[Rd] A bug in the R Mersenne Twister (RNG) code?

[Paul Gilbert]

On 08/30/2016 06:29 PM, Duncan Murdoch wrote:
> I don't see evidence of a bug.  There have been several versions of the
> MT; we may be using a different version than you are.  Ours is the
> 1999/10/28 version; the web page you cite uses one from 2002.
>
> Perhaps the newer version fixes some problems, and then it would be
> worth considering a change.  But changing the default RNG definitely
> introduces problems in reproducibility,

Well "problems in reproducibility" is a bit vague. Results would always 
be reproducible by specifying kind="Mersenne-Twister" or kind="Buggy
Kinderman-Ramage" for older results, so there is no problem reproducing 
results. The only problem is that users expecting to reproduce results 
twenty years later will need to know what random generator they used. 
(BTW, they may also need to record information about the normal or other 
generator, as well as the seed.) Of course, these changes are recorded 
pretty well for R, so the history of "default" can always be found.

I think it is a mistake to encourage users into thinking they do not 
need to keep track of some information if they want reproducibility. 
Perhaps the default should be changed more often in order to encourage 
better user habits.

More seriously, I think "default" should continue to be something that 
is currently considered to be good. So, if there really is a known 
problem, then I think "default" should be changed.

(And, no I did not get burned by the R 1.7.0 change in the default 
generator. I got burned by a much earlier, unadvertised, and more subtle 
change in the Splus generator.)

Paul Gilbert

so it's not obvious that we
> would do it.
>
> Duncan Murdoch
>
>
> On 30/08/2016 5:45 PM, Mark Roberts wrote:
>> Whomever,
>>
>> I recently sent the "bug report" below toR-core at r-project.org and have
>> just been asked to instead submit it to you.
>>
>> Although I am basically not an R user, I have installed version 3.3.1
>> and am also the author of a statistics program written in Visual Basic
>> that contains a component which correctly implements the Mersenne
>> Twister (MT) algorithm.  I believe that it is not possible to generate
>> the correct stream of pseudorandom numbers using the MT default random
>> number generator in R, and am not the first person to notice this.  Here
>> is a posted 2013 entry
>> (www.r-bloggers.com/reproducibility-and-randomness/) on an R website
>> that asserts that the SAS computer program implementation of the MT
>> algorithm produces different numbers than R does when using the same
>> starting seed number.  The author of this post didn’t get anyone to
>> respond to his query about the reason for this SAS vs. R discrepancy.
>>
>> There are two ways of initializing the original MT computer program
>> (written in C) so that an identical stream of numbers can be repeatedly
>> generated:  1) with a particular integer seed number, and 2) with a
>> particular array of integers.   In the 'compilation and usage' section
>> of this webpage (https://github.com/cslarsen/mersenne-twister) there is
>> a listing of the first 200 random numbers the MT algorithm should
>> produce for seed number = 1.  The inventors of the Mersenne Twister
>> random number generator provided two different sets of the first 1000
>> numbers produced by a correctly coded 32-bit implementation of the MT
>> algorithm when initializing it with a particular array of integers at:
>> www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.out.
>> [There is a link to this output at:
>> www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html.]
>>
>> My statistics program obtains exactly those 200 numbers from the first
>> site mentioned in the previous paragraph and also obtains those same
>> numbers from the second website (though I didn't check all 2000 values).
>>    Assuming that the MT code within R uses the 32-bit MT algorithm, I
>> suspect that the current version of R can't do that.  If you (i.e.,
>> anyone who might knowledgeably respond to this report) is able to
>> duplicate those reference test-values, then please send me the R code to
>> initialize the MT code within R to successfully do that, and I apologize
>> for having wasted your time. If you (collectively) can't do that, then R
>> is very likely using incorrectly implemented MT code.  And if this
>> latter possibility is true, it seems to me that this is something that
>> should be fixed.
>>
>> Mark Roberts, Ph.D.
>>
>>     [[alternative HTML version deleted]]
>>
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>
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