[R-sig-ME] Questions about mix models

[Alena Drašnarová]

Dear all,
 I have so complicated data and I am trying to gain correct results from them.

I am interested in factors influencing density and diversity of the
soil seed bank on alluvial meadows. I have nested design of my
experiment: 35 meadows (mead=M1-M35), three transects on each meadow
(trans=T1-T3) and  2 plots on each transect (top=A,B).
I found out  a lot of information (about soil properties, moisure,
litter, biomass, vegetation diversity and management).
At first, I tried to use glmer, but sometimes there was error message:

> a2<-glmer(number~top+depth+HPV+K+VVS+(1|mead/trans/top),data=dat,family=poisson)
Warning messages:
1: In mer_finalize(ans) :
 Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432
2: In mer_finalize(ans) :
 Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432
3: In mer_finalize(ans) : false convergence (8)

So, I decided to use MCMCglmm, but I am not sure with fitting the
model. I tried to fitt it by this way (example below is for one
factor):

> prior=list(R=list(V=1, n=0, fix=1), G=list(G1 = list(V =1,n=1),G2=list(V=1,n=1),G3=list(V=1,n=1)))
> m1 <- MCMCglmm(number ~ as.factor(top), random=~mead+mead:trans+mead:trans:top, family = "poisson", data=dat,prior=prior)
I am not sure with define prior and random effect.

I will be very happy, if anybody write me own experiences with these
models and similar data and help me which model is the best to use.

With kind regards
Alena Drašnarová