[R-sig-ME] Data frame size limits in MCMCglmm?

[Jarrod Hadfield]

Dear Stuart,

I think your are right that because the cutpoints and intercept are  
evenly spaced the data are consistent with discretised gaussian data.  
I think it is the even spacing that is important not that they  
correspond numerically with the ordinal categories. After all the  
scale is arbitrary. Whether a model assuming the data are continuous  
when in fact they are discrete would be robust, I'm not sure.  
Regarding your off-list questions tid is a variance rather than a  
standard deviation and the units are probits.

Cheers,

Jarrod





Quoting Stuart Luppescu <slu at ccsr.uchicago.edu> on Thu, 28 Feb 2013  
16:07:18 -0600:

> 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 ‘ª**’ 0.001 ‘ª*’ 0.01 ‘ªâ€™ 0.05 ‘®â€™ 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
>
>
>



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