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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