similar to: Calculating DIC from MCMC output

Dear All 1) I'm fitting spatial CAR models using R2Winbugs and although everything seems to go reasonably well (or I think so) the next message appears from WINBUGS 1.4 window: gen.inits() Command #Bugs: gen.inits cannot be executed (is greyed out) The question is if this message means that something is wrong and the results are consequently wrong, or Can I assume it as a simple warning

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

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

I am receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Dear all, I tried to run WinBUGS model in R by using package R2WinBUGS, but I failed because of the massage : cannot calculate DIC for model in log file of WinBUGS14. In fact, the model works in WinBUGS and I can get summaries of the model, such as density, stats..., except DIC , because DIC may not be appropriate in this kind of model due to some reasons(according to the manual of WinBUGS14),

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

I have been running a Gibbs Sampler to estimate levels of efficiency in the Louisiana Shrimp Industry. I created a matrix (samp) where I stored the results of each iteration for 86 variables. I run 10,000 iterations. So, the matrix samp is 10,000 x 86. I want to use the gelman-rubin test to check for convergence. To do that, I need at least two chains. If I run second chain with different starting

Hi, I??m trying to use the Win2BUGS package from R and I have a similar problem that reurns with the message: Error in FUN(X[[1]], ...) : symbol print-name too long But, there is no stray ` character in the file ( Sugestions given by: Duncan Temple Lang <duncan> Date: Mon, 26 Sep 2005 07:31:08 -0700 ) The progam in R is: library(R2WinBUGS) library(rbugs) dat <-

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: Is there a R-function that can generate samples from the posterior distribution of an ordered logistic regression model (just like MCMCoprobit from MCMCpack in R). I will greatly appreciate some guidance in this regard. Thanks Reez

Hello, I tryed to use the MCMC pack, particularly the function MCMCirtKd to simulate the posterior distribution in a multidimensional IRT model. The code I used is: posterior1 <- MCMCirtKd(Y, dimensions=2, item.constraints=list("V2"=list(3,0)), burnin = 1000, mcmc = 10000, thin=1, verbose = 1, seed = NA, alphabeta.start = NA, b0 = 0, B0=0, store.item = FALSE,

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

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

I tried calculating pD as 1/2 variance of the deviance, but I got hugely inflated numbers. Does anybody know how to calculate pD from the coda files output from R2WinBUGS? By the way, 'set DIC' is greyed out for some reason within WinBUGS, so I can't monitor DIC. [[alternative HTML version deleted]]

I'm trying to call BUGS from R. But it's not working. R freezes up and BUGS gives me a strange output in the log. Just to know, BUGS is registered. The modified date on the keys file is today (Dec. 9th). It should be fully registered so that I can use it fully. And, the BUGS model is syntactically correct. Any suggestions would be very helpful. Here is my BUGS model: model {

Hello, I am doing mcmc=10000 simulations from a posterior distribution of the parameters of a mixture of K=6 normal densities. I have mcmc by K matrices simMeans, simVars and simWeights containing the simulation output: one row for each simulation, one column for each normal component of the mixture. One thing I would like to do is a plot of the posterior predictive density. In order to do that

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <-

Hello, I have quite a tough problem, which might be able to be solved by MCMC. I am fairly new to MCMC (in the learning process) - so apologize if the answer is totally obvious, and any hints, links etc are greatly appreciated. I'll illustrate the problem in a version cut-down to the essentials - the real problem is ways more complex. Suppose I have a Markovian series of poisson