[R-sig-ME] examples of combining chains from MCMCglmm

[Jarrod Hadfield]

Hi,

I would expect the run time to be linear if the thinning interval is  
set so that the number of iterations stored is equivalent in the two  
runs. It may be non-linear otherwise particularly the number of latent  
variabels/random effects to be stored is very large.  If this is not  
the case it suggests a memory leak. If you could provide sessionInfo()  
and a reproducible example that would be great?

Cheers,

Jarrod





Quoting Joshua Wiley <jwiley.psych at gmail.com> on Sun, 16 Sep 2012  
13:01:09 -0700:

> Hi All,
>
> Just wondering if anyone has examples lying around of combining chains
> from different runs of MCMCglmm on the same model?  If anyone does,
> I'd love to look at some.  Ideally they would be generalized (i.e.,
> able to combine an arbitrary number of chains).  If not, once I am
> done I will probably make a little example and post it somewhere.
>
> Also, the time to complete does not seem to be a linear function of
> the number of iterations.  Does anyone have comments on that?  I am
> saving a bunch of information (pr = TRUE, pl = TRUE, saveX = TRUE,
> saveZ = TRUE, saveXL = TRUE) so perhaps it has to do with that.  I ran
> 2e4 iterations and it took about 6.5 minutes.  6e4 iterations took
> 39.5 minutes, or nearly twice as long as would be expected from a
> linear increase.  I cannot share the actual data, but the general
> structure of the model is:
>
> MCMCglmm(outcome ~ 22 fixed predictors, family = "ordinal", data = dat,
>     random = ~ var1 + var2,
>     prior = list(
>       B = list(mu = rep(0, 23), V = diag(23) * (1 + 1)),
>       R = list(V = 1, fix = 1),
>       G = list(
>         G1 = list(V = 1, nu = .002),
>         G2 = list(V = 1, nu = .002)
>       )),
>     pr=TRUE, pl=TRUE, saveX = TRUE, saveZ = TRUE, saveXL = TRUE,
>     nitt = 4e5, thin = 1000, burnin = 1e4)
>
> The thinning is high because I had problems with autocorrelation on
> some parameters, possible mixing issues related to relatively
> unbalanced distribution of the outcome (approximately 80%, 10%, 10%
> for a three level ordered outcome).
>
> Thanks for any thoughts or tips,
>
> Josh
>
>
> --
> Joshua Wiley
> Ph.D. Student, Health Psychology
> Programmer Analyst II, Statistical Consulting Group
> University of California, Los Angeles
> https://joshuawiley.com/
>
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>
>



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