similar to: MCMC validity question

I am trying to use the function 'filter.NA=TRUE' in Geneland. The function appears to be set on TRUE by default, as it appears as TRUE in the 'parameter.txt' file output and hence I do not need to enter the function per se (as it is an 'Unused argument otherwise') . Hence all my missing data (individuals that I have not yet scored at that specific loci) are scored as

Dear List members, I am using R to generate MCMC time series plots. This is the code I use; however, everytime an error message will come out saying could not find function "plot.mcmc." I have coda package and Lattice package installed. Does anyone know what may get wrong here? Thanks so much for your suggestions. Hongli Li *library (coda) dat1=read.csv("Itemtime.csv")

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

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

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

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

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,

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

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

I tried thinning of the mcmc run with 500,000 iteration. It looks like 100 or 200 is enough to remove the autocorrelation of a1 and tau. Is that too much thining? --- On Tue, 3/16/10, ping chen <chen1984612 at yahoo.com.cn> wrote: > From: ping chen <chen1984612 at yahoo.com.cn> > Subject: an ordinal regression MCMC run high correlation > To: r-help at r-project.org >

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 an mcmc object and I''m trying to plot the quantiles of the variables - and not as a function of the iterations as in cumuplot. I cannot seem to find the right combination of indexing to access the variables; after which I''m sure I can plot all the statistics I could hope for. Any hints for accessing the mcmc object would be appreciated. =Dave [[alternative HTML

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 am leaning MCMC sampling, and have a problem while trying to sample exponential r.v.'s via the following code: samp <- MCMCmetrop1R(dexp, theta.init=1, rate=2, mcmc=5000, burnin=500, thin=10, verbose=500, logfun=FALSE) I tried other distribtions such as Normal, Gamma with shape>1, it works perfectly fine. Can someon

Dear officer I have a question concerning running R when I am doing my research. Can you help me to figure that out? I am now running a MCMC iteration in the R program. But it is always stucked in some loop. This cause big problems for my research. So I want to know whether we can skip the current dataset and move to next simulated data when the iteration is stucked? Alternatively, can the MCMC

I am trying to get summary statistics from WinBUGS/JAGS output in the form of mcmc.list objects, using the summary() function. However, I get odd warning messages: Warning messages: 1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did not converge 2: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did

Hello Everyone I have a MCMC loop to calculate a time varying hierarchical Bayesian structure. This requires me to use around 5-6 matrix inversions in the loop. I use cholesky and chol2inv for the matrix decomposition. Because of the data I am working with I am required to invert a 167 by 167 matrix twice in one iteration. I need to run the iteration for 10000 times, but I get the error

The object is a mcmc sample from lmer. I am using R v2.7.1. Please let me know what additional information I can provide, hopefully I am just making a simple mistake. Thanks in advance! > data(ratdrink, package = 'faraway') > rd.er <- lmer(wt ~ weeks*treat + (1 | subject), data = ratdrink) > rd.mc <- mcmcsamp(rd.er, 10000) > library(coda) Loading required package:

Hi All, I'm preparing to co-teach a class on applied MCMC using R as the computational engine in the spring. As a consequence, I'm starting to look at MCMC textbooks. Please send along your recommendations! I will collate the responses and post a summary, so please reply to me directly. Thanks, -G