[GenABEL-dev] Interesting idea by Gulnara Svischeva

[Yurii Aulchenko]

In attached PDF I show the timing for running 'polygenic' (red line;
ML operating on Gulya's idea), polygenic_hglm (black; Xia/Lars), and
REML.rotate (green; Lars's implementation/extension of Gulya's idea)
as a function of sample size (X axes).

While polygenic and hglm show exponential trend, REMLrotate line is
not only the lowest, but also looks almost linear to me (also looking
at the code, it should be roughly linear on time with no of subjects).
So, with REMLrotate we can get arbitrary big speed-up (x100, x1000,
x10000, ... you name it!) cf other methods, just by adding more people
to the data :) Not a bad result!

Lars and Gulya, you are the champions!

best wishes,
Yurii

2011/5/19 Yurii Aulchenko <yurii.aulchenko at gmail.com>:
> Correction: as Lars spotted out, I did placed my 'tags' wrongly
> concerning time performance results, so actually REML.rotate is the
> fastest.
>
> Here are updated results:
>
>> With conv=1e-8 mean time for estimation on 300 IDs was:
>>
> 17.81 sec for polygenic_hglm
>> 2.27 sec for 'polygenic'-ML
> 1.33 sec for REML.rotate
>
> best wishes, and sorry for inconvenience,
> Yurii
>
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