similar to: Generating Data Sets -- Each with a Different Name

Hello all. I was wondering if the logLike.nls() and logLike.nlme() functions are still being used. Neither function seems to be available in the most recent release of R (1.9.1). The following is contained in the help file for logLik(): "classes which already have methods for this function include: 'glm', 'lm', 'nls' and 'gls', 'lme' and others in

Hello, I am trying to interface in my teaching some elementary probability with Monte Carlo ideas. In sampling from a finite population, the number of distinct samples of size 'k' from a population of size 'n' , when individuals are selected with replacement and the selection order does not matter, is choose(n + k -1, k). Does anyone have a suggestion about how to simulate

Hi all, I am trying to implement a Monte-Carlo simulation for each cell in a spatial matrix (using mcd2 package) . I have figured out how to conduct the simulation using data from a single location (where I manually input distribution parameters into the R code), but am having trouble (a) adjusting the code to pull input variables from my various data sets and then (b) applying the entire

Hi, We have recently published a paper on floating point accuracy analysis through Monte Carlo Arithmetic. We also released the open-source tool Verificarlo (https://github.com/verificarlo/verificarlo) that relies on LLVM for instrumenting floating point operations. Could you please add our paper to http://llvm.org/pubs/ ? Verificarlo: checking floating point accuracy through Monte Carlo

Hi All, I am using canonical correspondence analysis to compare a community composition matrix to a matrix of sample spatial relationships and environmental variables. In order to parse out how much variance is explained purely by space (S/E) or the environment (E/S) I am using a conditional (partial) CCA. I want to test significance via Monte Carlo but I can not find a way to do this with a

Hello Dear, I am trying to generate samples by using Monte Carlo simulation. For example, 1000 samples, Exponential distribution (f(x), lambda=0.0005, 0<=x<=360) Is there any package for Monte Carlo or just use random sample generation function? Many thank you for your help in advance, Jin -- View this message in context:

Hello all, I would like to know what the difference is between chisq.test and fisher.test when using the Monte Carlo method with simulate.p.value=TRUE? Thank you -- View this message in context: http://r.789695.n4.nabble.com/Difference-in-Monte-Carlo-calculation-between-chisq-test-and-fisher-test-tp2322494p2322494.html Sent from the R help mailing list archive at Nabble.com.

> Dear R experts, >I am sorry for my inability. >I have the following dataset: > Qtot Itot >1 73 684 >2 64 451 >3 71 378 >4 65 284 >5 47 179 >6 31 117 >7 19 69 > >Now I need to perform Monte Carlo Pertutation test underlaying the following condition. > > >Condition > >In order to choose randomly (5000 times) for the Qtot

Hello, How can I make a monte carlo simulation on R? Regards Adel -- PhD candidate in Computer Science Address 3 avenue lamine, cité ezzahra, Sousse 4000 Tunisia tel: +216 97 246 706 (+33640302046 jusqu'au 15/6) fax: +216 71 391 166 [[alternative HTML version deleted]]

I'm a R beginner. Recently I conduct a project about expected return that involves using R for calculations. I encounter three questions as follow: Q1. How to use R to demonstrate bootstrapping and Monte Carlo simulation under 10,000 times of random selection? Q2. How to do a loop in R? Q3. How to use R to do binominal tree calculation under multiple periods? I'm perplexed by the

How many distinct values can rnorm return? I assume that rnorm manipulates runif in some way, runif uses the Mersenne Twister, which has a period of 2^19937 - 1. Given that runif returns a 64 bit precision floating point number in [0,1], the actual period of the Mersenne Twister in a finite precision world must be significantly less. One of the arguments for Monte Carlo over the bootstrap is

Hi there, I am running Monte Carlo Simulations in R using ordinary "while (condition)" loops. Since the number of iterations is something like 100.000 and within each iteration a given subsample is extended sequentially it takes hours to run the simulation. Does anyone know if there is either a way to avoid using loops in Monte Carlo Simulations or how to include possible faster

Hi all, I'm using the code below within a loop that I run thousands of times and even with the super-computing resources at my disposal this is just too slow. The snippet below takes about 10s on my machines, which is an order of magnitude or two slower than would be preferable; in the end I'd like to set the number of monte carlo experiments to 1e4 or even 1e5 to ensure stable

Hi, I created several figures and their titles should appear like this: Figure 1: Monte Carlo results for alternative estimators of structural parameters (N = 100, T = 5) Because N and T change across figures, my code includes the following lines: N.set <- 100 T.set <- 5 mtext(“Figure 1: Monte Carlo results for alternative estimators of structural

Hi, R-helpers, I'm trying to use R to do a Monte Carlo simulation and need the help. What I have is a matrix that consists of the probabilities for the persons to choose zones. For example, in the matrix shown below, each column represents a person, and each row represents a zone. So, the probability that the first person will choose the 2nd zone is 30%. 25% 30% 10% 30% 20% 0% 20% 50% 60%

Hi all, I'm looking for an analytic method to obtain the PDF & CDF of the sampling distribution of a given correlation (rho) at a given sample size (N). I've attached code describing a monte carlo method of achieving this, and while it is relatively fast, an analytic solution would obviously be optimal. get.cors <- function(i, x, y, N){ end=i*N

HI R-users, I am trying to repeat an example from Rayner and Best "A contingency table approach to nonparametric testing (Chapter 7, Ice cream example). In their book they calculate Durbin's statistic, D1, a dispersion statistics, D2, and a residual. P-values for each statistic is calculated from a chi-square distribution and also Monte Carlo p-values. I have found similar p-values

.... Un código más optimizado para la aproximación Monte Carlo de la distribución de IH N=10000 # tamaño simulación Monte Carlo n=5 # numero de uniformes cdf.IH <-function(x,n,N) { z=replicate(N,sum(runif(n))) apply(outer(x,z,">="),1,mean) } x=seq(0,5,.1) y=cdf.IH(x,n=n,N=N) plot(x,y,type="l") Un saludo. Olivier ----- Mensaje original ----- De:

Dear R Users, Does anyone know of a package which can generate random realisations of a double-exponential jump diffusion process with a drift ? Something where I can specify the likelihoods of an up or a down jump, the drift rate, and the mean size, and get back a vector of realisation of the process (for purposes of a Monte-Carlo). Kind regards, Tolga

Hello, I am trying to simulate binary outcome data for a logistic regression Monte Carlo study. I need to eventually be able to manipulate the structure of the error term to give groups of observations a random effect. Right now I am just doing a very basic set up to make sure I can recover the parameters properly. I am running into trouble with the code below. It works if you take out the object