[R-sig-ME] effective sample size in MCMCglmm
Abraão de Barros Leite
@b@rro@|b @end|ng |rom gm@||@com
Tue Mar 23 17:21:39 CET 2021
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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