[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
Tue Mar 28 14:38:51 CEST 2017
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
> <mailto: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 <mailto: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>
>> <mailto: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>
>> <mailto: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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