similar to: error in runif

Hi all I would be grateful you can help me with my problem. I try to run an optimization code . in one line I have runif in order to sample the PDF. I get this error while i run it. Error in runif(1, f$d[[n.of.u.vars + n.of.o.vars + j]][[2]][1], f$d[[n.of.u.vars + : invalid arguments Here is a part of that code: # initialize random numeber generator if (seed>0)

Dear R experts, For a fixed seed, the first random number produced by rnorm and runif has the same rank within the distribution, which I find useful. The following ranks differ, however. > set.seed(123) > runif(4) [1] *0.2875775* 0.7883051 *0.4089769* 0.8830174 > set.seed(123) > pnorm(rnorm(4)) [1] 0.2875775 0.4089769 0.9404673 0.5281055 I noticed that rnorm seems to

>>>>> 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

Mersenne Twister generator is known to be sensitive to the algorithm used to generate its initial state. The initialization used in R generates the initial state in a way, which leaves linear dependencies mod 2 among the bits in the initial state. Since Mersenne Twister performs only operations, which are linear mod 2, these dependencies propagate to the output sequence. An easy to see

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

Hello, Right. Missed that one. Rui Barradas Enviado a partir do meu smartphone Samsung Galaxy.-------- Mensagem original --------De: Eric Berger <ericjberger at gmail.com> Data: 30/01/2018 10:12 (GMT+00:00) Para: Rui Barradas <ruipbarradas at sapo.pt> Cc: Daniel Nordlund <djnordlund at gmail.com>, smart hendsome <putra_autumn86 at yahoo.com>, r-help at r-project.org

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 5 Nov 2017, at 15:17 , Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > On 04/11/2017 10:20 PM, Daniel Nordlund wrote: >> 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

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

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

Hello, Another way would be to use ?replicate and ?colMeans. set.seed(2511) # Make the results reproducible fun <- function(n){ f <- function(){ a <- runif(5, 1, 10) b <- runif(5, 10, 20) colMeans(cbind(a, b)) } replicate(n, f()) } fun(10) Hope this helps, Rui Barradas On 1/30/2018 8:58 AM, Daniel Nordlund wrote: > On 1/29/2018

I get ties in output from runif() when I generate as few as 10^5 variates and get quite a lot when I generate 10^6. Is this expected?? I haven't seen any duplication with rnorm(10^6), but see varying amounts of duplication using rexp(), rbeta() and rgamma(). I would have thought that there'd be enough precision that one wouldn't get ties until generating samples larger than this..

In the code below, you seem to be essentially using the random number generator to implement a hash function. This isn't a good idea. My impression is that pseudo-random number generation methods are generally evaluated by whether the sequence produced from any seed "appears" to be random. Informally, there may be some effort to make long sequences started with seeds 1, 2, 3, etc.

Bill, Appreciate the point that both you and Serguei are making, but the sequence in question is not a selected or filtered set. These are values as observed in a sequence from a mechanism described below. The probabilities required to generate this exact sequence in the wild seem staggering to me. T On Fri, Nov 3, 2017 at 11:27 PM, William Dunlap <wdunlap at tibco.com> wrote: >

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

Bill, I have clarified this on SO, and I will copy that clarification in here: "Sure, we tested them on other 8-digit numbers as well & we could not replicate. However, these are honest-to-goodness numbers generated by a non-adversarial system that has no conception of these numbers being used for anything other than a unique key for an entity -- these are not a specially constructed

I'm inclined to agree with the view that the "precision" of a generator should be highlighted better in the manual pages. When I do ?runif I don't see a mention, but ?.Random.seed DOES give the info, as Duncan points out, and it is suggested to look there. A 1-liner with each random number generator "CAUTION: random number generators use different mechanisms and

Hello, I would like to sample a population but the result needs to be reproducible, using 'runif' or 'sample' is the good way to do it but I can't manage to make the results reproducible even with the 'set.seed' function. My aim is that th call to 'sample(1:100,10)' gives always the same result, how can I do that? Thanks! Eric Esposito

I'm working on a problem where I'm introducing random error and have been using the built in function runif to provide that random error. However, I realized that I seem to be getting some unexpected behavior out of the function and was hoping someone could share some insight. I don't know the runif algorithm at all, but from the behavior I'm seeing, it seems that whenever I open

Hi there My script at each iteration produces a .dat file which I save in a directory with write.table. Then I need to read each line of this file and create a directory for each line and put elements of that row in that directory. I do it with the following script which I have inserted somewhere between my outer iteration loop, it works fine for the first iteration, but the problem is that it