similar to: .Random.seed in R-devel

The Intel compiled version also fails the below test: > ###------------ Very big and very small > umach <- unlist(.Machine)[paste("double.x", c("min","max"), sep='')] > xmin <- umach[1] > xmax <- umach[2] > tx <- unique(outer(-1:1,c(.1,1e-3,1e-7)))# 7 values (out of 9) > tx <- unique(sort(c(outer(umach,1+tx))))# 11 values

It appears that the maximum number of connections available in R is about 48. Can anyone tell me how to bump this number up? I've been perusing the source, but any info would speed things up. Is there a reason that it was set to such a low number? Thanks for any help. -Frank

I checked the help and the mailing list archives, but I can find no mention of a routine that calculates higher moments like skewness and kurtosis. Of course, these are easy enough to write myself, but I was thinking that they MUST be in here. Am I wrong? Thanks. -Frank

Is there a way to tell how much memory the computer running R has? -Frank -----Original Message----- From: David Smith [mailto:dsmith at insightful.com] Sent: Monday, February 09, 2004 1:32 PM To: Ross Boylan Cc: r-help Subject: RE: [R] R does in memory analysis only? Ross Boylan writes: > R works only on problems that fit into (real or virtual) memory. > ... does S-Plus have the same

Hi again, thanks a lot for the quick answer. I just forgot the comma, always these stupid mistakes... Anyways, as I said before, I have two data.frames containing about 1000 rows and I would like to avoid looping through all of them... In each data.frame are coordinates (x,y,z), so every row is giving the information on one single point. I would like to calculate the distance from each point

If anyone has a few extra CPU cycles to spare, I'd appreciate it if you could verify a problem that I have encountered. Run the code below and tell me if it crashes your R before completion. library(lme4) data(bdf) dump<-sapply( 1:50000, function(i) { fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf, random = ~ IQ.ver.cen | schoolNR); cat("

hello all, I use the R standalone math library in my own C program, and the default R random number generator can be seeded with set_seed(const unsigned int, const unsigned int). How could I seed the RNG with an instance of .Random.seed ? I would need this or a similar workaround for debugging purposes. More precisely, I use the default R random number generator to sample from various

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

This would be easier if base::set.seed() accepted a value of .Random.seed instead of just a scalar integer or, new to R-3.0.0, NULL. If set.seed() returned the previous value of .Random.seed (NULL if there was no previous value) things might be even easier. People should not have to know where .Random.seed is stored. S+'s set.seed() accepts a value of .Random.seed but does not return the

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?

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

Hi R-users I have two conditions. For each condition, 100 sets of 10 random numbers from N(0,1) need to be generated. Here is my question. At the begining I specify a seed number. I want to make the 100th set of the first condition and 1st set of the second conditon the same. What do I need to do ? After generating 99th set of 10 random numbers and then saving .Random.seed then using

Hi all, I have a question about the SOM routine. You can either supply the initial representatives for the lattice yourself or else they are chosen randomly from the dataset. Is it possible to pass the random-seed as an argument somehow, when choosing the random initialisation of the lattice? As it is now, each time I run a SOM on a dataset with the same settings the resulting SOM will still

Hello, all! I need help on these two problems: 1) If I want to randomly draw numbers from standard normal (or other distributions) in loops e.g.: ty=0; ks=0 for (i in 1:5) { set.seed(14537+i) k<-rnorm(1) ks[i]<-.3*k+.9 if (ty==0) { while ((ks<.2)||(ks>3)) { #set.seed(13237+i*100) k<-rnorm(1)

I have this snippet of code from an example in Dr. Harrel's book "Regression Modeling Strategies" p 501 n<-2000 .Random.seed <-c(49,39,17,36,23,0,43,51,6,54,50,1) age <-50 + 12 * rnorm(n) age I get the error message: Error in rnorm(n) : .Random.seed[1] is NOT a valid RNG kind (code) I have tried this on Windows and Linux R versions 1.8.1, 1.9.0, and 1.9.1

Full_Name: Pall Melsted Version: 2.3.1 OS: WinXP Submission from: (NULL) (71.240.25.175) Random set is not reset when starting R again. When R starts with a [Previously saved workspace restored] it seems that the .Random.seed variable is already set. If you quit R (and don't save your workspace) the next time you start R .Random.seed will be set to the same value again. Steps to

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, The problem arises when I lazyLoad() the .Random.seed from a previously saved database. To simplify the process of reproducing the problem, see the example below: ## this assignment may not really make sense, but illustrates the problem delayedAssign('.Random.seed', 1L) typeof(.Random.seed) # [1] "integer" rnorm(1) # Error in rnorm(1) : # .Random.seed is not an integer

Hi, The problem arises when I lazyLoad() the .Random.seed from a previously saved database. To simplify the process of reproducing the problem, see the example below: ## this assignment may not really make sense, but illustrates the problem delayedAssign('.Random.seed', 1L) typeof(.Random.seed) # [1] "integer" rnorm(1) # Error in rnorm(1) : # .Random.seed is not an integer

I am not sure whether this is really a bug, but seems to be as my program works pretty well in S+, for small iterations. I am creating a random sample from a chi^2 distribution with 23.5 df. Thus I have to sample from a chi^2 with 23 or 24 df with probability 0.5. The other alternative is to create a weighted version of the two distribution, which is where my problem lies. I use the