similar to: Simulation in R

I must to create an array with dimensions 120x8x500. Better I have to make 500 simulations of 8 series of return from a multivariate normal distribution. there's the command "mvrnorm" but how I can do this repeating the simulation 500 times?" [[alternative HTML version deleted]]

Dear friends, regression towards the mean is interesting in medical circles, and a very recent paper (The American Statistician November 2007;61:302-307 by Krause and Pinheiro) treats it at length. An initial example specifies (p 303): "Consider the following example: we draw 100 samples from a bivariate Normal distribution with X0~N(0,1), X1~N(0,1) and cov(X0,X1)=0.7, We then calculate

Dear List: A few weeks ago I posted some questions regarding data simulation and received some very helpful comments, thank you. I have modified my code accordingly and have made some progress. However, I now am facing a new challenge along similar lines. I am attempting to simulate 250 datasets and then run the data through a linear model. I use rm() and gc() as I move along to clean up the

Hi there, I would like to draw 10 correlations from a bivariate population - but every draw should be done with a different sample size. I thought I could to this with a loop: r=numeric(10) #Goal vector N = c(1000,100,80,250,125,375,90,211,160,540) #Sample size vector for(i in 1:10) { data <- mvrnorm(n=N,mu=c(0,0),Sigma=matrix(c(1,.3,.3,1),2)) r[i] <- cor(data[,1],data[,2]) }

Hi, list I am doing 100,000 iterations of Bayesian simulations. What I did is I split it into 4 different R sessions, each one runs 25,000 iteration. But two of the sessions gave the simulation result. I did not use any set.seed(). What is going on here? Thanks, Mike [[alternative HTML version deleted]]

Dear experts! Is there a package that enables to create the bivariate log-normal variables? Thanks a lot, Vicky Landsman. [[alternative HTML version deleted]]

To whom it may concern, I try to simulate a non-normal multivariate distribution. The MASS package allows by mean of "mvrnorm" command to perform a multivariate normal simulation. Is there an equivalent command for an arbitrary multivariate distribution available in the R-language? Thank you in advance. Bernard Colin Colin Bernard Professeur titulaire D?partement de Math?matiques et

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Hi, I need to generate two normal variables with covariance matrix: 0.25, 0.20 0.20, 0.25 but I have no idea how to do that. Can anyone help me? Thanks, Joaquim ---------------------------------------------------------------------------- ---------- Joaquim J. S. Ramalho University of Bristol Department of Economics 8 Woodland Road Bristol BS8 1TN United Kingdom

Hello, I used the following codes to generate bivariate normal dependence structure with unit Frechet margins. Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate y <- mvrnorm(Nsam, c(0,0), Sigma) # random v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean = 0, sd = 1)) z <- cbind(-1/log(v[,1]),-1/log(v[,2])) z1 <- z[,1] z2 <- z[,2] And to

Dear everyone, I am having problem simulating multivariate data. Though I was able to simulate the data, but finding the variance-covariance matrix of simulated data did not give exact covariance matrix used in simulating the data. Unlike some other packages, like stata, using command "corr2data" will simulate data having the covariance matrix exactly with the specified covariance

Dear R users, I need to run 1000 simulations to find maximum likelihood estimates. I print my output as a vector. However, it is taking too long. I am running 50 simulations at a time and it is taking me 30 minutes. Once I tried to run 200 simulations at once, after 2 hours I stopped it and saw that only about 40 of them are simulated in those 2 hours. Is there any way to make my simulations

Dear R users,   I need to do a F test on the hypothesis that a 2 by 1 vector (X_1, X_2)' has the mean vector  (M_1, M_2)'. Specifically, I would like to assume the X vector comes from a bivariate Normal distribution (M, Sigma). Then, given 1000 observations on X, I wanted to test if the means of X agree with the means of the target Normal distribution. Any function or package in R could do

Dear helpers, As the title says, my question is not directly related to R. I find, however, that there are many people who are both knowledgeable and kind in this email list, and so decided to give it a try. I do stochastic simulations. Parameter values used in simulation often come from the observations of the real word phenomena. Parameter values are often given as "rates" (of

Dear R-Help members, I have a question about how to save to the hard drive the one thousand datasets I generated in a simulation. The datasets are created in a "for" loop that repeatedly creates normally distributed datasets, such as the example below: Library(MASS) for (number in 1:1000) { dataset = mvrnorm(n = 400, mu = c(0,0,0), Sigma =

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

I tried looking for help but I couldn't locate the exact solution. I have data that has several variables. I want to do several sample simulations using only two of the variables (eg: say you have data between people and properties owned. You only want to check how many in the samples will come up with bicycles) to estimate probabilities and that sort of thing. Now, I can only do a simulation

Does anyone have any R code or suggestions on how to generate a population that follows a Neyman-Scott process? More specifically, I'd like to randomly generate both the parents and the children from Poisson distributions and the children locations from a bivariate normal onto an N by N grid. If the locations spill over outside the grid, then I'd like to reflect those locations back into

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Dear?All, ? a few weeks ago I have posted a question on the R help listserv that?some of you have responded to with a great solution, would like to thank you for that? again.?I thought I would reach out to you with the issue I am trying to solve now. I have posted the question a few days ago, but probably it was not?clear enough, so I thought i try it again.?At times I have a multivariate example