similar to: R vs. Bugs

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

Dear R-helpers, Hi! All. I'm doing a project which needs MCMC simulation. I wonder whether there exists related packages in R. The only one I know is a MCMCpack package. What I want to do is implementing gibbs sampling and Metropolis-Hastings Algorithm to get the posterior of hierarchical bayesian models. Thanks in advance. Jun

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

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

Hi, I am running a model with count data and one categorical predictor (simple model for me to understand it fully), I did in R a glm like this: glm(Recruitment~Depth, family=poisson). I get the coefficientes and confidence intervals and all is ok. But then I want to do the same model with Bayesian stats, here is my code: model { for (i in 1:232) { Recruitment[i]~dpois(lambda[i])

Hello ALL! I am running Linux, Fedora 15 64-bits, and R on it. I need to use WinBUGS and R2WinBUGS, but as far as I read, WinBUGS is closed project, to be continued with/as OpenBUGS. Thus, I have found R2OpenBUGS on OpenBUGS Contributed Code (http://openbugs.info/w/UserContributedCode), not on CRAN. Author(s) states that it is equivalent for R2WinBUGS. I tried briefly, and realized few minor

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

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

I'm back! I've just learned that, on a fully updated Fedora Core Linux5 sytem, the working solution to access Winbugs under wine via the R package "rbugs" no longer works. Here was my last post on this topic (with the formerly working solution) from January. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/68497.html Currently, what happens is that WinBUGS starts up, but just

Greetings: I'm going to encourage some students to try Bayesian ideas for hierarchical models. I want to run the WinBUGS and R examples in Tony Lancaster's An Introduction to Modern Bayesian Econometrics. That features MS Windows and "bugs" from R2WinBUGS. Today, I want to ask how people are doing this in Linux? I have found a plethora of possibilities, some of which are not

I am trying to figure out if it is possible to run winBUGS from within R, using R2winBUGS, without having winBUGS spawn any windows (basically - 'true' batch - no GUI actions at all). The reason being I have a machine which I (and several others) ssh/telnet into, and would like to run winBUGS without having to mount a virtual desktop of any kind. I've looked through the r2winBUGS

Hi, I'm running wine-1.0.1, OpenBUGS 3.0.3, R 2.9.0, and R2WinBUGS on a Redhat Enterprise Linux machine. Following various peoples' suggestions... This works perfectly (yay!): wine Z:/opt/OpenBUGS/winbugs.exe Within R, however, I get this: (setup the example from ?bugs, then....) R> schools.sim <- bugs(data, inits, parameters, model.file, n.chains=3,

Hi, I have a strange behaviour of openBUGS and WinBUGS when I start them from R. Version: R: 2.10.1 openBUGS: 3.07 WinBUGS: 1.43 R2WinBUGS: 2.1-16 BRugs: 0.5-3 I have a model and data without initial start values. If I use the stand alone versions of openBUGS and WinBUGS I don't have any problems and I get what I want. If I use function bugs() from R2WinBUGS- resp. BRugs-Library then

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

I am trying to get summary statistics from WinBUGS/JAGS output in the form of mcmc.list objects, using the summary() function. However, I get odd warning messages: Warning messages: 1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did not converge 2: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did

Hi all, I observed that the 'link' between R and BUGS (winBugs/linBugs) has totally disappeared. I'm wondering what package can be use to run bayesian models specified using the BUGS language in R (specifically under Linux). Is there any other option besides JAGS ? Thanks ----------------------------------------- Stay ahead of the information curve. Receive EDA news and jobs on

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

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

This weekend I noticed that my R2WinBUGS connection was no longer working on my Windows computer at work AND my Ubuntu linux computer at home. As soon as WinBUGS opens, the message reads Index Out Of Range. I have un-installed and re-installed R on both computers and the problem remains the same. At first I thought it was the new version of R, but I was able to overcome the problem (on Windows