similar to: set.seed

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

Recieving digests. > RNGkind(NULL) [1] "Marsaglia-Multicarry" "Kinderman-Ramage" I would appreciate it if anybody has any comments on the following. Please do not comment on the R functions themselves, since they merely mimic a (bivariate simplification of a) C routine called from S. In particular, I would like to know if anything is available with regard to the

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

Hello there, I am trying to compute the 3 months return momentum with the timeSeries x.ts, which is just a subset of simple returns from a much bigger series, > class(x.ts) [1] "timeSeries" attr(,"package") [1] "timeSeries" > dim(x.ts) [1] 20 3 > x.ts[1:8,] GMT MS.US AAPL.US CA.FP 1996-01-31 0.15159065 -0.133391894

Unfortunately, RNGkind is buggy. It will not generate warnings except the full name "Buggy Kinderman-Ramage" is supplied for normal.kind. match.arg is supposed to be called before "==" comparison. ======================================== Shengqiao Li Research Associate The Department of Statistics PO Box 6330 West Virginia University Morgantown, WV 26506-6330

On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > I think the analyses are correct, but I doubt if a change to the default > is likely to be accepted as it would make it more difficult to reproduce > older results. I'm a bit alarmed by the logic here. Unbiased sampling seems basic for a statistical language. As a consumer of R I'd

Hi, Our department has detected a bug in the implementation of the Kinderman-Ramage generator for normal random variates in version 1.7.0, which can be seen from the below R session. (Consecutive calls for chisq.test(...) always gives p-values very close to 0.) We have already encountered this bug in version 1.6.2 The error is in file R-1.7.0/src/nmath/snorm.c Here is a patch for this file to

On 09/19/2018 10:03 AM, Ben Bolker wrote: ... > Balancing backward compatibility and correctness is a tough problem > here. I think improvements in the RNG is a situation where backward compatibility is not really going to be lost, because people can specify the old generator, they just will not get it by default. My opinion is that the default needs to generally be the best option

On 2018-09-19 09:40 AM, David Hugh-Jones wrote: > On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.duncan at gmail.com> > wrote: > >> >> I think the analyses are correct, but I doubt if a change to the default >> is likely to be accepted as it would make it more difficult to reproduce >> older results. > > > I'm a bit alarmed by the logic

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

I'm pleased to announce that my employer, Becton Dickinson, has an open position for software implementation in the Matlab and S languages. If interested, send a cover letter and resume (or CV) to Dr. Richard Moore at richard_moore at bd.com. If you have questions, contact myself (at jim_garrett at bd.com) or Dr. Moore. Below find a position description. Jim Garrett Becton Dickinson

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

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

Today I struggled for hours to understand some unexpected package test results. It turned out that this is because package "parallel", buried deep in my dependencies, calls runif() during it's initialization and in this way changes the random number sequence. This seems to be a part of a more general question--which kind of functions can we trust if we want to preserve random

On 7 February 2015 at 19:52, otoomet wrote: | random numbers. For instance, can I be sure that | set.seed(0); print(runif(1)); print(rnorm(1)) | will always print the same numbers, also in the future version of R? There Yes, pretty much. I've been lurking here over fifteen years, and while I am getting old and forgetful I can remember exactly one such change where behaviour was changed,

I'm using R 1.9.1 on suse server v9 enterprise with the gee package version 4.13-10. I have code that runs in an automated script. It uses the gee function from the gee package. The script is run quite often without error. I have a problem where the script locks up R when calling this function (it starts execution and never finishes). I was able to track down the offending data and determine

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 two questions about using a user-defined random number generator (RNG) with the standalone Rmath library. The default RNG with the standalone Rmath library is the Marsaglia-multicarry generator, which has poor properties. The "R Installation and Administration" manual, in the section "The standalone Rmath library", states that: ``` A little care is needed to use the

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 >