--- "Rodrigo A. Alfaro" <[email protected]> wrote:
> Usually, when the method works the R2's for each imputation are not
> too much different. I suggest you to report the average writing a
> small note about the dispersion of the all R2's that you get... maybe
> something like that in the table "R2 = 0.45" and in the note
> "Average R2 obtained from 5 imputed datasets. The min R2 obtained was
> 0.42 and the max 0.47".
That's fine, even better would be if you computed the log of each R2,
than compute the mean, and than transform the mean back (with the -exp-
function) to the R2 metric. You can find the rational for this in:
http://www.stata.com/statalist/archive/2007-04/msg00900.html .
--- Richard Williams <[email protected]> wrote:
> See especially Maarten's very detailed post at
>
> http://www.stata.com/statalist/archive/2007-04/msg00900.html
>
> (although I think he said afterwards he had made a mistake
> somewhere).
I had some code that supposed to calculate the likelihood ratio
statistic proposed in (Meng and Rubin 1992). That code gives different
values for different seeds (after the multiple imputations were
created). So that is where I probably made a mistake.
Hope this helps,
Maarten
Meng, X.L. and Rubin, D.B. (1992) ``Performing likelihood ratio tests
with multiply-imputed data sets''. Biometrika, 79, pp. 103-111.
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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