similar to: about MCMC pack again...

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,

I'm having some problems in installing some source packages under linux. As an example, MCMCpack. An error is raised when linking: > install.packages("MCMCpack") [...] * Installing *source* package 'MCMCpack' ... checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking

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, does anyone know something about missing data in the MCMC pack? thank you, Mariagiulia Mariagiulia Matteucci Dipartimento di Scienze Statistiche ?Paolo Fortunati? Universit? di Bologna Via Belle Arti, 41 40126 Bologna (ITALY) e-mail: matteucci at stat.unibo.it TEL: +39 051 264182 FAX: +39 051 232153

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

Hi there, I am running MCMCpack (MCMCirt1d model) on some files (26 items x about 800 – 1200 individuals). I have a problem. When I am working on “big” files, the R program crashes. More precisely I got the following Microsoft Warning: ******************************************* Microsoft Visual C++ Runtime Library Runtime Error! Program: C:\Program Files\R\R-2.4.1\bin\Rgui.exe

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

Hello, I'd like to perform a regression using MCMCregress (MCMCpack). One variable therefore should be a function rather than a variable: I want to use X as an input and X should be defined as a random number between to values. Therefore I want to use the function runif like: X <-(1, Xa, Xb) but it seems that runif doesn't allow to use vectors. So I think I've to calculate the new

Hi, I used the MCMCirtKd function of MCMCpack: posterior2 <- MCMCirtKd(data, dimensions = 2, + burnin = 5000, mcmc = 50000, thin = 10, + verbose = 10000, B0 = .25, store.item = TRUE, item.constraints = beta.constraints) And after apply the comand summary() I got some erros and warnings that I could not understand: summary.posterior2 <- summary(posterior2)

Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is

Hi, We're running samba-1.9.18p8 on a few different platforms (mostly IRIX and AIX.) I have noticed that we don't seem to have supplemental group support. We have a group file shared by NIS, and some directories are controlled by group. For example: We have a share here called cs whose path is /home/cs/common. Under that directory we have /home/cs/common/ta. The "ta"

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the