How to determine the length of the required burn-in until convergence in MCMCglmm package or another package

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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