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

[Euis Aqmaliyah]

Dear Jarrod,

So, because my zero-inflated data is a mixture of zeros and continuosly
diatributed positive values, i use two models, there are linear mixed model
(LMM) for positive values and generalized linear mixed model (GLMM) with
logit as link function for probability of positive values.
In LMM, prior for residual variance that i set is R=list(V=1, nu=0) like
you said in my post a few days ago.
And then, in GLMM, i set fix residual variance.
Is it true?

Regards

Pada tanggal 28 Mar 2017 19.20, "Jarrod Hadfield" <j.hadfield at ed.ac.uk>
menulis:

> Hi,
>
> If you are not explicitly fitting a zero-inflated model then my
> suggestions are not relevant, and you should not fix the residual variance.
> A description of your data and a post of your model syntax would help us
> diagnose the problem.
>
> Cheers,
>
> Jarrod
>
>
>
> On 28/03/2017 13:16, Euis Aqmaliyah wrote:
>
> 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.
>>>>      >
>>>>      >         [[alternative HTML version deleted]]
>>>>      >
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>>>>
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>>
>>
>> --
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>> Scotland, with registration number SC005336.
>>
>>
>
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>

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