similar to: Which BUGS should one use?

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

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, 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

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?)?

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

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])

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.

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

I'm new to bugs, so please bear with me. Can someone tell me if the following two models are doing the same thing? The reason I ask is that with the same data, the first (based on 4 separate coeffs a1--a4) takes about 50 secs, while the second (based on a vectorized form, a[]) takes about 300. The means are about the same, though R-hat's in the second version are quite a bit better.

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

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

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

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 using the dump command to pass data to WinBUGS/OpenBUGS/JAGS and have run into a problem. Here is some R-code: foo <- array(1:6, dim=c(2,3)) dump('foo', file='dumpdata.R') dump('foo', file='dumpdata.R', append=TRUE, evaluate=TRUE) foo2 <- array(c(2,3,5,7,9,7,5,3), dim=c(2,4)) dump('foo2', file='dumpdata.R', append=TRUE) And here is

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

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

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

Greetings I have been experimenting with sampling from posterior distributions using R. Assume that I have the following observations from a normal distribution, with an unscaled joint likelihood function: normsamples = rnorm(1000,8,3) joint_likelihood = function(observations, mean, sigma){ return((sigma ^ (-1 * length(observations))) * exp(-0.5 * sum( ((observations - mean ) ^ 2)) / (sigma