similar to: mcmc simulation

Hi Ajay: A casual search of google using terms like "MCMC", "Markov Chains", "Monte Carlo", and "tutorial" pulled up over 4000 hits. A few links that may interest you, I thought, like: http://csep1.phy.ornl.gov/CSEP/MC/MC.html http://www.stat.fi/isi99/proceedings/arkisto/varasto/gree0167.pdf http://www.maths.soton.ac.uk/staff/Sahu/utrecht/ ... and so

Dear list users, I need to learn about MCMC methods, and since there are several packages in R that deal with this subject, I want to use them. I want to buy a book (or more than one, if necessary) that satisfies the following requirements: - it teaches well MCMC methods; - it is easy to implement numerically the ideas of the book, and notation and concepts are similar to the corresponding R

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

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

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

Package: mcgibbsit Title: Warnes and Raftery's MCGibbsit MCMC diagnostic Version: 1.0 Author: Gregory R. Warnes <gregory_r_warnes at groton.pfizer.com> Description: mcgibbsit provides an implementation of Warnes & Raftery's MCGibbsit run-length diagnostic for a set of (not-necessarily independent) MCMC sampers. It combines the estimate error-bounding approach of Raftery

Package: mcgibbsit Title: Warnes and Raftery's MCGibbsit MCMC diagnostic Version: 1.0 Author: Gregory R. Warnes <gregory_r_warnes at groton.pfizer.com> Description: mcgibbsit provides an implementation of Warnes & Raftery's MCGibbsit run-length diagnostic for a set of (not-necessarily independent) MCMC sampers. It combines the estimate error-bounding approach of Raftery

Hello R Programmers, We have a large existing Perl codebase which is currently being upgraded to use the new optimizing RPerl compiler (not related to the R language). http://rperl.org/ Also in use by the same Perl codebase is some custom R code which loads the "bayesm" library in order to execute Markov Chain Monte Carlo (MCMC) calculations.

(apologies if this looks like a re-post, I just sent a similar message to the r-help mail list. This version is via Nabble.) My intended application is error propagation using the ISO GUM Supplement 1 approach (propagation of distributions using Monte Carlo strategies). To automate uncertainty analysis I typically have the following data: (1) a measurement function y(x1,x2,...xn) (2) 'n'

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,

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

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

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

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 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]]

Hi, Forgive me if I seem naive, I'm tackling multivariate stats for the first time! Q. I'd like to know if there are packages that can be used to simulate random draws from general multivariate (joint) PDF functions when ONLY the independent marginal PDFs are known (RV means and covariance or correlation matrix)? Q. I see there is a Markov Chain Monte Carlo package, but the mcmc

NIMBLE version 0.6-2 has been released on CRAN and at r-nimble.org. NIMBLE is a system that allows you to: - Write general hierarchical statistical models in BUGS code and create a corresponding model object to use in R. - Build Markov chain Monte Carlo (MCMC), particle filters, Monte Carlo Expectation Maximization (MCEM), or write generic algorithms that can be applied to any model. -

NIMBLE version 0.6-2 has been released on CRAN and at r-nimble.org. NIMBLE is a system that allows you to: - Write general hierarchical statistical models in BUGS code and create a corresponding model object to use in R. - Build Markov chain Monte Carlo (MCMC), particle filters, Monte Carlo Expectation Maximization (MCEM), or write generic algorithms that can be applied to any model. -

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

Hi all, I'm trying to run some monte carlo simulation for my roll call data using the ideal() function, which resides in the pscl package. However, I'm receiving an error message that I don't understand. Error in ideal(a, maxiter = 1000, thin = 10, burnin = 50, store.item = TRUE, : NA/NaN/Inf in foreign function call (arg 13) my code is simple the following: > m_a <-