similar to: Where is package "Umacs"?

Hello all... I am just starting to teach myself Bayesian methods, and am interested in learning how to use UMacs. I've read the documentation, but the single example is a bit over my head at the level I am at right now. I was wondering if anyone has any simple examples they'd like to share. I've successfully done a couple of simple gibbs examples, but have had a hard time

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),       

Dear R users, I would like to fit cross classified or multiple membership logistic models or a 3 level hierarchical logistic model using the Umacs package. Can anyone advise me on how to proceed or better point me to examples of how its done. Regards, -- Luwis Diya, Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Kapucijnenvoer 35 blok d - bus 7001, 3000 Leuven,

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?)?

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

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, When i try to inser this on LDAP database, i get this error: "ldapadd: invalid format (line 14) entry: "uid=spessoa,ou=users,ou=accounts,dc=telbit,dc=pt"" I can't see nothing wrong. The .ldif file follows my signature. Any help would be appreciated. Warm Regards, M?rio Gamito -- dn: dc=telbit,dc=pt objectClass: top objectClass: dcObject objectClass: organization o:

I'm testing some different formulations of pairwise interaction point processes in Spatstat (version 1.11-6) using R 2.5.0 on a Windows platform and I wish to simulate them using the Metropolis-Hastings algorithm implemented with Spatstat. Spatstat utilizes Fortran77 code with the preprocessor RatFor to do the Metropolis-Hastings MCMC, but the Makefile is more complicated than any I have

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

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

Hi, Do any body know which packages of R I need to go for the below topics? 1. Monte Carlo Markov chain (MCMC) 2. Gibbs Sampling 3. Metropolis Hastings Thanks in advance... Shubha [[alternative HTML version deleted]]

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Hello all, could you tell where is an example of metropolis hasting? Thank you! Tania Sent from my iPod

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 everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

Hello all, I have a large parameter problem with the following very simple likelihood function: fn<-function(param) { x1<-param[1:n] g1<-param[(n+1):(2*n)] beta<-param[(2*n+1):(2*n+k)] sigma2<-param[2*n+k+1]^2 meang1sp<-mean(g1[sp]) mu<-beta%*%matrix(x1,1,n)-(g1[sp]-meang1sp)%*%matrix(g1,1,n) return(sum((ydc-mu)^2)/(2*sigma2) + n*k*log(sqrt(sigma2)) +

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Hi list, I am writing several functions and running out variable names. I am using words such as "t", "c", "matrix" to keep the notation same as formulas I am using. For example I have, unnormalized <- function(t, x, y){ val <- rnorm(t, mean=x, var=y) return(val) } metropolis <- function(t, c, x, y){ den1 <- unnormalized(t, mean=x, sd=y)

Hello, I have written a simple Metropolis-Hastings MCMC algorithm for a binomial parameter: MHastings = function(n,p0,d){ theta = c() theta[1] = p0 t =1 while(t<=n){ phi = log(theta[t]/(1-theta[t])) phisim = phi + rnorm(1,0,d) thetasim = exp(phisim)/(1+exp(phisim)) r = (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8) if(runif(1,0,1)<r){ theta[t+1] = thetasim }

On 03/11/16 19:37, Volker Lendecke wrote: > On Thu, Nov 03, 2016 at 07:11:12PM +0000, Alex Crow via samba wrote: >> >> On 03/11/16 14:28, Ralph Böhme wrote: >>>> Ah, my smb.conf is missing the "ctdb socket" parameter. According to "man >>>> smb.conf" this should be set, but I don't remember seeing it on the wiki >>>> pages.