similar to: MCMC in R

A naive question from a non-statistician: I'm looking into running a Bayesian analysis of a model with high dimensionality. It's not a standard model (the likelihood requires a lot of code to implement), and I'm using a Linux machine. Was wondering if someone has any thoughts on what the advantages of OpenBugs are as opposed to just R (or should I be thinking WinBUGS under Wine?)?

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

Hello, I recently changed from Win XP to Mac OS X (10.5.1). Is there a way to run Bugs (in any version) in R (R version 2.6.0 (2007-10-03)) on this platform? Fredrik

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

Hello, Consider MCMC sampling with metropolis / metropolis hastings proposals and a density function with a given valid parameter space. How are MCMC proposals performed if the parameter could be located at the very extreme of the parameter space, or even 'beyond that' ? Example to express it and my very nontechnical 'beyond that': The von Mises distribution is a circular

Hi, Do any body know which packages of R I need to go for the below topics? 1. Monte Carlo Markov chain (MCMC) 2. Gibbs Sampling 3. Metropolis Hastings Thanks in advance... Shubha [[alternative HTML version deleted]]

Hello, I have written a simple Metropolis-Hastings MCMC algorithm for a binomial parameter: MHastings = function(n,p0,d){ theta = c() theta[1] = p0 t =1 while(t<=n){ phi = log(theta[t]/(1-theta[t])) phisim = phi + rnorm(1,0,d) thetasim = exp(phisim)/(1+exp(phisim)) r = (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8) if(runif(1,0,1)<r){ theta[t+1] = thetasim }

Hi All, I am trying to figure out how to use R-Coda with the output from OpenBugs. I have installed and loaded the packages BRugs and R2WinBUGS. I have successfully run a simple Bayes model in WinBUGS using R2WinBUGS' "bugs" and have used "read.bugs" to build the coda object. I can successfully switch to OpenBugs and run the same model and get the basic summary

I'm testing some different formulations of pairwise interaction point processes in Spatstat (version 1.11-6) using R 2.5.0 on a Windows platform and I wish to simulate them using the Metropolis-Hastings algorithm implemented with Spatstat. Spatstat utilizes Fortran77 code with the preprocessor RatFor to do the Metropolis-Hastings MCMC, but the Makefile is more complicated than any I have

Frank (or anyone else), can you offer any comments comparing runjags, R2jags, rjags ? I couldn't find any vignettes, nothing except a brief mention on Task Views. Kevin On Wed, Oct 7, 2009 at 7:48 AM, Frank E Harrell Jr <f.harrell@vanderbilt.edu > wrote: > Have you tried the rjags package which uses the jags system? It is much > more integrated into R and works quite well.

Hi all, I've managed to get JAGS working on my Ubuntu Hardy Linux with a 32-bit computer and AMD processors using R 2.8.1. JAGS is great. I've read that JAGS is the fastest, but that hasn't been my experience. At any rate, I have more experience with WinBUGS under Windows and would like a version of that working as well. It seems like I've read a lot on the subject and tried a

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Hello. In this post: http://finzi.psych.upenn.edu/Rhelp10/2010-March/233815.html Uwe Ligges suggests using BRugs rather than R2WinBUGS under windows. He also notes that it is not in the main CRAN repository, but it is in "extras" which is a default repository under windows. I have OpenBUGS 3.1.0 (the latest that has a native Linux version which is no longer called linBUGS)

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

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

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

Hola, buenas a todos: Me dispongo a empezar un proyecto con logística bayesiana y sé que hay varios paquetes que puedo utilizar, arm, Bayelogit y MCMCpack. Me preguntaba si alguien tiene experiencia con éstos paquetes y me pueda decir cuál de ellos es mejor. Además de esto quería saber si sabíais alguna bibliografía para informarme mejor sobre lógica bayesiana, tengo algunas referencias,

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is