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

[Walid Crampton-Mawass]

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!
>
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>
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