[R-sig-ME] effective sample size in MCMCglmm

Abraão de Barros Leite @b@rro@|b @end|ng |rom gm@||@com
Mon Mar 22 20:09:38 CET 2021 

Hello,  this is my script, and my dataset has 235 species.

prior3.1 <- list(G = list(G1 = list(nu=0.002, V=1),G2 = list(nu=0.002,
V=1)),#fatores de variâncias a priori#
                 R = list( V=1,nu=0.002, fix=1))
m1<-MCMCglmm(progofic~1+Dieta+trafic+log(massakg)+endg,data=databird,
family="categorical",pedigree=contree,random=~animal+measureID,verbose = F,
         nitt=2500000,burnin=250000,thin=10000,prior =prior3.1)
summary(m1)
acf(m1$Sol[,1],lag.max =100)

*Results:*
  Iterations = 250001:2490001
 Thinning interval  = 10000
 Sample size  = 225

 DIC: 6.619977

 G-structure:  ~animal

       post.mean l-95% CI u-95% CI eff.samp
animal      7713    999.2    15303    10.58

               ~measureID

          post.mean  l-95% CI u-95% CI eff.samp
measureID     319.5 0.0005769     1505    81.04

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units         1        1        1        0

 Location effects: progofic ~ 1 + Dieta + trafic + log(massakg) + endg

                   post.mean l-95% CI u-95% CI eff.samp  pMCMC
(Intercept)          -32.757 -127.914   59.857  152.759 0.4889
DietaInvertebrate    -73.571 -153.500   -4.571    9.841 0.0356 *
DietaNectarivorous  -156.649 -527.852  191.174    4.441 0.6489
DietaOmnivore         -6.580  -43.869   33.693   83.293 0.7644
DietaVert            -13.890  -87.780   73.738   62.163 0.8000
traficyes             -1.761  -30.506   31.131  102.410 0.8711
log(massakg)          25.917    6.443   43.469   18.590 <0.004 **
endgEN                -3.139  -42.611   37.907   25.394 0.8889
endgVU               -35.510  -73.109    1.471   24.992 0.0444 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> acf(m1$Sol[,1],lag.max =100)
> autocorr(m1$VCV)
,* , animal*
               animal    measureID units
Lag 0      1.00000000 -0.022820572   NaN
Lag 10000  0.80567811 -0.045602281   NaN
Lag 50000  0.58800623  0.037466483   NaN
Lag 1e+05  0.37539889  0.221289380   NaN
Lag 5e+05 -0.08870539 -0.005622699   NaN
,* , measureID*
                animal    measureID units
Lag 0     -0.022820572  1.000000000   NaN
Lag 10000 -0.004848064  0.369835208   NaN
Lag 50000 -0.052249906  0.006497318   NaN
Lag 1e+05 -0.053667796 -0.001787120   NaN
Lag 5e+05 -0.015126358 -0.027780522   NaN
,* , units*
          animal measureID units
Lag 0        NaN       NaN   NaN
Lag 10000    NaN       NaN   NaN
Lag 50000    NaN       NaN   NaN
Lag 1e+05    NaN       NaN   NaN
Lag 5e+05    NaN       NaN   NaN

Thanks,
Abraão
On Mon, Mar 22, 2021 at 3:32 PM Walid Crampton-Mawass <
walidmawass10 using gmail.com> wrote:

> Yes possibly, or the sample size is too small for the model structure you
> are attempting. It would help if you share your model structure and results
> of autocorr() to check if autocorrelation between chain iterations is high.
>
> Additionally, when replying in this thread, use the reply all option so
> our thread and discussion is included in the r-sig archives.
> --
> Walid Crampton-Mawass
> Ph.D. candidate in Evolutionary Biology
> Population Genetics Laboratory
> University of Québec at Trois-Rivières
> 3351, boul. des Forges, C.P. 500
> Trois-Rivières (Québec) G9A 5H7
> Telephone: 819-376-5011 poste 3384
>
>
> On Mon, Mar 22, 2021 at 2:14 PM Abraão de Barros Leite <
> abarrosib using gmail.com> wrote:
>
>> Hello Walid, I used your thin, burnin, nitt values, the model arrived
>> sample size=1000, and there wasn't convergence still.
>> Do think if the problem is the priori values?
>>
>> Thanks,
>> Abraão
>>
>>
>> Em seg, 22 de mar de 2021 14:30, Walid Crampton-Mawass <
>> walidmawass10 using gmail.com> escreveu:
>>
>>> Hello,
>>>
>>> One way to improve the convergence of your phylogenetic model would be
>>> to increase the burn in iterations of the chain and take it into account in
>>> your total number of iterations. So in your case, I would set nitt=2500000,
>>> burnin= 500000 and nitt=2000, that way you would have a sample of 1000
>>> iterations saved from the total chain iterations (of course you can
>>> increase the thin interval based on the sample size of saved iterations you
>>> want).
>>>
>>> Good luck
>>> --
>>> Walid Crampton-Mawass
>>> Ph.D. candidate in Evolutionary Biology
>>> Population Genetics Laboratory
>>> University of Québec at Trois-Rivières
>>> 3351, boul. des Forges, C.P. 500
>>> Trois-Rivières (Québec) G9A 5H7
>>> Telephone: 819-376-5011 poste 3384
>>>
>>>
>>> On Mon, Mar 22, 2021 at 11:24 AM Abraão de Barros Leite <
>>> abarrosib using gmail.com> wrote:
>>>
>>>> Hello Mathew
>>>>  My name is Abraão, I saw your answer aboute MCMCGLMM sample size.
>>>> So, please can you help me?
>>>> I am working with relation between brain mass and nest birds in my
>>>> doctorate.
>>>> My dataset has 250 species, but in my analysis MCMCGLMM with
>>>> phylogenetic
>>>> control, I haven't convergence, with nitt=2000000, thin=3500,
>>>> burnin=4000.
>>>> Please, can you help me?
>>>> How I can to improve my convergence?
>>>> Sample size=100 in the end it's ok?
>>>> Thanks!
>>>>
>>>>         [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models using r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>

-- 
Abraão de Barros Leite
Universidade Federal de São Carlos (UFSCAR)
Programa de Pós-Graduação em Ecologia e Recursos Naturais- São Carlos
(PPGERN)

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