similar to: What does the warning message exactly say?

Dear R lists, I ran Winbugs under R. I could get the results, but I kept getting the error messages: Error in file(con, "wb") : cannot open the connection In addition: Warning messages: 1: In file.create(to[okay]) : cannot create file 'c:/Program Files/WinBUGS14//System/Rsrc/Registry_Rsave.odc', reason

Dear R lists, I ran Winbugs under R. I could get the results, but I kept getting the error messages: Error in file(con, "wb") : cannot open the connection In addition: Warning messages: 1: In file.create(to[okay]) : cannot create file 'c:/Program Files/WinBUGS14//System/Rsrc/Registry_Rsave.odc', reason

Dear R-help lists: Hello! I ran WinBUGS under R using the function "bugs". But I kept having the following error message: Error in FUN(X[[3L]], ...) : .C(..): 'type' must be "real" for this format Why did I have the error message?What's the problem?How to get rid of the error message? Thank you very much for your help! Best wishes Zhongjun Lin

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

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 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

Dear Sir/Madam, I am currently doing a project which need to use Bayesian Model. I need to use WINBUGS to do MCMC calculation, and therefore I need to between R and WINBUGS. I tried to submitted install.packages("R2WinBUGS") from R, but the system says, "Warning: unable to access index for repository...". I am not sure what happened with it. Could anyone tell me

Dear all I used R2WinBUGS package's bugs() function to generate MCMC results. Then I tried to save the simulation draws in R, using read.bugs() function. Here is a simple test: ###################### library(coda) library(R2WinBUGS) #fake some data to test beta0=1 beta1=1.5 beta2=-1 beta3=2 N=200 x1=rnorm(N, mean=0,sd=1) x2=rnorm(N, mean=0,sd=1) x3=rnorm(N, mean=0,sd=1) lambda2= exp(beta0+

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

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?)?

I'm trying to understand how a latent state matrix is updated by the MCMC iterations in a WinBUGS model, using the package R2WinBUGS and an example from Kery and Schaub's (2012) book, "Bayesian Population Analysis Using WinBUGS". The example I'm using is 7.3.1. from a chapter on the Cormack-Jolly-Seber model. Some excerpted code is included at the end of this message;

Dear Members, I have a question about using R2WinBUGS to obtain the WinBUGS results. By default, when R2WinBUGS returns summary stats, I got mean, sd, 2.5%, 25%, median, 75% and 97.5%. Could anyone tell me how to modify the code to obtain 5% and 95% summary results? Many thanks Alice _________________________________________________________________ [[elided Hotmail spam]]

Dear R users, I´m studying about Bayesian Statistics. In this context, please, anyone have some basic script of RJMCMC (Reversible Jump Markov chain Monte Carlo) in R or WinBUGS? My aim is to learn how to implement this methodology. Thanks a lot. Marcus Vinicius [[alternative HTML version deleted]]

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.

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

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

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])