That's all right. Thanks. On Sat, Oct 30, 2021 at 12:29 AM Marc Schwartz <marc_schwa...@me.com> wrote:
> 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 > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
