Message-ID: <1362089238.14071.9.camel@musuko.uchicago.edu>
Date: 2013-02-28T22:07:18Z
From: Stuart Luppescu
Subject: Data frame size limits in MCMCglmm?
In-Reply-To: <20130125103605.16911d1gwewajp5w@www.staffmail.ed.ac.uk>
On Fri, 2013-01-25 at 10:36 +0000, Jarrod Hadfield wrote:
> Hi Stuart,
>
> 2.4 million records is bigger than anything I've tried but in theory
> it should run, or return an error if it can't allocate enough memory.
> It definitely shouldn't be seg-faulting. If you could send a
> reproducible example (preferably one where it fails quickly) I will
> take a look into it.
I finally got around to doing this analysis on a 25% random sample. It
ran but took about 25 hours for 100,000 iterations. (Was that too many?)
Here are the results:
Iterations = 3001:99991
Thinning interval = 10
Sample size = 9700
DIC: 1739944
G-structure: ~tid
post.mean l-95% CI u-95% CI eff.samp
tid 0.4597 0.4426 0.4754 7732
R-structure: ~units
post.mean l-95% CI u-95% CI eff.samp
units 1 1 1 0
Location effects: final.points ~ gr10 + gr11 + gr12
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 1.0179 1.0007 1.0347 6334 <1e-04 ***
gr10 0.3155 0.3033 0.3278 7514 <1e-04 ***
gr11 0.5825 0.5686 0.5959 7728 <1e-04 ***
gr12 0.7262 0.7121 0.7412 7390 <1e-04 ***
---
Signif. codes: 0 ????**??? 0.001 ????*??? 0.01 ??????? 0.05 ??????? 0.1
??? ??? 1
Cutpoints:
post.mean l-95% CI u-95% CI eff.samp
cutpoint.traitfinal.points.1 0.9506 0.9459 0.9552 1458
cutpoint.traitfinal.points.2 1.9154 1.9097 1.9216 1092
cutpoint.traitfinal.points.3 2.9882 2.9807 2.9956 1096
The main reason I'm doing this analysis is to see if the results are
different with ordered category outcomes as opposed to treating the
outcome as numbers (which I've done with lmer). Does the fact that the
posterior means for the cutpoints are very close to the numerical values
mean that I am not gaining much by treating outcome as ordered
categories (and I can just use the results from lmer)?
Thanks.
--
Stuart Luppescu <slu at ccsr.uchicago.edu>
University of Chicago