On Sat, 6 Feb 2010, Patrick Burns wrote:
A couple comments.
Although pseudo-random numbers were originally
used because of necessity rather than choice,
there is a definite upside to using them. That
upside is that the computations become reproducible
if you set the seed first (see 'set.seed').
I tend to encourage skepticism at pretty much
every turn. But I find this piece of skepticism
a bit misplaced. The application that you describe
does not sound at all demanding, and R Core is
populated by some of the best statistical computing
people in the world.
It depends on the purpose that the random numbers are needed for. For
statistical simulation the default generators are good, and if you want to be
even more sure you can run a simulation again with a different generator.
There are some purposes for which the generators are inadequate
1) they are not cryptographically secure: it is feasible to work out the random
seed and hence the future sequence by observing enough of the output. They
cannot be used to generate numbers that must be unpredictable to an intelligent
adversary. For many applications like this you wouldn't want to use numbers
from random.org either -- they are sent over the public networks, after all.
2) they may not be random enough for some number-theoretic algorithms. For
example, there is an efficient algorithm for finding prime numbers based on
random choices, but no efficient deterministic algorithm is known and it is an
open question whether an efficient deterministic algorithm even exists. It is
possible that simple random number generators could give substantially worse
performance in random algorithms of this sort, though the limited empirical
evidence I am aware of is in the other direction.
-thomas
On 05/02/2010 22:04, b k wrote:
Hello,
I'm running R 2.10.1 on Windows Vista. I'm selecting a random sample of
several hundred items out of a larger population of several thousand. I
realize there is srswor() in package sampling for exactly this purpose, but
as far as I can tell it uses the native PRNG which may or may not be random
enough. Instead I used the random package which pulls random numbers from
random.org, although in my extended reading [vignette("random-intro",
package="random")] it seem like that may have problems also.
I'm curious what the general consensus is for random number quality for
both
the native built-in PRNG and any alternatives including the random package.
Thanks,
Ben K.
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--
Patrick Burns
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http://www.burns-stat.com
(home of 'The R Inferno' and 'A Guide for the Unwilling S User')
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Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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