similar to: R random number generator

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

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. ---559023410-851401618-1218751024=:15885 Content-Type: TEXT/PLAIN; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: QUOTED-PRINTABLE I didn't describe the problem clearly. It's about the number of distinct=20 values. So just

Brian I've been playing a bit with the Wichmann-Hill RNG. I would prefer to have normally distributed random numbers and I think I have things generally worked out to use Wichmann-Hill and then Box-Muller. In the process, I was looking at R's rnorm.c, but could not figure out what transformation is used in R to convert uniform rv's to normal rv's. Do you know? It looks like there

Hello everyone. If I do f <- function(n){max(rnorm(n))} plot(sapply(rep(5000,4000),f)) #[this takes my PC about 30 seconds] then I get something quite unexpected: gaps in the distribution. For me, the most noticable one is at about 3.6. Do others get this? Is it an optical illusion? It can't be right, can it? Or maybe I just don't understand the good ol' Gaussian very

Dear Sir/Madam, I am a beginner in R. Here is my questions. 1. Can you give me one test for randomness (a name and descriptive paragraph is sufficient). 2. I have learned a uniform random number generator [e.g. not the algorithms: i)Wichmann-Hill, ii) Marsaglia-Multicarry, iii) Super-Duper (Marsaglia), iv) Mersenne-Twister, v) TAOCP-1997 (Knuth), or vi) TAOCP-2002 (Knuth)] . Is there any other

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

I am writing a function for the purposes of a simulation. Due to memory problems, the function sometimes crashes. In order to get around this problem, I would like to include to be able to save the "last" seed, so I can pick up with the next run of the simulation after a "crash". I am having trouble understanding what is going on with .Random.seed! For each run of the

I have commited fixes for 0.64.1 for (1) From: Mai Zhou <mai@ms.uky.edu> > .Random.seed <- c(1, 1:2) > .Random.seed [1] 1 1 2 > runif(5) Warning: Wrong length .Random.seed; forgot initial RNGkind? set to Wichmann-Hill[1] 0.02253721 0.84832584 ........ Here the length of the seed was being tested before the kind was picked out, so the length of the previous type was used. (2)

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

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

I'm running into problems with set.seed--maybe I'm misunderstanding something. I'm running R 1.5.1 on Windows 2000. I'm basically trying to capture the random seed so that I can reproduce a simulation if it's necessary later. Using set.seed, I can certainly get reproducible results, but not the results I get on the first pass. Here's an example: # Generate a random

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()

This question may not be specific to R, but I'm using R so here goes: Since R is slow (as is Splus) I want to split a simulation and run it on 2 or 3 systems at once. The simulations involve generating a large number of random values. How can I set .Random.seed so that the succession of random values don't overlap across systems. I see that when I invoke R and give command runif(1) a

Starting with example Wichmann-Hill code from Brian Ripley I have been playing with a set of programs for getting the same random sequences from R and Splus. A copy is included below along with a test (which works in Solaris with R and Splus 3.3). The approach is somewhat different from the usual problems on this list as I am trying to get the same results from Splus as I get from R. However,

There seems to be a minor problem with reproducing numbers from rnorm with Box-Muller. The pattern suggests it might have something to do with the value that gets dropped when an odd number of numbers is requested. (Details below.) Also, could "user-supplied" be added as an option for normal.kind in RNGkind. I'm sure the Box-Muller in R is better than my own attempt, but I would

Before the next release we really should to sort out the bias issue in sample() reported by Ottoboni and Stark in https://www.stat.berkeley.edu/~stark/Preprints/r-random-issues.pdf and filed aa a bug report by Duncan Murdoch at https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17494. Here are two examples of bad behavior through current R-devel: set.seed(123) m <- (2/5) * 2^32

Hi R-Developers, I had a question about the random number generator used in the R StandAlone Math Library. The stand-alone library depends on the unif_rand() function for most simulated values, and this function is provided in the sunif.c file in the relevant directory. At present, this program implements the "Marsaglia-Multicarry" algorithm, which is described throughout the R

Hello all, I am generating large samples of random numbers. The RNG help page says: "All the supplied uniform generators return 32-bit integer values that are converted to doubles, so they take at most 2^32 distinct values and long runs will return duplicated values." But I find that the cycles are not the same as the 32-bit integer. My test indicated that the cycles for

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

Hi, I'm seeing something that may be a bug in R's standalone math library, which is packaged by Debian as r-mathlib. I reported it to the Debian BTS as http://bugs.debian.org/657573 I'm using Debian squeeze, and the code was tested with r-mathlib 2.11.1-6 (default on stable) and 2.14.1-1 (from testing/unstable). I summarize this report below. The following code with the R math