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.
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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(home of 'The R Inferno' and 'A Guide for the Unwilling S User')
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