similar to: Unexpected warnings from summary() on mcmc.list objects

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 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

Is there a way of running BUGS on OS X (from R)? I only see Windows versions on their website. If not, what are the alternatives for Bayesian analysis? Thanks, Tamas

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.

Hello, I am about to move all of my modelling work into R, and I have been investigating the present state of MCMC and Bayesian methods in R. Following a thread on the mailing list in 2000, I have looked at mcmcpack and Hydra. Three years down the line, is there anything new in this area? I have used both MCSim and WinBUGS in the past. The first one seems promising, but is too focused towards

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])

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, I used the MCMCirtKd function of MCMCpack: posterior2 <- MCMCirtKd(data, dimensions = 2, + burnin = 5000, mcmc = 50000, thin = 10, + verbose = 10000, B0 = .25, store.item = TRUE, item.constraints = beta.constraints) And after apply the comand summary() I got some erros and warnings that I could not understand: summary.posterior2 <- summary(posterior2)

i am having problem running my own data. yesterday it was working just fine. today it is not. this is the code i was using as an example to follow. this code ALSO worked just fine yesterday, and is no longer working at all. i suspect it is a problem with either my computer or the software, at this point. if THIS won't even run.... something is wrong. i can assure you this isn't

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

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 all, I would like to compute ergodic mean using MCMC output from WinBUGS. I tried using CODA package, but it seems that it is not implemented yet. Could anyone help me to compute this? Attached to this email are my output and index files. Kind regards, Raquel -- Raquel Rangel de Meireles Guimar?es Doutoranda em Demografia raquel at cedeplar.ufmg.br

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, 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

Dear all, I'm not 100% sure if this question is best directed at the r-list, or a mailing list concerned with Bayesian analysis, so please accept my apologies if another audience may be more appropriate. I have been using the rjags package to run Jags models with multiple chains and store the results in a Coda based mcmc list. For instance, having created a jags model and done initial

Hello, In WinBUGS 1.4 manual (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf), the gamma density is presented as dgamma(r,mu) where r and mu are the shape and rate parameters, respectively. In JAGS (rjags) manual version 1.0.2, May 9, 2008 (http://www-fis.iarc.fr/~martyn/software/jags/jags_user_manual.pdf), on page 26 the gamma density is presented as dgamma(mu,r) instead of dgamma(r,mu).

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

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

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

Hello, I implemented the Beta binomial distribution following the patterns of the binomial distribution code and inspired by JAGS' code [1]. I have studied the code carefully but it's my first run in the R internals. Can somebody review the code and if everything it's ok commit to the repository? [1]