[R-sig-ME] Setting priors for binary fixed effects

[Yannis Dim.]

Dear Pierre,

Thank you very much for your reply.
I was using family="categorical", but I will try your suggestion and I will
try family="threshold".
Thank you for explaining Halfwidth test to me. So I should only check the
traceplots to check the convergence?

Kind regards,
Yannis




Στις Τρί, 8 Σεπ 2020 στις 4:40 μ.μ., ο/η Pierre de Villemereuil <
pierre.devillemereuil using ephe.psl.eu> έγραψε:

> Dear Yannis,
>
> > I have a comparative phylogenetic model with a binary response variable,
> 5
> > binary explanatory variables as fixed effects and the phylogeny as a
> random
> > effect
>
> Which family are you using? In my experience, the family "threshold" tend
> to yield the best results in terms of MCMC mixing in MCMCglmm.
>
> >All the variables pass the Stationarity test but 2 of them fail the
> Halfwidth test.
>
> The Halfwidth test is not a test of convergence. It tests whether the
> sampling was "large enough", but in my experience, it's not a very robust
> test and I tend to rely on effective sample size to evaluate whether the
> MCMC was long enough or not. So, if you do not see trends in your
> traceplots, I would say that convergence is not a problem here.
>
> > Should I change them, due to the binary nature of my fixed effects?
> What's
> > the best priors for binary fixed effects?
>
> More informative priors for the fixed effect can help the mixing of the
> MCMC (at the cost of possible underestimation and issues with CI coverage
> if the priors are too informative), but as I said above, I'm unsure whether
> there is a problem here.
>
> Hope this helps,
> Pierre.
>
> Le mardi 8 septembre 2020, 08:48:01 CEST Yannis Dim. a écrit :
> > Dear everyone,
> >
> > I have a comparative phylogenetic model with a binary response variable,
> 5
> > binary explanatory variables as fixed effects and the phylogeny as a
> random
> > effect. The issue I have is that with a nitt=300,000,000, I check the
> > heidel.diag(mcmc.list(model$Sol)) and the plot(model) and  the model does
> > not converge. All the variables pass the Stationarity test but 2 of them
> > fail the Halfwidth test. The same 2 variables also have bad trace plots.
> > I wonder if changing the priors will improve the convergence.Currently I
> > use these priors:
> > prior<-list(R=list(V=1, fix=1),G=list(G1=list(V=1, nu=1000, alpha.mu=0,
> > alpha.V=1)))
> >
> > As you can see, I am using the default priors for the fixed effects.
> >
> > Should I change them, due to the binary nature of my fixed effects?
> What's
> > the best priors for binary fixed effects?
> >
> > I will be immensely grateful if someone could help, as this issue's been
> > bothering me for some time now.
> >
> > Kind regards,
> > Yannis Dimopoulos
> > PhD Student - The University of Hull
> >
> >       [[alternative HTML version deleted]]
> >
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> >
>
>
>

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