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

[Euis Aqmaliyah]

Okay.
Thank you Jarrod for your help.

Regards

Pada tanggal 28 Mar 2017 19.39, "Jarrod Hadfield" <j.hadfield at ed.ac.uk>
menulis:

> Hi,
>
> Yes - for the binary model you should fix the residual variance.
>
> Cheers,
>
> Jarrod
>
> On 28/03/2017 13:34, Euis Aqmaliyah wrote:
>
> 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]]
>>>>>      >
>>>>>      > _______________________________________________
>>>>>      > R-sig-mixed-models at r-project.org
>>>>>      <mailto:R-sig-mixed-models at r-project.org> mailing list
>>>>>      > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>      <https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>>>>>
>>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
>>>
>>> --
>>> The University of Edinburgh is a charitable body, registered in
>>> Scotland, with registration number SC005336.
>>>
>>>
>>
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>

	[[alternative HTML version deleted]]