Ken Peng wrote on 10/29/21 2:39 AM:
I saw runif(1) can generate a random num, is this the true random?
runif(1)
[1] 0.8945383
What's the other better method?
Thank you.
Hi,
You do not indicate your use case, and that can be important.
The numbers generated by R's default RNGs are "pseudo random" (PRNGs),
which means that for most general purpose applications, such as common
Monte Carlo simulations or randomized clinical trial treatment
allocations, as suggested by the other replies, they will work fine.
As PRNGs, the actual pseudo-random permutations can be replicated by
setting the same 'seed' value each cycle for the PRNG in use.
For example:
> runif(5)
[1] 0.6238892 0.8307422 0.4955693 0.4182567 0.9818217
> runif(5)
[1] 0.2423170 0.4129066 0.9213000 0.8290358 0.1644403
will yield two different, pseudo-random, sequences.
However:
> set.seed(1)
> runif(5)
[1] 0.2655087 0.3721239 0.5728534 0.9082078 0.2016819
> set.seed(1)
> runif(5)
[1] 0.2655087 0.3721239 0.5728534 0.9082078 0.2016819
will yield the same sequence given the use of the same seed value before
each call to runif().
Thus, the sequences will appear to be random, but given a specific
algorithm and seed value, they are deterministic.
That repeatable behavior can be important if one wishes to come back at
some future date and replicate the exact same output sequence, presuming
other factors have not changed in the mean time, such as occurred with R
version 3.6.0, which is referenced in ?Random, where a default changed
to improve behavior.
Also, some R functions may use simulation or resampling approaches to
create various parameters, and you may wish to replicate the same result
with each iteration. Setting the seed value prior to the relevant
function call can enable that.
Also, review the resources at https://www.random.org for additional
references on the differences between PRNGs and other implementations,
especially if you might need something closer to a "true" RNG for more
rigorous work.
Regards,
Marc Schwartz
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