similar to: R-alpha: compatibility

>>>>> Thomas Lumley writes: >> Two compatibility issues found while trying to convert a simulation >> from S to R. >> 1. set.seed() We don't have this function. According to Venables & >> Ripley it just picks a seed from a list of 1000 possibilities. How about >> >> "set.seed" <-function (i)

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,

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)

Below is a revised copy of my compatibility functions with additions from Martin. I expect I may need a new version when 0.6 arrives but I thought it would be worth circulating this in case it suggests anything which might be resolved before the release of 0.6 rather than after. Thanks to a few people for pointing out the R function machine(). I have a kludge below for determining the same

Kurt The function tempfile is available based on some C code from Friedrich Leisch. The function unlink is fairly simple in unix but I haven't experimented elsewhere. In this regard, I have put together a small kernel set of functions to try to handle cross platform and R vs S issues. This is appended below and I would certainly appreciate feedback. The purpose of this kernel is so that I can

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

I have found that specifying different "sizes" in the sample command has a funny effect on the random sampling. The code below is a condensed version of a function I wrote to simulate a bootstrap method. For simplicity, I eliminated the internal bootstrap loop, but kept a statement to draw one bootstrap sample, because this is where the problem occurs. The output (mean(y)^2) should be

In splus rexp() and rweibull() are related: > set.seed(153) > rexp(1) [1] 0.0493267 > set.seed(153) > rweibull(1, shape=1) [1] 0.0493267 (you can also try shape =2, then rweibull = sqrt(rexp) ) However, in rw0.64.1 (on Win NT) they are different > .Random.seed <- 1:4 > rexp(1) [1] 1.412030 > .Random.seed <- 1:4 > rweibull(1, shape=1) [1] 2.054032 May be rweibull

There is a difference between R and S (or S-Plus, I'm not sure about that) concerning the generation of random numbers. If an error occurs during the execution of a function, the seed is restored to its old value in S-Plus, but not in R. This feature of S-Plus has just turned out to be very useful for me because I got an error message during a small simulation for a certain choice of

Hello, I think there is an issue in the sampling rejection algorithm in R 3.6.0. The do_sample2 function in src/main/unique.c still has 4.5e15 as an upper limit, implying that numbers greater than INT_MAX are still to be supported by sample in base R. Please review the examples below: set.seed(123) max(sample(2^31, 1e5)) [1] 2147430096 set.seed(123) max(sample(2^31 + 1, 1e5)) [1] 1

--Multipart_Mon_Nov_24_14:51:09_1997-1 Content-Type: text/plain; charset=US-ASCII >>>>> "PaulG" == Paul Gilbert <pgilbert@bank-banque-canada.ca> writes: MM> The code is basically in V&R 1 and 2; V&R2 on p.167. I have it as a MM> C function that I used to dyn.load into S-plus in order MM> to prove that S-plus was using it.

Hi, I have a randomforest object "cost.rf" that was created in splus 8.0, now I need to use this trained RF model in R. So in Splus, I dump the RF file as below data.dump("cost.rf", file="cost.rf.txt", oldStyle=T) then in R, restore the dumped file, library(foreign) data.restore("cost.rf.txt") it works fine and able to restore the

In 2004 there was this R-Help posting from Jan 2004: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/33097.html R equivalent of Splus peaks() function? The peaks function there has worked well for me on a couple of projects, but some code using "peaks" failed today, which had worked fine in the past. I was looking for a peak in a test case that was a sine curve over one

Full_Name: Bin Nan Version: 1.3.0 OS: Win32 Submission from: (NULL) (141.211.15.110) The function resid(fit, "pearson") seems not giving the same Pearson's residuals for logistic regression as what Splus does. I found the problem when I fitted glm with family = binomial using Crowder's seed data.

I''m doing some simulations for which i need to use both S-plus and R. I generate in S+ some random normal distributions to define one dataset by iteration. I need to use the same dataset generated in S-plus in R. I was first thinking to generate in R the same dataset by using the same random number generator with a fixed seed. But It seems that S-plus and R don''t use the same

On 08/30/2016 06:29 PM, 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

Dear R-Developers and -Community, after compiling and installing R 1.2.2., I started 'demo(graphics)' for a test, and got: 'Error in rnorm(0): .Random.seed(0) is not a Normal type' Obviously .Random.seed was not initialized appropriately. Following the the documentation for '.Random.seed' [see below 'Examples'], this can be fixed rm(.Random.seed);

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

Hello, happy new year. I've come back to a problem from last spring. I need to understand what what is the technically correct method to save a seed and then re-set it. Although this example arises in the context of MT19937, I'm needing to do this with other generators as well, including L'Ecuyer streams. The puzzle is this comment in ?Random: "?set.seed? is the recommended way

Whomever, I recently sent the "bug report" below toR-core at r-project.org and have just been asked to instead submit it to you. Although I am basically not an R user, I have installed version 3.3.1 and am also the author of a statistics program written in Visual Basic that contains a component which correctly implements the Mersenne Twister (MT) algorithm. I believe that it is