[Rd] Bias in R's random integers?
@grubb @ending from redh@t@com
Thu Sep 20 23:09:23 CEST 2018
On Thursday, September 20, 2018 11:15:04 AM EDT Duncan Murdoch wrote:
> On 20/09/2018 6:59 AM, Ralf Stubner wrote:
> > On 9/20/18 1:43 AM, Carl Boettiger wrote:
> >> For a well-tested C algorithm, based on my reading of Lemire, the
> >> unbiased "algorithm 3" in https://arxiv.org/abs/1805.10941 is part
> >> already of the C standard library in OpenBSD and macOS (as
> >> arc4random_uniform), and in the GNU standard library. Lemire also
> >> provides C++ code in the appendix of his piece for both this and the
> >> faster "nearly divisionless" algorithm.
> >> It would be excellent if any R core members were interested in
> >> considering bindings to these algorithms as a patch, or might express
> >> expectations for how that patch would have to operate (e.g. re Duncan's
> >> comment about non-integer arguments to sample size). Otherwise, an R
> >> package binding seems like a good starting point, but I'm not the right
> >> volunteer.
> > It is difficult to do this in a package, since R does not provide access
> > to the random bits generated by the RNG. Only a float in (0,1) is
> > available via unif_rand().
> I believe it is safe to multiply the unif_rand() value by 2^32, and take
> the whole number part as an unsigned 32 bit integer. Depending on the
> RNG in use, that will give at least 25 random bits. (The low order bits
> are the questionable ones. 25 is just a guess, not a guarantee.)
> However, if one is willing to use an external
> > RNG, it is of course possible. After reading about Lemire's work , I
> > had planned to integrate such an unbiased sampling scheme into the dqrng
> > package, which I have now started. 
> > Using Duncan's example, the results look much better:
> >> library(dqrng)
> >> m <- (2/5)*2^32
> >> y <- dqsample(m, 1000000, replace = TRUE)
> >> table(y %% 2)
> > 0 1
> > 500252 499748
> Another useful diagnostic is
> plot(density(y[y %% 2 == 0]))
> Obviously that should give a more or less uniform density, but for
> values near m, the default sample() gives some nice pretty pictures of
> quite non-uniform densities.
> By the way, there are actually quite a few examples of very large m
> besides m = (2/5)*2^32 where performance of sample() is noticeably bad.
> You'll see problems in y %% 2 for any integer a > 1 with m = 2/(1 + 2a)
> * 2^32, problems in y %% 3 for m = 3/(1 + 3a)*2^32 or m = 3/(2 +
> 3a)*2^32, etc.
> So perhaps I'm starting to be convinced that the default sample() should
> be fixed.
I find this discussion fascinating. I normally test random numbers in
different languages every now and again using various methods. One simple
check that I do is to use Michal Zalewski's method when he studied Strange
Attractors and Initial TCP/IP Sequence Numbers:
The technique works by mapping the dynamics of the generated numbers into a
three-dimensional phase space. This is then plotted in a graph so that you
can visually see if something odd is going on.
I used runif(10000, min = 0, max = 65535) to get a set of numbers. This is
the resulting plot that was generated from R's numbers using this technique:
And for comparison this was generated by collecting the same number of
samples from the bash shell:
The net result is that it shows some banding in the R generated random
numbers where bash has uniform random numbers with no discernible pattern.
More information about the R-devel