[R] random number generator?
Liaw, Andy
andy_liaw at merck.com
Tue Jan 28 22:04:03 CET 2003
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
>
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