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

[Euis Aqmaliyah]

Hi Jarrod,

Zero-Inflated that i meant is not Zero-Inflated Poisson. It is follow a
semicontinuous distribution with a mixture
of zeros and continuously distributed positive values.
But, i have tried to set fix residual variance and the convergence has
reached in two model that i use.
Thank you for your advice.

Regards

Pada tanggal 28 Mar 2017 01.18, "Jarrod Hadfield" <j.hadfield at ed.ac.uk>
menulis:

> 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?
>
> Cheers,
>
> Jarrod
>
>
>
> 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.
>>>      >
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>>>      >
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>>>
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