similar to: Getting Started with Bayesian MCMC

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

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

I have been having trouble with rjags on Ubuntu 10.04 > library(rjags) Loading required package: coda Loading required package: lattice linking to JAGS 3.2.0 module basemod loaded Error : .onLoad failed in loadNamespace() for 'rjags', details: call: dyn.load(file) error: unable to load shared object '/usr/lib/JAGS/modules-3/bugs.so': /usr/lib/JAGS/modules-3/bugs.so:

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

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 folks Earlier versions of jags and rjags installed on my system without any difficulty, but I'm having trouble with jags version 4.3.0-1ubuntu2~ubuntu14.04.1~ppa1 (from Michael Rutter's R ppa) and rjags version 4.6. The call to install.packages() I always used successfully now produces the error "cannot link to JAGS library in /usr/lib/x86_64-linux-gnu."? shown below

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hi all. Are there any R functions around that do quick logistic regression with a Gaussian prior distribution on the coefficients? I just want posterior mode, not MCMC. (I'm using it as a step within an iterative imputation algorithm.) This isn't hard to do: each step of a glm iteration simply linearizes the derivative of the log-likelihood, and, at this point, essentially no

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

I am an economist who decided it's high time that I learned some Bayesian statistics. I am following An Introduction to Modern Bayesian Econometrics by T. Lancaster. The book recommends using BUGS, but I wonder if there are any alternatives which are free software and fully integrated to R (which I have been using for more than two years for numerical computations.) I would like to learn

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

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, 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

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]

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 all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

Has anybody had issues running MCMC (either BUGS or JAGS) on data sets of this magnitude (ie 30k x 20-30). I've been trying to run a hierarchical random effects model on expression data but R completely stalls out on jobs run on 32bit R on our server (doesn't respond...then eventually crashes out with an exception fault). Would using 64bit R help with JAGS? (As far as I know there's

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, With the sid package jags (2.0.0-1) and r-base-core (2.11.0-1), I cannot install package rjags anymore (this used to work at some point; can't recall exactly at what R/JAGS versions): ---<--------------------cut here---------------start------------------->--- R> install.packages("rjags", configure.args="--with-jags-modules=/usr/lib/JAGS/modules-2.0.0")