[R-sig-ME] effective sample size in MCMCglmm
Abraão de Barros Leite
@b@rro@|b @end|ng |rom gm@||@com
Tue Mar 23 17:57:29 CET 2021
Unfortunately, I am able to have convergence in my model just with sample
size = 200. This sample size is so bad?
Thanks,
Abraão
Em ter, 23 de mar de 2021 13:21, Abraão de Barros Leite <abarrosib using gmail.com>
escreveu:
> Thanks, I will make these changes and monitor how the models will converge.
>
> All the best,
> Abraão
>
> Em ter, 23 de mar de 2021 12:42, Walid Crampton-Mawass <
> walidmawass10 using gmail.com> escreveu:
>
>> Hello,
>>
>> Indeed there is very high autocorrelation in your animal term. And your
>> scale seems to be quite large given the estimates and HPDs. A good step
>> would be to scale down your continuous variables to see if that helps with
>> convergence in any way. Another possible practice is to drop one of the
>> random terms in your model to see how that changes the behavior of your
>> model. A side note, in your prior, there is no need to add n=0.002 to your
>> residual term since you already fixed it to 1.
>>
>> 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 3:09 PM Abraão de Barros Leite <
>> abarrosib using gmail.com> wrote:
>>
>>> 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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