similar to: autologistic regression with Gibbs sampler

Dear all: I am using R to do multiple imputation for longitudinal data set. The Gibbs chain basically requires draw posterior distribution of model parameters, including the random effects. The multiple imputation requires several independent Gibbs chains. So my program structure is like: for (chain in 1:5) { # perform Gibbs sampling... for (row in 1:row.no) { b.row=some function # draw

I have the following code, gibbs <-function(m,theta = 0.25, lambda =0.55, n =1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:(m+1)){ x[t,1] <- rbinom(1, x[t-1,3], x[t-1,1]) x[t,2]<-rbeta(1, x[t-1,1] + alpha, x[t-1,3] - x[t-1,1] + beta) x[t,3]

I am writing a Gibbs sampler. I think it is outputting some of what I want, in that I am getting vector of several thousand values (but not 10,000) in a txt file at the end. My question is, is the error message (see below) telling me that it can't output 10,000 values (draws) because of a limitation in my memory, file size, shape etc, or that there is an error in the sampler itself? >

Hi, I have spatially autocorrelated data (with a binary response variable and continuous predictor variables). I believe I need to do an autologistic model, does anyone know a method for doing this in R? Many thanks C Bell

Hey everyone, I have read the paper by Ritter and Tanner(1992) on Griddy-Gibbs sampler and I am trying to implement it in R without much luck. I was wondering if anyone had used this or could point me to any example code. Thanks, Liz --------------------------------- [[alternative HTML version deleted]]

Hi, I am new to autologistic regression and R. I do have questions when starting a project in which I believe autologistic regression is needed. I have a point layer whose attribute table stores the values of the dependent variable and all the independent variables. I hope to to fit an autologistic model to analyze which factors or combinations of factors have effects on the presence/absence of

Dear all, Could you pleas advise me on the following? I need to use general(ized) linear models (binomial distribution + logit link function) , to describe the preferred environment of each species (each sample is an individual in which I have measured several variables and also recorded the species it belongs to) However, must account for the spatial autrefoocorrelation between

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),       

Hi R Users: This package provides two generic functions for performing Markov chain sampling in a naive way for a user-defined target distribution, which involves only continuous variables. The function "gibbs_met" performs Gibbs sampling with each 1-dimensional distribution sampled with Metropolis update using Gaussian proposal distribution centered at the previous state. The function

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

Sort of a warning before I start: This post may be considered to describe a rather amateurish approach to distributing software which may annoy some people, but I sincerely hope it doesn't. I've been working for some years with David Clayton on a project which started life as an S package but has now turned into an R library. It is (now) called GLMMGibbs and estimates the parameters of

hi i'm looking for a gibbs sampling algorithm for R for the case of mixture of K normals, and in particular for the case of bivariate normals. i'd be grateful if anyone could send its own R-routine, at least for the univariate case. thank you in advance matteo

Hi there, Just wondering why my post was rejected? cheersRachel Subject: repeating autocovariate functions From: r-help-owner@r-project.org To: moyble@hotmail.com Date: Sat, 2 Feb 2013 02:56:27 +0100 Message rejected by filter rule match --Forwarded Message Attachment-- Date: Fri, 1 Feb 2013 17:56:14 -0800 From: moyble@hotmail.com To: r-help@r-project.org Subject: repeating autocovariate

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Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

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

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

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

Hello ALL! Some times ago I started to learn and play with Bayesian stuffs. Many advice use of WinBUGS for Bayesian inference Using Gibbs Sampler. However, WinBUGS is discontinued, and now, development is under OpenBUGS. I wasn't lazy, so I installed both and tried out. In more than 90% of cases they give comparable outcome. But in few cases I got substantial differences. Recently, I read nice