[R-sig-ME] effective sample size in MCMCglmm
Walid Crampton-Mawass
w@||dm@w@@@10 @end|ng |rom gm@||@com
Tue Mar 23 16:42:21 CET 2021
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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