[R-sig-ME] RE : Questions about mix models

Mon Aug 16 21:49:39 CEST 2010

Hello,

given that you are interested in investigating the effects of a series of 
predictors (e.g. moisture) on the number of seeds, whilst using random 
effects to account for your sampling design, I would actually suggest to fit 
your model without "top" also as fixed effect. Something like:

glmer(number ~ depth + HPV + K + VVS + 
(1|mead/trans/top),data=dat,family=poisson)

HTH, just my 2 cents.

Cheers,

Luca

----- Original Message ----- 
From: "Alena Drasnarová" <drasnarova.alena at gmail.com>
To: "Julien Beguin" <julien.beguin.1 at ulaval.ca>
Cc: <r-sig-mixed-models at r-project.org>
Sent: Monday, August 16, 2010 1:38 PM
Subject: Re: [R-sig-ME] RE : Questions about mix models

Julien, thank you for your reaction.
1) Below you can see structura of my data (for 1 meadow)

        mead trans top depth number man litt water pH Ca K Mg P N C  VVS  1
1 A S 605 L 8.6 0 5.28 40.667 8.000 14.292 1.903 0.165 14.068 0.199  1 1 A V
582 L 8.6 0 5.28 40.667 8.000 14.292 1.903 0.165 14.068 0.199  1 1 B S 135 L
10.5 208 4.49 3.629 4.484 2.387 1.889 0.185 10.173 0.096  1 1 B V 153 L 10.5
208 4.49 3.629 4.484 2.387 1.889 0.185 10.173 0.096  1 2 A S 3 L 2.6 182
5.90 114.113 33.967 27.520 1.848 0.167 8.782 0.457  1 2 A V 2 L 2.6 182 5.90
114.113 33.967 27.520 1.848 0.167 8.782 0.457  1 2 B S 18 L 7.7 332 5.48
133.495 9.194 41.580 1.769 0.252 11.612 0.252  1 2 B V 57 L 7.7 332 5.48
133.495 9.194 41.580 1.769 0.252 11.612 0.252  1 3 A S 387 L 5.4 0 5.84
266.500 8.588 51.103 1.777 0.211 18.139 0.232  1 3 A V 462 L 5.4 0 5.84
266.500 8.588 51.103 1.777 0.211 18.139 0.232  1 3 B S 62 L 4.5 5 5.32
227.184 15.444 47.302 1.895 0.337 14.172 0.313  1 3 B V 22 L 4.5 5 5.32
227.184 15.444 47.302 1.895 0.337 14.172 0.313
Only on 2 meadows there are some missing data. But I prefer to use these
plots too.

2)
I did not try my model without top in random part. I can try it, but I think
that the model will lost important information about my design. About deegre
of freedom, I am not sure how to calculate them.

Alena

Dne 16. srpna 2010 15:26 Julien Beguin <julien.beguin.1 at ulaval.ca>
napsal(a):
> Alena,
>
> 1) Can you join a summary of your data. Is it a balanced design?
>
> 2) Not sure to understand how your model assigns the residual error...
Have you tried to exclude variable 'top' from the random component: only
(1|mead/trans) ? does it improve convergence? and do you get the appropriate
number of degree of freedom for your fixed effects (based on your
experimental design)?
>
> Julien Beguin
> ________________________________________
> De : r-sig-mixed-models-bounces at r-project.org [
r-sig-mixed-models-bounces at r-project.org] de la part de Alena DraÅ¡narovÃ¡ [
drasnarova.alena at gmail.com]
> Date d'envoi : 16 aoÃ»t 2010 05:58
> Ã? : r-sig-mixed-models at r-project.org
> Objet : [R-sig-ME] Questions about mix models
>
> 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 DraÅ¡narovÃ¡
>
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