[R-sig-ME] MCMCglmm: mix of good and bad eff.samp

[rafter sass ferguson]

In case any other newbie finds this thread in their searching:

Someone emailed me off-list and suggested going ahead and progressively
ramping up the iterations, and increasing the thin rate and burnin as well.

It worked great. All the model diagnostics I can throw at it are looking
good.


Rafter Sass Ferguson, MS
PhD Candidate | Crop Sciences Department
University of Illinois in Urbana-Champaign
liberationecology.org
518 567 7407

On Thu, Jun 18, 2015 at 4:48 PM, rafter sass ferguson <
liberationecology at gmail.com> wrote:

> Hello,
>
> I have a question about a mix of good and bad effective sample sizes. I've
> read Jarrod's tutorial, course notes, and paper, and googled around
> extensively, and haven't had any luck figuring this out. I'd be grateful
> for any guidance.
>
> Working with 721 observations, I fit a 2-level model with three Gaussian
> response variables and several fixed effects and interactions. The model
> looks good as far as trace plots and gelman diagnostics go.
>
> My concern is with the effective sample size. Eff.samp overall and for
> trait:block are quite good (1200, and mostly ~1200 w/ one 920,
> respectively). But for trait:ID and residuals (trait:units) eff.samp is
> terrible - mostly in the 150-200 range.
>
> I'll post more model details below, but here are my questions:
> • Do I need to worry about the poor eff.samp scores if I'm only interested
> in the fixed effects?
> • If it is a problem, could it be fixed by increasing iterations by a ~10x?
>
> Here is the model I fit -
> prior2 <- list(R=list(V=diag(3),nu=3),
>                G=list(G1=list(V=diag(3), nu=3.02, alpha.mu=rep(0,3),
> alpha.V=1000*diag(3)),
>                       G2=list(V=diag(3), nu=3.02, alpha.mu=rep(0,3),
> alpha.V=1000*diag(3))
>                ) )
>
> m2b <- MCMCglmm(cbind(professional, relational, practice) ~
>                  -1 + trait +
>                  trait:income + trait:age + trait:ethnicity + trait:gender
> + trait:residence +                  trait:education + trait:HDI +
> trait:Ineq + trait:Enviro + trait:Enviro:gender + trait:Ineq:gender +
> trait:Ineq:ethnicity + trait:Ineq:income,
>                random= ~us(trait):block + us(trait):ID, rcov=
> ~us(trait):units,
>                family=rep("gaussian",3),
>                prior=prior2,
>                nitt <- 80000, thin <- 25, burnin <- 50000,
>                data=df_sel,
>                verbose=TRUE)
>
> Here is the first part of the model summary:
>  Iterations = 50001:79976
>  Thinning interval  = 25
>  Sample size  = 1200
>
> If a full data set will help, let me know and I'll post one.
>
> Thanks so much for any suggestions!
>
> Warmly,
> Rafter
>
>
>
> Rafter Sass Ferguson, MS
> PhD Candidate | Crop Sciences Department
> University of Illinois in Urbana-Champaign
> liberationecology.org
> 518 567 7407
>

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