similar to: Why R simulation gives same random results?

Hi all, I am running a simulation and a curious error keeps coming up that stops the whole process. The error is a subscript out of bounds error, and it seems to happen at different points (floating around) throughout the looping simulation. Say, for example, it crashes on sample 1 - iteration 200. I can force it to start again on iteration 202 with all of the same settings, and it is

I wonder if someone can help me understand some counterintuitive simulation results. Below please find 12 lines of R code that theoretically, to the best of my understanding, should produce essentially a flat line with no discernable pattern. Instead, I see an initial dramatic drop followed by a slow rise to an asymptote. The simulation computes the mean of 20,000 simulated trajectories

Hi list, I am trying to calculate the sensitivity, specificity, and odds ratio(and confidence interval). Say, my data looks like this 42.53, 37.56, 40.51, 32.67, 38.19, 81.74, 41.55, 68.94, 59, 63, 54.13, 48.85, 50.46, 51.78 Is there any packages in R that does this? Espeically the odds ratio confidence interval. Mike [[alternative HTML version deleted]]

Can you provide the summary(m2) results? > -----Original Message----- > From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] > Sent: Wednesday, August 16, 2006 7:14 AM > To: Doran, Harold > Cc: r-help at stat.math.ethz.ch > Subject: [SPAM] - RE: [R] REML with random slopes and random > intercepts giving strange results - Bayesian Filter detected spam > > Hi again,

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 a simple question i want to run simulations in r. i however want the experiments to be repeated at a later time with exactly the same numbers by other users. can i set the random number seed for rnorm in some way? e.g. is there some arguement that goes with rnorm? please supply an example regards Allan

Dear list, I was curious how to subtract a vector from matrix? Say, I have mat <- matrix(1:40, nrow=20, ncol=2) x <-c(1,2) I want, x-mat[1,] and x-mat[2,], and so on... Basically, subtract column elements of x against column elements in mat. But x-mat won't do it. Thanks, Mike [[alternative HTML version deleted]]

Hi, I am trying to do a bayesian prediction for soil pollution data above a certain threshold, using geoR. Everything is working fine until i am doing the krig.bayes. I tried to do the prediction on a grid 67 by 113 cells and my computer is freezing to death. At larger numbers of cells it tells me after a while that it reaches the max. memory of 511 Mb. My computer has only 512 Mb of RAM.

Greetings, I have a simulation of a nonlinear model that is failing. But it does not fail til way into the simulation. I would like to look at the run that is failing and maybe I could if I could capture the seed for the failing run. The help file on set.seed says you can do it but when I tried rs<-.Random.seed print(paste("rs",rs,sep=" ")) I got 626 of them so I

Greetings I have been experimenting with sampling from posterior distributions using R. Assume that I have the following observations from a normal distribution, with an unscaled joint likelihood function: normsamples = rnorm(1000,8,3) joint_likelihood = function(observations, mean, sigma){ return((sigma ^ (-1 * length(observations))) * exp(-0.5 * sum( ((observations - mean ) ^ 2)) / (sigma

Hi listers, I am working on a simulation... But I am having some troubles... Suppose I have a function A which produces two results (mean and variance)... Then I would like to simulate this function A with a function B many times using the results from function A For example: #Function A boot<-function(a,b,c){ mean_boot<-(a+b)/2 var_boot<-c #list(a=a,b=b,c=c) return(a) } Then I would

Thanks much Ted. I actually had just tried what you suggest here before you posted, and resolved the problem. Thanks also for the other tips. I wrote x = as.vector(c(1:12)) because I thought that the mode of x might be the problem, the error message pointing to .Random.seed notwithstanding. On a related note, I did a brief test a couple weeks back where I ran a million random samples of 3 from

Hi list, I am curious how to stop the set.seed(), I don't want the same repeated random number. I know I can set it to a different seed, but I don't want to go through the trouble of setting different seed every time. Thanks, Mike [[alternative HTML version deleted]]

I am a new R user. As a test, I want to write a simple code that does the following simulation: 1. Randomly generate a number from a distribution, say, Poisson. Let's say that number is 3. 2. Randomly generate 3 numbers from another distribution, say, Normal. 3. Compute the sum of the numbers generated in step 2 and read it into a vector, V. 4. Repeat steps 1 through 3 for 100,000 times. 5.

I need help with these R simulations _______________________________________ The generation of pseudo-random numbers with uniform distribution [0.1] can be done through a method called Congruencial Committee, which consists the use of a recursive relationship of the type: xi +1 = (a.xi + c) mod m being a, c, and the positive integers m, c <m. Discuss in light of possible values of a, c, my

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Dear R folks, I need to simulate first passage times for iterated partial sums. The related papers are for example [1][2]. As a start I want to simulate how long a simple random walk stays negative, which should result that it behaves like n^(-?). My code looks like this. -------- 8< -------- code -------- >8 -------- n = 100000 # number of simulations length = 100000 # length of

Hello. Douglas Bates has explained in a previous posting to R why he does not output residual degrees of freedom, F values and probabilities in the mixed model (lmer) function: because the usual degrees of freedom (obs - fixed df -1) are not exact and are really only upper bounds. I am interpreting what he said but I am not a professional statistician, so I might be getting this wrong... Does

Dear Listers, I am trying to use "rbinom" in my C code, but i always get zeros as output no matter the probability. Am not sure what I am doing wrong because the function has worked before. Attached in an example. Noticed that "rbinom" expects 'n' to be REAL. Regards, Vumani R 2.3.1 (2006-06-01) Windows XP Gcc /* Called this file binom.c and then ran rcmd shlib on it

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