similar to: MCMCpack combining chains

Hi, A question. When I run gelman.diag and gelman.plot with mcmc lists obtained from MCMCregress, the results are following. > post.R <- MCMCregress(Size~Age+Status, data = data, burnin = 5000, mcmc = 100000, + thin = 10, verbose = FALSE, beta.start = NA, sigma2.start = NA, + b0 = 0, B0 = 0, nu = 0.001, delta = 0.001) > post1.R <- MCMCregress(Size~Age+Status, data

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

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

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

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

Hi, I've been testing some models with the MCMCpack library. I can run the process and get a nice model "object". I can easily see the summary and even plot it. I can't seem to figure out how to: 1) Access the final coefficients in the model 2) Turn the coefficients into a model so I can then run predictions using them. A summary command will SHOW Me the coefficients, but

Hello everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

Greetings, I'm having some trouble with the realtime engines. When asterisk loads, everything looks fine, there don't seem to be any problems via notices or anything. Furthermore, cdr_odbc is working, and actively logging my failed call attempts to db through ODBC using the same DSN. unixODBC and the mysql drivers are installed from source. Here are the relevant parts of the config:

What possible factors can cause a 2 gig file size limit ? I've verified that the underlying filesystem can handle much larger files. I'm using the standard samba package from Debian testing, version is 2.2.3a-12. Please cc me on responses. Thanks in advance ! -- Josh Litherland (fauxpas@temp123.org) -------------- next part -------------- A non-text attachment was scrubbed... Name:

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

Hello to R people Does anybody know to calculate exact logistic regression in R? Does such option exist anywhere? Surprisingly, could not find it using search engine. It is hard to believe, however, that such useful function is not implemented in R yet? Could you help, please Thank you Denis

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hi all, I am having problems using the R2Winbugs function When I perform an analysis directly in Winbugs I can specify that the first 'n' iterations are to be done using an 'adaptive' phase. After this phase the markov chain seems to mix a lot better. I don't seem to be able to specify R2winbugs to carry out this adaptive phase, I can just specify the burnin length but

I'm getting oops whenever I pull a big file off of an ext3 filesystem on my large LV. The kernel this comes from happens to have lvm 1.0.2 and posix ACL for ext2/3 patched in, but I get the crash even on vanilla 2.4.17. kymoops 2.4.3 on i686 2.4.17-acl-lvm. Options used -V (default) -k /proc/ksyms (default) -l /proc/modules (default) -o /lib/modules/2.4.17-acl-lvm/

Does the wins libnss module not "work" yet, or am I misconfiguring it ? Everything else in the whole samba/winbind realm works, I can log in as a domain user using pam_winbind, smbclient can resolve netbios names from WINS, etc etc. but 'ping NETBIOS_NAME' doesn't resolve. Here's some background data: Samba 2.2.8-0.1 on debian from master.samba.org apt repos. wbinfo -N

Hello! I have the following problem. My code: --snip-- ergebnisse <- rep(0, each=2) stichproben <- rep(0, each=2) for (i in seq(1:2)) { n <- dim(daten)[1] ix <- sample(n,200) # producing samples samp_i <- daten[ix,] stichproben[i] <- samp_i # doesn???t works # Calculation of the model: posterior_i <- MCMClogit(y ~ fbl.ind + fekq3 + febitda4 + fuvs + fkru +

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

Dear All, I have a MCMC result in x1. I was wondering if there is a simpler, more elegant way of evaluating the estimate of an integral then this (I am pretty sure there is): Also if I want to count the x's say -1<x<2 but not the ones in the burn in period. [code] z <- -2 burnin <- 2000 int1 <-

I hope some of the authors of the package MCMCpack read this. I don't know if there is a way to set the response in the model formula of MCMClogit other than a (numeric) response vector. I think the MCMClogit in the MCMCpack needs some development so that the response in the formula could be set as a two-column matrix with the columns giving the numbers of successes and failures (just as in