similar to: monte carlo simulation

Hi, please use the following the matrix z as the example: x<-c(2,4,5,7,6,9,8,2,0) y<-matrix(x,3,3) z<-apply(y,2,function(x)x/sum(x)) z On Tue, Mar 23, 2010 at 6:59 PM, David Winsemius <dwinsemius@comcast.net>wrote: > > On Mar 23, 2010, at 9:05 PM, Hongwei Dong wrote: > > Hi, R-helpers, >> >> I'm trying to use R to do a Monte Carlo simulation and

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%

Dear R helpers Suppose I have a portfolio of securities with exposure to Equity, Bonds and Forex (say $ 1000000 each). Is there any fucntion in R that will help me calculate Value at Risk (VaR) using Monte carlo Simulation , Historic simulation and Variance - Covariance Simulation. With regards Maithili

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]]

Hi, Dear R users, I'm wondering if I can do Monte Carlo Simulation in R. My problem is like this: I know variable X follows Gamma distribution with shape parameter 0.067 and scale parameter 0.008. The sum of the X is 2000. I need R help me to simulate a vector of X that satisfies both the probability distribution and the sum. Anyone has a clue to this? Much appreciated. Regards Garry

I use fGarch package to estimate AR(1)-ARCH(1) process for a vector of returns. Then, using the estimated parameters I want to simulate 10 000 sample paths where each path has the same length as the vector of returns. So the first line of the code is: spec=garchSpec(model=list(ar= 0.440270860, omega=0.000374365,alpha=0.475446583 , mu=0, beta=0))---- The only way I can think of generating 10 000

Exactly! Thanks, Duncan. Let me re-phrase me question like this: 1) X_i values are independent Gammas, with the shape 0.067 and scale 0.008 2) Min(X)=1 and Max(X)=85 3) SUM(X)=2000 4) Do I also have to define the number of draws? if yes, it could be 250. Based on these restrictions, I want to generate random draw. I'm wondering how I can do this in R. Thanks. Garry On Tue, Nov 10, 2009

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

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 - 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,

===== Summary ===== Version 0.1.0 provides the initial release of RcppSMC, an integration of the SMCTC template classes for Sequential Monte Carlo and Particle Filters (Johansen, 2009, J Statistical Software, 30:6) with the Rcpp package for R/C++ Integration (Eddelbuettel and Francois, 2011, J Statistical Software, 40:8). RcppSMC allows for easier and more direct access from R to the

===== Summary ===== Version 0.1.0 provides the initial release of RcppSMC, an integration of the SMCTC template classes for Sequential Monte Carlo and Particle Filters (Johansen, 2009, J Statistical Software, 30:6) with the Rcpp package for R/C++ Integration (Eddelbuettel and Francois, 2011, J Statistical Software, 40:8). RcppSMC allows for easier and more direct access from R to the

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:

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

Hi, This is an announcement for a package that has been up on CRAN since March 2006 but was never announced. The package is Mchtest - for Monte Carlo hypothesis tests allowing sequential stopping. The idea is to use the sequential probability ratio test boundaries to stop resampling for a Monte Carlo hypothesis test such as a bootstrap or permutation test. This means that you will take many

Hi, This is an announcement for a package that has been up on CRAN since March 2006 but was never announced. The package is Mchtest - for Monte Carlo hypothesis tests allowing sequential stopping. The idea is to use the sequential probability ratio test boundaries to stop resampling for a Monte Carlo hypothesis test such as a bootstrap or permutation test. This means that you will take many

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

> 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 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.

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