similar to: RNG

Full_Name: Richard Simard Version: OS: Linux Submission from: (NULL) (132.204.25.139) In the random number generator MARSAGLIA_MULTICARRY, your algorithm is different than the one in the original reference that you give in the R manual: Marsaglia in his post to the mailing list {\it sci.stat.math} on September 29, 1997. The last line in the R program has a ^ while Marsaglia's algorithm has

Forwarded message: >From BMCCULLO at fcc.gov Fri Nov 6 12:50:07 1998 Message-Id: <s642efa3.006 at fcc.gov> X-Mailer: Novell GroupWise 4.1 Date: Fri, 06 Nov 1998 12:51:24 -0500 From: Bruce McCullough <BMCCULLO at fcc.gov> To: mai at ms.uky.edu Subject: S-Plus RNG Mime-Version: 1.0 Content-Type: text/plain Content-Disposition: inline Mai, Someone forwarded to me your e-mail about

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

Hi: I was wondering why Marsaglia's new ziggurat method for generating deviates from the standard normal distribution has not been implemented in the R base package. I know that it is available in SuppDists pacakage of Bob Wheeler, as "rziggurat". According my timing tests, it is about 6 to 7 times faster (on a Pentium 2.4 MHz) machine than the default Inversion method used in

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

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 All. I have R code whose functionality is being replicated within a C+ program. The outputs are to be compared to validate the conversion somewhat - however (as is always the case) I have stuffed my code with random number calls. Random uniform numbers in C+ are being produced using the (Boost) mersenne-twister generators (mt11213b & mt19937) - which is the default type of generator

Dear All, The editor of a journal to which I had submitted a publication asked whether R has a "tested random number generator." My paper included Monte Carlo simulations generating random normal and random chi-square values. help(rnorm) lists Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37, 477-484. as a

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

Hi, subject more or less says it all. I freely admit to not having bothered to find some of the online papers about method of testing the quality of random number generators -- but in an idle moment I wondered what to expect from something like the following: randa<-runif(1000) randb<-runif(1000) t.test(randa,randb)$p.value var.test(randa,randb)$p.value [repeat ad nauseum] Is the

On 30 August 2016 at 18:29, Duncan Murdoch wrote: | I don't see evidence of a bug. There have been several versions of the | MT; we may be using a different version than you are. Ours is the | 1999/10/28 version; the web page you cite uses one from 2002. | | Perhaps the newer version fixes some problems, and then it would be | worth considering a change. But changing the default RNG

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

Roger Marquis wrote: > [snip] > >It takes all of 2 seconds to generate a ssh 2 new session on a >500Mhz cpu (causing less than 20% utilization). Considering that >99% of even the most heavily loaded servers have more than enough >cpu for this task I don't really see it as an issue. > >Also, by generating a different key for each session you get better >entropy,

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

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

Hi, I am a quite new to libvirt, and need to use it from within Java code. I was wondering if anyone has been able to generate Java classes from the publisher rng schema. I also wonder why rng format was used over more common formats, which would make things easier since there are a number of code generators for it which are well established, at least in the Java world... Any suggestion would be

Dear all, Is there a test in R for the randomness of a sequence of observations (e.g. to test the random number generator)? Specifically I am looking for autocorrelations which are not necessarily linear in nature, which the acf function does not seem to be flexible enough to detect as it tests for linear autocorrelation. Thanks in advance, Paul.

Dear all, i have a question about the "sample" function used in R, does it work as a pseudo-dandom number generator programmed with C, like it is described in Modern Applied Statics with S-Plus 3d edition chapter 5 section 2? Thanks a lot --------------------------------- [[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