[Rd] A bug in the R Mersenne Twister (RNG) code?
ersatz.too at gmail.com
Tue Aug 30 23:45:37 CEST 2016
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:
[There is a link to this output at:
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.
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