similar to: MCMC object indexing

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")

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:

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 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

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

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,

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

I have been checking my package coda with the current R-devel using "R CMD check". As usual, this has uncovered a large number of errors and inconsistencies in my documentation, for which I am very grateful. The only problem I have is with code/documentation mismatches when I have written a method for a generic function, e.g. * checking for code/documentation mismatches ... WARNING

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 R users: I am using the package coda (the last verison in CRAN) to analyse the output from a MCMC Bayesian analysis. And I get unconsitented results. I have export the chain using the read.table function and after I have transformed this data frame to an mcmc object using the mcmc function. I am interested in three variables, when I use the function effectiveSize I have these figures:

i am having problem running my own data. yesterday it was working just fine. today it is not. this is the code i was using as an example to follow. this code ALSO worked just fine yesterday, and is no longer working at all. i suspect it is a problem with either my computer or the software, at this point. if THIS won't even run.... something is wrong. i can assure you this isn't

Hello All, I'm trying to run a one-dimenional irt model using the packages MCMC and coda on a rather large set of roll-call voting data with many missing observations. Here's a sample of the code: Post10<- MCMCirt1d (Italy10, burnin = 1000, mcmc=50000, thin=100, verbose=TRUE, theta.constraints = list(V549=1, V443=-1)) The MCMCirt1d command seems to work fine, but when I try to

Dear all, I just wrote a script to have each of my three mcmc chins running on a different computer core to improve computation speed. To do it I use the function sfLapply from the package snowfall. Before using parallell computation, I did my diagnostic analyses just after the model updating, but now I have to save the coda before. Using the CODA package, it is possible to load the outputs in a

Hi All, I am trying to figure out how to use R-Coda with the output from OpenBugs. I have installed and loaded the packages BRugs and R2WinBUGS. I have successfully run a simple Bayes model in WinBUGS using R2WinBUGS' "bugs" and have used "read.bugs" to build the coda object. I can successfully switch to OpenBugs and run the same model and get the basic summary

Hi all, I would like to compute ergodic mean using MCMC output from WinBUGS. I tried using CODA package, but it seems that it is not implemented yet. Could anyone help me to compute this? Attached to this email are my output and index files. Kind regards, Raquel -- Raquel Rangel de Meireles Guimar?es Doutoranda em Demografia raquel at cedeplar.ufmg.br

Dear all, I'm not 100% sure if this question is best directed at the r-list, or a mailing list concerned with Bayesian analysis, so please accept my apologies if another audience may be more appropriate. I have been using the rjags package to run Jags models with multiple chains and store the results in a Coda based mcmc list. For instance, having created a jags model and done initial

I can take the results of a simulation with one random variable and generate an empirical interval that contains 95% of the observations, e.g., x <- rnorm(10000) quantile(x,probs=c(0.025,0.975)) Is there an R function that can take the results from two random variables and generate an empirical ellipse that contains 95% of the observations, e.g., x <- rnorm(10000) y <- rnorm(10000) ?

Hi folks, This is really minor but to someone not familiar with the various tentacles of the lmer package it could be really annoying. I was trying to plot the posterior density of the fixed effect parameters of a lmer model, > hr.mcmc = mcmcsamp(hr.lmer, n=50000) > densityplot(hr.mcmc, plot.points=F) There is this error, "Error in densityplot(hr.mcmc, plot.points = F) : no

The document R-exts contains the following example of using mathematics in R documentation. \deqn{p(x) = {\lambda^x\ \frac{e^{-\lambda}}{x!}} {p(x) = lambda^x exp(-lambda)/x!} There is a syntax error in there, but that's not my point. The problem is that using "R CMD Rd2dvi" I find that putting the alternate forms of the equation on top of each other doesn't work.

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