similar to: Support for Bayesian statistics in R

Full_Name: Kevin S. Van Horn Version: 1.7.1 OS: Linux Submission from: (NULL) (12.209.149.192) I used help.start() to get the web help displayed in my browser (Mozilla 1.21, on a RedHat Linux 9.0 system). I then clicked on "Search Engine and Keywords", and searched on "optimize". The resulting web page had a number of links. The first time I clicked on one these links it

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 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 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){

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

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

Dear all, I am trying to learn Bayesian inference and Bayesian data analysis, I am new in the field. Would any experts on the list recommend any good sites or materials for beginners? My approach is to learn and understand the theory first, then program on my own using R, though I see there are already packages. appreciate any help, thanks in advance!

Dear R users, I've found bugs.R : the functions for running WinBUGs from R that is writen by Dr. Andrew Gelman who is a professor from Columbia University. The bugs.R would be very useful for me, and I think many of you know it as well. I followed the instuctions on Dr. Gelman's web to install all of documents that bugs.R needs, but when I try to run the school example the web posted in

Dear useRs, I have written a function that implements a Bayesian method to compare a patient's score on two tasks with that of a small control group, as described in Crawford, J. and Garthwaite, P. (2007). Comparison of a single case to a control or normative sample in neuropsychology: Development of a bayesian approach. Cognitive Neuropsychology, 24(4):343?372. The function (see

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 While I'm not an R expert, I have used R on Windows XP. Now I've moved to Ubuntu (Intrepid), and I'm trying to configure R to work with the Gelman and Hill _Data Analysis Using Regression and Multilevel/Hierarchical Models_. So far, it's not working. I start by following the instructions for installing arm and BRugs at

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, I am trying to use the following code to reproduce results from Prof. Gelman's book, but have the listed error for R2WinBUGS version (the openbugs version is good). I am using R-2.6.1 on windows XP, and all the R packages are most current ones. schools.bug can be found at http://www.stat.columbia.edu/~gelman/bugsR/runningbugs.html . Can anyone help me to figure out what's

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

Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from

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

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

Hi all, May i know does R has packages or code to run "Bayesian Relative Survival Analysis"? I have look through Bayesian Survival Analysis(2001) by Joseph George Ibrahim<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Joseph+George+Ibrahim%22>, Ming-Hui Chen<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Ming-Hui+Chen%22>, Debajyoti

HeLLo, I'm new in R, I would like to use it for diagnostics with bayesian network, I want to know how can I calculate inference and if can I enter my probability tables like logical or arithmetic equation, Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Bayesian-network-tp4662059.html Sent from the R help mailing list archive at Nabble.com.

I am trying to calculate Bayesian Credible Intervals for a proportion (disease prevalence values to be more specific) and am having trouble using R to do this. I am working with ncredint() function but have not had success with it. Please help! Example: Positive samples = 3 Total sampled = 10 Prevalence = 0.3 pvec <- seq(1,10,by=1) npost = dbinom(pvec,10,prob=0.3, log=FALSE) ncredint(pvec,

Hi R bayesians, I need an advise how to resolve the two different estimates applying a traditional glm (TG) and a bayes glm (BG), and different results depending on the data formats of response data and the prior specs using bayesglm in R. I'm not familiar with bayes estimate and my colleague asked me to look into this because the EPA from France reported a quite different estimates for