Ecology and Evolution Uppsala University Sweden
MCMCglmm, priorR and binomial distribution
2 messages · Camille Madec, Jarrod Hadfield
Hi Camille, The second prior is the correct one. The residual variance is not identifiable in the likelihood for binary data: I have tried to explain this intuitively in Section 2.6 of the CourseNotes using a tasteless example of hospital deaths. Cheers, Jarrod Quoting Camille Madec <camille.madec at ebc.uu.se> on Thu, 31 Jan 2013 15:21:12 +0100:
Dear everyone, I have a model with 2 fixed factors, 1 random factor and a binary response variable. I ran a MCMCglmm with family=?categorical? and the prior for the residual being R=list(V=1, nu=0.002). In the summary of the model I got high post.mean values (around 50 for fixed effects and >1000 for random effects and sometimes up to 14000). I ran the same model with R=list(V=1, fix=1) which means that the variance of the residual is fixed to 1, so the residual becomes a fixed factor (if I understand correctly). In that case my post.mean values are smaller (between zero and 24). My questions are: 1) Are the large values in the first case normal? 2) How do I know which prior is the more appropriate for the residual? Bests, Camille ----- Camille Madec PhD student Plant Ecology and Evolution Uppsala University Sweden
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