similar to: Memory(RAM) issues

Hi there, Ive tried trawling the lists for a solution but I could not find any matches. I am typing up all my code on a Linux machine and I call this other script file from my script file using source("foo.r"). Now sometimes i access my folder from my Windows machine at work (the files are on dropbox). But of course my windows machine would not understand the linux path name. Is

Hi, Im not sure if this is the right place to post this. I am using Xubuntu Karmic Koala and am trying to use the Rcpp package. I am testing it using a simple code that takes in a vector and adds 1 to each element: #include <Rcpp.h> // This file takes in a vector and adds one to each entry RcppExport SEXP addone(SEXP vec){ // create a local copy of vec Rcpp::NumericVector

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

I am writing a Gibbs sampler. I think it is outputting some of what I want, in that I am getting vector of several thousand values (but not 10,000) in a txt file at the end. My question is, is the error message (see below) telling me that it can't output 10,000 values (draws) because of a limitation in my memory, file size, shape etc, or that there is an error in the sampler itself? >

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),       

Hey everyone, I have read the paper by Ritter and Tanner(1992) on Griddy-Gibbs sampler and I am trying to implement it in R without much luck. I was wondering if anyone had used this or could point me to any example code. Thanks, Liz --------------------------------- [[alternative HTML version deleted]]

Hello everyone, I have some binary, spatially autocorrelated data I would like to run autologistic regression on. I hope to incorporate both ordinary covariates (environmental predictors) and a spatial autocovariate in the model, ideally with a second-order neighbourhood structure. Since my computing skills are limited, I am wondering if anyone has composed an algorithm for this purpose, and

I have the following code, gibbs <-function(m,theta = 0.25, lambda =0.55, n =1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:(m+1)){ x[t,1] <- rbinom(1, x[t-1,3], x[t-1,1]) x[t,2]<-rbeta(1, x[t-1,1] + alpha, x[t-1,3] - x[t-1,1] + beta) x[t,3]

Dear all: I am using R to do multiple imputation for longitudinal data set. The Gibbs chain basically requires draw posterior distribution of model parameters, including the random effects. The multiple imputation requires several independent Gibbs chains. So my program structure is like: for (chain in 1:5) { # perform Gibbs sampling... for (row in 1:row.no) { b.row=some function # draw

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

Hello ALL! Some times ago I started to learn and play with Bayesian stuffs. Many advice use of WinBUGS for Bayesian inference Using Gibbs Sampler. However, WinBUGS is discontinued, and now, development is under OpenBUGS. I wasn't lazy, so I installed both and tried out. In more than 90% of cases they give comparable outcome. But in few cases I got substantial differences. Recently, I read nice

Hello all. I'm developing a package for R holding a Gibbs sampler, which tends to have better performance when written in C than in R. During each iteration in the Gibbs sampler, I need the inverse of a symmetric matrix. For this, I wish to use lapack, as is concisely suggested in "Writing R extensions", since this will have better performance than I could ever write myself. After

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

Hi all, I am trying to run rejection sampling for the quantity z11 in the function below. Unfortunately I can't simplify the function further so that z11 only appears once. Whenever I run the algorithm, R looks as if it is running it (no error messages or anything), but then nothing happens for minutes...how long should it take to run something like this in R? I have tried in in both linux

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

Hello everyone! First of all, please note that I'm working under Windows 7. I have written a Gibbs sampler in R and I'm now in the process of translating it in C++ to increase the speed. I'm relatively new to C++ and this question may be trivial, but my search so far have been unsuccessful. In my Gibbs sampler I am using some basic R functions (like the rep function for example)

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Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose