[R-sig-ME] How to determine the length of the required burn-in until convergence in MCMCglmm package or another package

Jarrod Hadfield j.hadfield at ed.ac.uk
Mon Mar 27 20:18:38 CEST 2017

Hi Euis,

In an earlier post you said you were fitting zero-inflated models 
(zipoisson)? Is it possible you

a) forgot to fix the non-identifiable residual variance for the 
zero-inflation process at some value (e.g. 1)?

b) that the data are not zero-inflated but just over-dispersed so the  
zero-inflation parameters are heading off towards -Infinity?



On 27/03/2017 18:40, Ben Bolker wrote:
>   [please keep r-sig-mixed in Cc:]
>    To repeat what I said below, the general brute-force strategy would be
> N=2 (or 10 or something)
> run MCMCglmm with some reasonably optimistic default settings such that
> the final sample size (nitt-nburn)/thin is 1000
> while (convergence not satisfactory)
>      nitt = N*nitt
>      thin = N* thin
>      re-try MCMCglmm
> This brute force strategy will fail if something is wrong with your
> model (e.g. underdetermined).  Strengthening priors may help.  Other
> than that, without more information, we really can't help you more.
> On 17-03-27 11:19 AM, Euis Aqmaliyah wrote:
>> Thank you for your reply.
>> I'm sorry if my subject mail or my question is not clear.
>> Actually, i have understood that diagnostic convergence can use
>> potential scale reduction, potential scale reduction factor, or use
>> trace plot or another graphic  (i use potential scale reduction and
>> trace plot). But, in MCMCglmm Tutorial that i read, if convergence
>> hasn't reached, we can increase length of chain, or length of burn-in,
>> or thinning interval. So, it is that i ask.
>> Oh yes, i also have apply raftery.diag(). The output show sample size
>> that i need. So, i combine chain length, burn-in length, and thinning
>> interval so that yield sample size like in that output. But, it is still
>> doesn't convergence.
>> Regards
>> Pada tanggal 27 Mar 2017 21.16, "Ben Bolker" <bbolker at gmail.com
>> <mailto:bbolker at gmail.com>> menulis:
>>        We would probably need more information to help you.
>>        Some quick thoughts:
>>      - MCMCglmm usually burns in very quickly.   I would guess that either
>>      (1) your problem/data are really pathological; (2) you're confusing
>>      "burn-in" with "mixing"; if your chain reaches the stationary state
>>      quickly but samples it slowly, then you're having a burn-in rather
>>      than a mixing problem.  In general PRSF is meant to diagnose
>>      convergence, not just burn-in. (Although now that I read your
>>      question, it sounds like it's only the title that's specific to
>>      burn-in ...)
>>      - I think what most people do is brute-force (increase length of
>>      chain, increasing thinning at the same time so that the number of
>>      samples remains constant, until traceplots look OK/PRSF looks OK).
>>      - setting more informative priors may be helpful/necessary
>>      - the coda package has other diagnostics, in particular the
>>      Raftery-Lewis (raftery.diag()), which is supposed to estimate the
>>      chain length required for convergence.  You should be able to apply it
>>      to the components of an MCMCglmm fit ($Sol, $VCV, etc.), which are
>>      mcmc objects
>>      On Mon, Mar 27, 2017 at 5:16 AM, Euis Aqmaliyah
>>      <aqmalsaepul at gmail.com <mailto:aqmalsaepul at gmail.com>> wrote:
>>      > Hi,
>>      >
>>      > I stil try fit linear mixed model. I use Potencial Scale Reduction
>>      (PSR) to
>>      > check convergence. But, it still dosn't convergence. Is there any
>>      function
>>      > that can i use to determine length of chains, length of burn-in, or
>>      > thinning interval?
>>      >
>>      > Thank you.
>>      >
>>      >         [[alternative HTML version deleted]]
>>      >
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