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

[Euis Aqmaliyah]

Hi Ben,

Thank you for your advise.
I try to re-set specification prior that i use.

Pada tanggal 28 Mar 2017 00.40, "Ben Bolker" <bbolker at gmail.com> menulis:


 [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>
>

	[[alternative HTML version deleted]]