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

  [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>

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

--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.

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]]
                 >
                 > _______________________________________________
                 > R-sig-mixed-models at r-project.org

            <mailto:R-sig-mixed-models at r-project.org>
                 <mailto:R-sig-mixed-models at r-project.org
            <mailto:R-sig-mixed-models at r-project.org>> mailing list

                 >

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

--
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.

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]]
                     >
                     > _______________________________________________
                     > R-sig-mixed-models at r-project.org

                <mailto:R-sig-mixed-models at r-project.org>
                     <mailto:R-sig-mixed-models at r-project.org
                <mailto:R-sig-mixed-models at r-project.org>> mailing list

                     >

    The University of Edinburgh is a charitable body, registered in
    Scotland, with registration number SC005336.

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]]
     >
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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.