Hi all,

I have been working with mixed effects models for a couple of years but I am relatively new to MCMCglmm() and MCMC techniques in general so I hope someone may be able to shed some light on an issue I have.

I am currently trying to fit a 3 level ordinal multinomial mixed model. To begin, I fitted a very simplistic model to try out the approach with only an intercept term and the random effect of ID using the following code:

prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0)))

m1<-MCMCglmm(newbmi~1, random=~ID, family="ordinal", data=data1, prior=prior)



>From assessment of  autocorr(m1$Sol)it appears that convergence is ok.



The issue I have concerns the random effects.  If I assess anything involving m1$VCV l obtain really strange results. For example,



> HPDinterval(m1$VCV[, "ID"]/(m1$VCV[,"ID"]+m1$VCV[,"units"]))
            lower     upper
var1 1.269593e-11 0.1352029
attr(,"Probability")
[1] 0.95


> cor(m1$VCV)
      ID units
ID     1    NA
units NA     1
Warning message:
In cor(m1$VCV) : the standard deviation is zero


> diag(autocorr(m1$VCV)[2,,])
           ID         units 
-0.0006257816           NaN 


Can anyone explain what this output means? My ID effect is tiny and I am not sure that it is necessary to have the random effect in the model after all. However, I intend to fit ID level explanatory variables in the model so wish to retain this random effect. I just don't understand what the problem is with the units term. Am I specifying the model incorrectly?



Any help/suggestions would be greatly appreciated!



Cheers,
Karen



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