similar to: using mean substitution

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

I normally use the following code to create a figure displaying the mark correlation function for the point pattern process "A": M<-markcorr(A) plot(M) I have now started to use the following code to perform 1000 Monte Carlo simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test based on envelopes of the Mark correlation function obtained from simulated point

dear r-experts: I need to speed up my monte-carlo simulations. my code is written in R (and it was also the cause of my many questions here over the last few days). my code is almost all matrix/vector algebra on panel data sets---long-difference, fixed-effects, blundell-bond, etc.. the data set is about 10MB, so 1GB per CPU core should be plenty for my operations, and with $10/GB of

This is a message for whoever maintains "chisq.test": For an outcome more extreme than 2000 simulations, a Monte Carlo p-value of "< 2.2e-16" was printed. Ripley said the proper p-value for such cases should be 1/(B+1) = 1/2001. This can be easily fixed by adding "if(PVAL==0)PVAL <- 1/(B+1)" right after the following line in the code for chisq.test (in R

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,

Hi, Can anyone help me with the following. I have been using R for Monte Carlo simulations and got some results I couldn't explain. Therefor I performed following short test: -------------- mean.sds <- NULL sample.sizes <- 3:30 for(N in sample.sizes){ dum <- NULL for(I in 1:5000){ x <- rnorm(N,0,1) dum <- c(dum,sd(x)) } mean.sds<- c(mean.sds,mean(dum)) }

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to estimate the model. I

Hello, currently I'm working on a model based on Monte-Carlo-Simulations. I observed that a generated normal distributed times series using rnorm(100,mean=0,sd=1) is far away from being not autocorrelated. Is there any other gerenator implemented in R, which might solve my problem? -- View this message in context:

Dear all, I have a .dta database which is about 400 MB. I cannot open it though I have no problem to import smaller ones (80 MB or even 174 MB). I tried to modify some options with --max-mem-size=2047M --max-vsize=2047M. But it does not seem to be enough. I do not know the exact meaning of these options : vsize seems to be made for vectors. I have got Monte Carlo simulations running in another

List, I have looked through several R books and searched the web to find answers to my questions with no results. I have a ensembles of time series data (essentially from Monte Carlo simulations) which I would like to summarize as a time series of boxplots. I don't know how to do this and I am not sure how I should structure the data to get what I want. Another related question: while

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David

Dear list users, I need to learn about MCMC methods, and since there are several packages in R that deal with this subject, I want to use them. I want to buy a book (or more than one, if necessary) that satisfies the following requirements: - it teaches well MCMC methods; - it is easy to implement numerically the ideas of the book, and notation and concepts are similar to the corresponding R

Dear R users, I'm working with Windows 2000 and R -- note that I could maybe work with Linux too... I would like to know if it is possible to build a stand-alone statistical package which: -- could be programmed in R language, -- could have a nice graphical interface with buttons and menus (this package will be distributed to non-statistician people), -- could realize statistical operations,

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

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! I am running a very simple mini Monte-Carlo below using the function tstatistic (right below this sentence): tstatistic = function(x,y){ m=length(x) n=length(y) sp=sqrt( ((m-1)*sd(x)^2 + (n-1)*sd(y)^2)/(m+n-2) ) t=(mean(x)-mean(y))/(sp*sqrt(1/m+1/n)) return(t) } alpha=.1; m=10; n=10 # sets alpha, m, n - for run 1 N=10000 # sets the number of simulations n.reject=0 # counter of num.

Dear Wine-friends, I was wondering if any of you would have a clue around why WinBUGS (http://www.mrc-bsu.cam.ac.uk/bugs/), a nifty Markov chain Monte Carlo sampler widely used in Bayesian statistical modelling, no longer works when run through any version of WINE newer than 1.1.12. I have encountered this issue of my machines at home (which runs on Zenwalk 6.0) and work (Mandriva 2008.1) a

Is there anyone who can help me with the following? It's hard to describe in a few words, but hope you'll understand. I attempt to model refugee mortality over time using Monte Carlo. Before doing repetitive simulations, my function should go through only one survey/row at the time, but instead gives outcomes of all studies/rows, which is not correct, because it uses random numbers

> 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