search for: seed

...;t seem to be able to find answer so I am hoping someone here can help. I found that if the factor names are all in numbers, fitting the model in lm would return labels that are not very recognizable. # Example: let's just assume that we want to fit this model fit <- lm(height ~ age + Seed, data=Loblolly) # See the category names are all mangled up here fit Call: lm(formula = height ~ age + Seed, data = Loblolly) Coefficients: (Intercept) age Seed.L Seed.Q Seed.C Seed^4 -1.31240 2.59052 4.86941 0.87307 0.37894 -0.46...

Hello again, I was testing today's SNAP (openssh-SNAP-20000830.tar.gz) in my Solaris 2.6-8 environment, when I found some problems with the ssh2 support. While connecting, it seeds the RNG something like 32 times! And then once connected, it seeds again 2 or 3 times with *every* keystroke! This makes for some slow going. This happens on all of the Solaris boxes I tried. Binary was compiled on 2.6 against OpenSSL 0.9.5a. Output follows: 6:19pm.orangecrush: ~# ssh -2...

...ction(){T or F} # is.Splus <- function(){T or F} if (is.S()) { RNGkind <- function(kind=NULL) {# With a null argument this returns the current kind. # kind = "default" resets the RNG to the Splus default. # Splus does not allow arbitrary setting of .Random.seed so # .RandomSeed is used. The existence of .RandomSeed is # used to indicate whether an alternate RNG is used and the # first element of .RandomSeed indicates the generator. if ( is.null(kind)) {if(exists(".RandomSeed")) kind <- c("...

Could someone please suggest a method to store the current random seed. I'm having trouble understanding how to correctly use set.seed and .Random.seed. Specifically, I have the following code that crashes: set.seed(seed) for(i in 1:10){ print( runif(1)) } To get around this I need to split the number of iterations into chunks: set.seed(seed) for(i...

I have noticed that when I iterate permutations of short vectors with the same seed, the cycle lengths are much shorter than I would expect by chance. For example: X <- 1:10 Xorig <- X start <- 112358 N <- 10 for (i in 1:N) { seed <- start + i for (j in 1:1000) { # Maximum cycle length to consider set.seed(seed) # Re-seed RNG to same initial state X...

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I receive (see the minimal example below): ,---- | > Loading required package: Rmpi | Loading req...

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 following function, a random uniform and the current .Random.seed should be printed: test<-function(runs,seed){ .Random...

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) { if (any(i > 1000) | any(i < 1)) stop("argument to set.seed() must be in 1:1000&quot...

Hi, this is related to a question just raised on Bioconductor where one function sets the random seed internally but never resets it, which results in enforced down streams random samples being deterministic. What is the best way to reset the random seed when you use set.seed() within a function? Is it still to re-assign '.Random.seed' in the global environment as following example illust...

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 to specify seeds." What I did not understand b...

I would like to suggest a modification of initial scrambling of the seed in RNG_Init (called from do_setseed). The modified code is equivalent, but faster. Patch against R-devel_2007-09-22 follows --- R-devel-orig/src/main/RNG.c 2007-09-02 07:49:35.000000000 +0200 +++ R-devel-modif/src/main/RNG.c 2007-09-23 10:51:59.234566440 +0200 @@ -216,8 +216,8 @@ BM_norm_keep...

is there a way to avoid c() appending ".0" and ".1" to seed? --8<---------------cut here---------------start------------->8--- > c("nons"=1, "seed"=3) nons seed ## good! 1 3 > c("nons"=1, "seed"=tab[1]) nons seed.0 ## don't want ".0"! 1 2...

Hi, I would like to construct a transition matrix from a data frame with annual transitions of marked plants. plant<-c(1:6) class<-c("seed","seed", "seed", "veg", "rep", "rep") fate<-c("dead", "veg","veg","rep", "rep", "veg") trans<-data.frame(plant, class, fate) plant class fate 1 1 seed dead 2 2 see...

...a permutation is decomposed into cycles and how the order relates to a partition of n (n=10 in your case). Enjoy! Eric On Fri, Dec 8, 2017 at 6:39 AM, Boris Steipe <boris.steipe at utoronto.ca> wrote: > I have noticed that when I iterate permutations of short vectors with the > same seed, the cycle lengths are much shorter than I would expect by > chance. For example: > > X <- 1:10 > Xorig <- X > start <- 112358 > N <- 10 > > for (i in 1:N) { > seed <- start + i > for (j in 1:1000) { # Maximum cycle length to consider > set.se...

> On 30/10/2019 9:08 a.m., peter dalgaard wrote: > > You can fairly easily work around that by saving and restoring .Random.seed. This is actually quite tedious to get correct; it requires you to under how and when .Random.seed is set, and what are valid values on .Random.seed. For instance, a common mistake (me too) is to reset to .GlobalEnv$.Random.seed <- NULL in a fresh R session but this will produce a warning on:...

Hi. Suppose I have a function which does some random number generation within. The random number generation inside the function changes the value of .Random.seed in the calling environment. If I want to restore the pre-function call .Random.seed, I can do: save.seed<-.Random.seed result<-myfunction() .Random.seed<-save.seed Is there a way to do the restoration inside the function? I tried putting the "save.seed<-.Random.seed" and...

For reasons that are best known to myself [ ;-) ] I have a value of .Random.seed saved (via dput()) in a file ``.Random.seed.save''. In my .Rprofile I have the lines: .Random.seed <- dget(".Random.seed.save") Junk <- dget(".Random.seed.save") print(all.equal(.Random.seed,dget(".Random.seed.save"))) print(all.equal(Junk,dget(".R...

...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 & zero down on the core issue, since I continue to believe that this is an issue with either R's implementation or a bad interaction of the seeds supplied with the Mersenne-Twister algorithm itself. The latter would require a deeper understanding of the algorithm than I have at the moment. If we can rule out the former through this thread, then I will pursue the latter solution path. Responses inline below, but summarizing: 1. All example...

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name...

On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: > The following helps identify when .GlobalEnv$.Random.seed has changed: > > rng_tracker <- local({ > last <- .GlobalEnv$.Random.seed > function(...) { > curr <- .GlobalEnv$.Random.seed > if (!identical(curr, last)) { > warning(".Random.seed changed") > last <<- curr > }...