[R-sig-ME] How to determine the length of the required burn-in until convergence in MCMCglmm package or another package
j.hadfield at ed.ac.uk
Tue Mar 28 14:20:53 CEST 2017
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.
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.
> 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?
> 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
> model (e.g. underdetermined). Strengthening priors may help.
> than that, without more information, we really can't help you
> 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
> 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.
> 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>>>
> 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
> 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
> - 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
> > 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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