similar to: predictable bit patterns in runif(n) shortly after set.seed

>>>>> Tirthankar Chakravarty <tirthankar.lists at gmail.com> >>>>> on Fri, 3 Nov 2017 13:19:12 +0530 writes: > This is cross-posted from SO > (https://stackoverflow.com/q/47079702/1414455), but I now > feel that this needs someone from R-Devel to help > understand why this is happening. Why R-devel -- R-help would have been

Dear list, I have noticed surprisingly big performance differences of runif() between Windows XP and (Debian) linux on similar CPUs (Pentium D 3.0GHz (WinXP)/3.2GHz (Linux)) and I wonder if there is a simple explanation for the difference. On a linux system (with a slightly better CPU and 1GB more RAM), execution of runif() seems to consume about 80% more CPU time than on a Windows XP

Martin, Thanks for the helpful reply. Alas I had forgotten that (implied) unfavorable comparisons of *nix systems with Windows systems would likely draw irate (but always substantive) responses on the R-devel list -- poor phrasing on my part. :) Regardless, let me try to address some of the concerns related to the construction of the MRE itself and try to see if we can clean away the shrubbery

Ralf I don't doubt this is expected with the current implementation, I doubt the implementation is desirable. Suggesting to turn this to pbirthday(1e6, classes = 2^53) ## [1] 5.550956e-05 (which is still non-zero, but much less likely to cause confusion.) Best regards Kirill On 26.02.19 10:18, Ralf Stubner wrote: > Kirill, > > I think some level of collision is actually

Dear all, Recently I am generating large random samples (10M) and any duplicated numbers are not desired. We tried several RNGs in R and found Wichmann-Hill did not produce duplications. The duplication problem is the interesting birthday problem. If there are M possible numbers, randomly draw N numbers from them, the average number of dupilcations D = N(N-1)/2/M. For Knuth-TAOCP and

Kirill, I think some level of collision is actually expected! R uses a 32bit MT that can produce 2^32 different doubles. The probability for a collision within a million draws is > pbirthday(1e6, classes = 2^32) [1] 1 Greetings Ralf On 26.02.19 07:06, Kirill M?ller wrote: > Gabe > > > As mentioned on Twitter, I think the following behavior should be fixed > as part of the

Full_Name: Wolfgang Huber Version: 1.8.0 OS: Linux Submission from: (NULL) (193.174.58.146) The man page for RNGkind says that the default is Mersenne-Twister, and when I start R interactively, I get in fact > RNGkind() [1] "Mersenne-Twister" "Inversion" However, during the execution of "R CMD check" I get > > ### ** Examples > > > > RNGkind()

Gabe As mentioned on Twitter, I think the following behavior should be fixed as part of the upcoming changes: R.version.string ## [1] "R Under development (unstable) (2019-02-25 r76160)" .Machine$double.digits ## [1] 53 set.seed(123) RNGkind() ## [1] "Mersenne-Twister" "Inversion"??????? "Rejection" length(table(runif(1e6))) ## [1] 999863 I don't

I sent this to R-devel early last month, but have received no response, so I guess it really is a bug. This looks like a bug to me, and is a bit hard to describe, but easy to reproduce. ? Basically, if RNGkind is saved as something other than the default, and if the first operation in a session is a set.seed(), the default is reverted to. ?Reproduce by: cafe-rozo> ?R --vanilla R version

This looks like a bug to me, and is a bit hard to describe, but easy to reproduce. Basically, if RNGkind is saved as something other than the default, and if the first operation in a session is a set.seed(), the default is reverted to. Reproduce by: cafe-rozo> R --vanilla R version 2.7.1 (2008-06-23) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is

In a vanilla R 2.14.0 GUI session (on Windows XP SP3): > library(parallel) > cl<-makePSOCKcluster(2) > RNGkind() [1] "Mersenne-Twister" "Inversion" > clusterSetRNGStream(cl) > RNGkind() [1] "L'Ecuyer-CMRG" "Inversion" > stopCluster(cl) Is it intentional that clusterSetRNGStream() changes the RNG kind in the master process?

Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad > On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> >

Dear list, For the implementation of a particular optimization algorithm it is very important the random number generator. I would like to know if somebody could tell me what is the random number generator used by default in the 'runif' function. >From the help page of 'runif' and '.Random.seed' I guess that the default algorithm is 'Mersenne-Twister', but I

The random numbers in a stream initialized with one seed should have about the desired distribution. You don't win by changing the seed all the time. Your seeds caused the first numbers of a bunch of streams to be about the same, but the second and subsequent entries in each stream do look uniformly distributed. You didn't say what your 'upstream process' was, but it is easy to

On Sun, Mar 4, 2018 at 3:23 PM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: >> >> The following helps identify when .GlobalEnv$.Random.seed has changed: >> >> rng_tracker <- local({ >> last <- .GlobalEnv$.Random.seed >> function(...) { >> curr <- .GlobalEnv$.Random.seed

Another other generator is subject to the same problem with the same probabilitiy. > Filter(function(s){set.seed(s, kind="Knuth-TAOCP-2002");runif(1,17,26)>25.99}, 1:10000) [1] 280 415 826 1372 2224 2544 3270 3594 3809 4116 4236 5018 5692 7043 7212 7364 7747 9256 9491 9568 9886 Bill Dunlap TIBCO Software wdunlap tibco.com On Fri, Nov 3, 2017 at 10:31 AM, Tirthankar

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

Hi All, I have k identical parallel pieces of code running, each using n.rand random numbers.? I would like to use the same RNG (for now), and set the seeds so that I can guarantee that there are no overlaps in the random numbers sampled by the k pieces of code.? Another side goal is to have reproducibility of my results.? In?the past I have used C with SPRNG for this task, but I'm hoping

On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: > The following helps identify when .GlobalEnv$.Random.seed has changed: > > rng_tracker <- local({ > last <- .GlobalEnv$.Random.seed > function(...) { > curr <- .GlobalEnv$.Random.seed > if (!identical(curr, last)) { > warning(".Random.seed changed") > last <<- curr

Tirthankar, "random number generators" do not produce random numbers. Any given generator produces a fixed sequence of numbers that appear to meet various tests of randomness. By picking a seed you enter that sequence in a particular place and subsequent numbers in the sequence appear to be unrelated. There are no guarantees that if YOU pick a SET of seeds they won't produce