[R-sig-ME] Can an uninformative prior be too diffuse?

Jarrod Hadfield j.hadfield at ed.ac.uk
Mon May 12 17:09:31 CEST 2014

Hi Iain,

It's a bit hard to diagnose without seeing the data. Would it be  
possible to post them? Is it possible you have some random terms with  
very few levels?



Quoting Iain Stott <iainmstott at gmail.com> on Mon, 12 May 2014 11:54:50 +0100:

> Hi R-users
> I'm having an interesting problem in using MCMCglmm for a meta analysis.
> I run models as I would normally run them, with diffuse priors on
> fixed and random effects (fixed: mu=0, V=10e8; random: V=1, nu=0.001;
> gaussian model), and the posteriors I'm getting out of the models are
> not like anything I've seen before. The range is very high but the
> variance is very low, so that fixed effect posteriors are a spike
> around the mean and random effects posteriors are highly truncated at
> 0. A handful of coefficient sets are taking extreme values that seem
> to make no sense at all.
> I wonder why this is: running for longer does not fix the problem and
> the chains aren't autocorrelated. Parameter expansion does not help
> the random coefficients. I'm always seeing a handful of samples that
> are orders of magnitude larger than the data themselves (which are
> real weighted mean differences over about -2 to 2) whilst the vast
> majority are being taken from a much more sensible range.
> It seems the only solution to getting better posteriors is to make
> them less diffuse (decrease V for fixed effects, increase nu for
> random effects). This makes sense, but I'm not comfortable doing it
> when the posteriors are so sensitive to variances on the priors, and I
> don't know what it would mean for interpretation of the model. I'm not
> convinced that a prior can be "too" diffuse, and I'm not sure why
> these extreme samples are being accepted. But then, perhaps allowing
> the model to sample from a range that is so much larger than the data
> just doesn't make sense... although like I say, I would have expected
> these extreme values to be ditched based on likelihood.
> If anyone can shed some light, it would be greatly appreciated. For
> now I'll have to forego some of the random effects and forge ahead
> with gls...
> Iain
> - - - - - - - - - - - -
> Dr. Iain Stott
> Environment and Sustainability Institute
> University of Exeter, Cornwall Campus
> Tremough, Treliever Road
> Penryn, Cornwall, TR10 9FE, UK.
> - - - - - - - - - - - -
> http://www.exeter.ac.uk/esi/
> http://biosciences.exeter.ac.uk/cec/
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