similar to: books about MCMC to use MCMC R packages?

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to estimate the model. I

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of Science in Statistics at Mindanao State University-Iligan Institute Technology (MSU-IIT), Philippines. I am currently working on my master's thesis titled "Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain

Hello all, could you tell wehere I can find information related to MCMC (Monte Carlo). And some examples about this topic. Thanks, Tania [[alternative HTML version deleted]]

I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod. I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the

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

I have hit a problem with the design of the mcmc package I can't figure out, possibly because I don't really understand the R function call mechanism. The function metrop in the mcmc package has a ... argument that it passes to one or two user-supplied functions, which are other arguments to metrop. When the two functions don't have the same arguments, this doesn't work.

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

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 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]]

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Dear colleagues, I'm an epidemiologist with no background in programming and just started using R a few weeks ago. I am working on the epidemiology of African sleeping sickness, and am trying to use R to perform a Monte Carlo Markov Chain analysis to estimate three unknown parameters within a model of African sleeping sickness case detection that is mainly informed by actual field programme

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

I do the these passages: library(Geneland) set.seed(1) data <- simdata(nindiv=200, coord.lim=c(0,1,0,1) , number.nuclei=5 , allele.numbers=rep(10,20), IBD=FALSE, npop=2, give.tess.grid=FALSE) geno <- data$genotypes coord <- t(data$coord.indiv) path.mcmc <-

Hello all, I am currently attempting to replicate data from a political science article that utilized a Markov chain transition model to predict voter turnout intention at time *t*; the data was separated into two different models based on whether prior intent was to vote or not to vote. The details don't really matter. Mostly I am curious how to run a Markov chain transition model in R,

hi there! how u doin?...hope fine. look, i've a difficulty in simulation especially for the bayesian analysis using R. Using the famous Markov Chain Monte Carlo (MCMC) method, Methropolis hasting algorithm i want to simuate different distrbution like: 1/ Weibull-exponential 2/Gamma-Gamma 3/Poisson-Gamma 4/Normal-Normal 5/Normal-Gamma 6/ Negative binomial - Jeffrey's prior Especially, i

Hi, Is there a way to plot non numerical data in R? Specifically, I have an array, say with 1000 entries, where each entry is a string of 4 characters (in any order, 24 possibilities in my case). I would like on the y-axis all the strings that are in the array as labels. The x-axis I would like labeled 0 to 1000. The line is to show how the strings change as we move through the array.

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David

Hi, Does anybody know a function to simulate a Markov chain given a probability transition matrix and an initial state ? Thanks. Philippe -- -------------------------------------------------- Philippe Hup? Institut Curie - Equipe Bioinformatique 26, rue d'Ulm - 75005 PARIS France +33 (0)1 42 34 65 29 Philippe.Hupe at curie.fr <mailto:Philippe.Hupe at curie.fr>

Hello, I am a chemical student and I make use of 'lme/lmer function' to handle experiments in split-plot structures. I know about the mcmcsamp and I think that it's very promissory. I would like knowing "the concept behind" of the mcmcsamp function. I do not want the C code of the MCMCSAMP function. I would like to get the "pseudo-algorithm" to understanding that

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss