similar to: R questions

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

R 0.64 on windows NT 4.0 Sometimes I got an error message by doing this > .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 ........ Sometimes I do not get error message: > .Random.seed <- c(1, 1:2) > .Random.seed [1] 1 1 2 > runif(1) [1] 0.5641106 >

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

If I have matrics as follows: > a <- c(1,1,0,0) > b <- c(4,4,0,0) > c <- c(3,5,5,6) How can I use R code to solve the equation ax^2+bx+c=0. thanks! yuying shi [[alternative HTML version deleted]]

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

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

I have applied L'Ecuyer's TESTU01 suite of RNG tests to the RNGs in R. TESTU01 offers three increasingly more stringent suites, called "Small Crush", "Crush" and "Big Crush". If a particular RNG fails Small Crush, there is no need to apply Big Crush. Below I summarize the results: Number of Tests Failed Small Crush Crush Big Crush

Hi all, I have happened to work on MS .NET for sometime now, and I found that this language offers RNG what is called as Donald E. Knuth's subtractive random number generator algorithm (found here: http://msdn.microsoft.com/en-us/library/system.random.aspx#Y12). ? Here I was wondering whether R also have same RNG in it's inventory, so looked at ?set.seed. There I found 2 related RNGs

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

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

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

Hi, I have a few questions about the RNG in R; apologies if these are dumb questions: 1. It is my understanding that, among the three types of random number generators available in R now, the best one is the Marsaglia Multicarry. Is this correct? 2. How does the best RNG in R compare (in terms of quality) to the RNG in SPlus? (based on Marsaglia's Super Duper)? Does the Super-Duper in

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)

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

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: >

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. We are facing a weird situation in our code when using R's [`runif`][1] and setting seed with `set.seed` with the `kind = NULL` option (which resolves, unless I am mistaken, to `kind = "default"`; the default being

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