[R] random number generator?

[Liaw, Andy]

Might I suggest taking a poll (even though unscientific) of how many people
will be affected by a change in default RNG?  My totally arbitrary guess is
very few, if any.

If I'm not mistaken, Python had only recently changed the default RNG to
Mersenne-Twister.  If Python can do it, I should think R can, too, without
too much pain...

Just my $0.02...

Andy

> -----Original Message-----
> From: ripley at stats.ox.ac.uk [mailto:ripley at stats.ox.ac.uk]
> Sent: Tuesday, January 28, 2003 3:53 PM
> To: Charles Annis, P.E.
> Cc: r-help at stat.math.ethz.ch
> Subject: RE: [R] random number generator?
> 
> 
> Can I suggest
> 
> RNGkind("Mersenne-Twister", "Inversion")
> 
> and especially the use of Inversion where tail behaviour of 
> the normal is
> important.
> 
> Were it not for concerns about reproducibility we would have 
> switched to 
> Inversion a while back.
> 
> On Tue, 28 Jan 2003, Charles Annis, P.E. wrote:
> 
> > 
> > Earlier today I reported finding an unbalanced number of 
> observations in
> > the p=0.0001 tails of rnorm.
> > 
> > Many thanks to Peter Dalgaard who suggested changing the normal.kind
> > generator.  
> > 
> > Using  RNGkind(kind = NULL, normal.kind ="Box-Muller")
> > seems to have provided the remedy.  For example:
> > 
> > > observed.fraction.below 
> > [1] 0.000103
> > > observed.fraction.above 
> > [1] 0.000101
> > >  
> > 
> > Thank you, Peter!
> > 
> > 
> > Charles Annis, P.E.
> > 
> > Charles.Annis at StatisticalEngineering.com
> > phone: 561-352-9699
> > eFAX: 503-217-5849
> > http://www.StatisticalEngineering.com
> > 
> > 
> > -----Original Message-----
> > From: r-help-admin at stat.math.ethz.ch
> > [mailto:r-help-admin at stat.math.ethz.ch] On Behalf Of Peter 
> Dalgaard BSA
> > Sent: Tuesday, January 28, 2003 2:36 PM
> > To: Charles Annis, P.E.
> > Cc: r-help at stat.math.ethz.ch
> > Subject: Re: [R] random number generator?
> > 
> > "Charles Annis, P.E." <AnnisC at asme.org> writes:
> > 
> > > Dear R-Aficionados:
> > > 
> > > I realize that no random number generator is perfect, so 
> what I report
> > > below may be a result of that simple fact.  However, if I 
> have made an
> > > error in my thinking I would greatly appreciate being corrected.
> > > 
> > > I wish to illustrate the behavior of small samples (n=10) and so
> > > generate 100,000 of them.
> > > 
> > > n.samples <- 1000000
> > > sample.size = 10
> > > p <- 0.0001
> > > z.normal <- qnorm(p)
> > > # generate n.samples of sample.size each from a 
> normal(mean=0, sd=1)
> > > density
> > > #
> > > small.sample <- matrix(rnorm(n=sample.size*n.samples, 
> mean=0, sd=1),
> > > nrow=n.samples, ncol=sample.size)
> > > # Verify that from the entire small.sample matrix, p 
> sampled values
> > are
> > > below, p above.
> > > #
> > > observed.fraction.below <- sum(small.sample <
> > > z.normal)/length(small.sample)
> > > observed.fraction.above <- sum(small.sample >
> > > -z.normal)/length(small.sample)
> > > 
> > > > observed.fraction.below 
> > > [1] 6.3e-05
> > > > observed.fraction.above 
> > > [1] 0.000142
> > > > 
> > > 
> > > I've checked the behavior of the entire sample's mean and 
> median and
> > > they seem fine.  The total fraction in both tails is 0.0002, as it
> > > should be.  However in every instance about 1/3 are in 
> the lower tail,
> > > 2/3 in the upper.  I also observe the same 1/3:2/3 ratio for one
> > million
> > > samples of ten.
> > > 
> > > Is this simply because random number generators aren't 
> perfect?  Or
> > have
> > > I stepped in something?
> > > 
> > > Thank you for your kind counsel.
> > 
> > You stepped in something, I think, but I probably shouldn't 
> elaborate
> > on the metaphor ... There's an unfortunate interaction 
> between the two
> > methods that are used for generating uniform and normal 
> variables (the
> > latter uses the former). This has been reported a couple of times
> > before and typically gives anomalous tail behaviour. Changing one of
> > the generators (see help(RNGkind)) usually helps.
> > 
> > 
> 
> -- 
> Brian D. Ripley,                  ripley at stats.ox.ac.uk
> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
> University of Oxford,             Tel:  +44 1865 272861 (self)
> 1 South Parks Road,                     +44 1865 272866 (PA)
> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> http://www.stat.math.ethz.ch/mailman/listinfo/r-help
> 


------------------------------------------------------------------------------