search for: randomizations

Is there a way to generate identical random samples using R's runif function and SAS's ranuni function? I have assigned the same seed values in both software packages, but the following results show different results. Thanks! R === > set.seed(6) > random <- runif(10) > random [1] 0.6062683 0.9376420 0.2643521 0.3800939 0.8074834 0.9780757 0.9579337 [8] 0.7627319 0.5096485

Hey all, I seem to be having trouble fitting a spline to a large set of data using PSpline. It seems to work fine for a data set of size n=4476, but not for anything larger (say, n=4477). For example: THIS WORKS: ----------------------------- random = array(0,c(4476,2)) random[,1] = runif(4476,0,1) random[,2] = runif(4476,0,1) random = random[order(random[,1]),] plot(random[,1],random[,2])

Dear All, I have a codes which calculates the result of Ripley's K function of my data. I want to repeat this process 999 times. However, i am getting an error when i use the "for i in" function. Is there any way to repeat this analysis 999 times. Here are the codes i used ; data4 <- matrix(c(sample(id),data1),203,3) a <- data4[,1] random.case=data4[a==0,]

Currently we use non-cryptographically secure random numbers in two places, the error filter (to inject errors at random) and the random plugin. For this we have used either random_r or a home-brew-ish Linear Congruential Generator. Use of random_r is problematic on BSDs because it doesn't exist there. Use of the LCG is simply a bad choice. Replace both uses with a better quality and

v2: - Fix seeding. - Add a test that nbdkit-random-plugin is producing something which looks at least somewhat random. Rich.

Hello, In ?set.seed I notice that a seed is created from the system time. Thus if two machines were (hypothetically) running for the same time and R was started simultaneously on both, the would have the same seeds (correct?). I assume reading from /dev/random would be different for both of these machines, so my question is why not use an integer read from /dev/random to create the seed? Would

Currently we use non-cryptographically secure random numbers in two places, the error filter (to inject errors at random) and the random plugin. For this we have used either random_r or a home-brew-ish Linear Congruential Generator. Use of random_r is problematic on BSDs because it doesn't exist there. Use of the LCG is simply a bad choice. Replace both uses with a better quality and

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, I'm running R 2.10.1 on Windows Vista. I'm selecting a random sample of several hundred items out of a larger population of several thousand. I realize there is srswor() in package sampling for exactly this purpose, but as far as I can tell it uses the native PRNG which may or may not be random enough. Instead I used the random package which pulls random numbers from random.org,

Hi, I'm like to try a get the new release to work with Sun's new device, that can be installed with patch 112438-01. I compiled SSL attempting to point it at the random device: cd openssl-0.9.6d ./Configure solaris-sparcv7-gcc make DEVRANDOM="/kernel/drv/random" And then ran the SSH configure: ./configure --prefix=/opt/OBSDssh --with-pam --without-rsh \ --sysconfdir=/etc/ssh

Hi, I am very new to R and cannot seem to find how it generates random numbers. I am currently involved with a project that requires a random number generator and have developed one. I am, however, unsure of just how random it is and was wanting to compare my generator with that of R (as well as others). If anyone knows how the random numbers are generated or have any ideas on testing or

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")))

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

Dear R users: I just got confused some R code used in linear mixed model~ example,two factors,A, B,C,A is fixed ,B,C are random,and B is nested in C,if I wannt to use linear mixed model,are the following code correct for each case? case1:want to know random effect of B, case1<-lme(y~A*B*C,random=~B|C) where "B|C" stand for what?,mean B is nested in C? case2: how to wirte

Hello R users, Here is my question about generating random sample. How to set the random seed to recreate the same random numbers? For example, 10 random numbers is generated from N(0,1), then "runif(10)" is used.What if I want to get the same 10 random numbers when I run runif(10) again? Is it possible?I think .Random.seed should be used here. Thanks. Xiaoxi [[alternative

Dear useRs, A few days ago, the initial version 0.1.0 of a new package 'random' was uploaded to CRAN. The random packages provides convenient access to the non-deterministic random numbers provided by the random.org site created by Mads Haahr (http://www.random.org). While certain hardware and software solutions that provide access to non-deterministic random-numbers exist, few if any

Dear useRs, A few days ago, the initial version 0.1.0 of a new package 'random' was uploaded to CRAN. The random packages provides convenient access to the non-deterministic random numbers provided by the random.org site created by Mads Haahr (http://www.random.org). While certain hardware and software solutions that provide access to non-deterministic random-numbers exist, few if any

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?

Dear all, I've read numerous posts about the random and nested factors in lme, comparison to proc Mixed in SAS, and so on, but I'm still a bit confused by the notations. More specifically, say we have a model with a fixed effect F, a random effect R and another one N which is nested in R. Say the model is described by Y~F Can anyone clarify the difference between : random = ~1|R:N random

Hi, I have the following conceptual / interpretative question regarding random effects: A mixed effects model was fit on biological data, with observations coming from different species. There is a clear overall effect of certain predictors (entering the model as fixed effect), but as different species react slightly differently, the predictor also enters the model as random effect and with