similar to: DIC and MCMCpack

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

Dear R users, Thanks in advance. I am Deb, Statistician at NSW Department of Commerce, Sydney. I am using R 2.9.1 on Windows XP. This has reference to the package “MCMCpack”. My objective is to select a better model using various alternatives. I have provided here an example code from MCMCpack.pdf. The matrix of Bayes Factors is: model1 model2 model3 model1 1.000 14.08

Dear All 1) I'm fitting spatial CAR models using R2Winbugs and although everything seems to go reasonably well (or I think so) the next message appears from WINBUGS 1.4 window: gen.inits() Command #Bugs: gen.inits cannot be executed (is greyed out) The question is if this message means that something is wrong and the results are consequently wrong, or Can I assume it as a simple warning

Dear R users, I have to call fortran program from within R (R 2.8.1 on ubuntu 8.10 machine). Suppose I have a fortran code like this (this is only a toy model, my working model is far more complex, but input/output is similar) DOUBLE PRECISION FUNCTION model(times, alfa, beta) DOUBLE PRECISION alfa, beta, times model=beta*sin(times)+alfa*cos(times) END FUNCTION which

I'm completely new to R and am trying to install an add-on package for one of our faculty members. I've had no problems with most of them, but am completely stuck trying to figure out why MCMCpack won't install.. any help is VERY much appreciated!!! here's what I get when I try to install the package: * Installing *source* package 'MCMCpack' ... checking for C++ compiler

I tried calculating pD as 1/2 variance of the deviance, but I got hugely inflated numbers. Does anybody know how to calculate pD from the coda files output from R2WinBUGS? By the way, 'set DIC' is greyed out for some reason within WinBUGS, so I can't monitor DIC. [[alternative HTML version deleted]]

Hi list, I tried to install MCMCpack to R-2.12.0, got the following error, CC -m64 -library=stlport4 -I/apps/sparcv9/R-2.12.0/lib/R/include -DSCYTHE_COMPILE_DIRECT -DSCYTHE_DEBUG=0 -DHAVE_TRUNC -DHAVE_IEEEFP_H -I/opt/csw/include -KPIC -g -c MCMCSVDreg.cc -o MCMCSVDreg.o "error.h", line 598: Error: The function "abort" must have a prototype.

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Hi, I've been having problems getting MCMCpack installed on my Solaris 10 x86 system. I've installed gcc from opencsw.org, and, for the most part, other R packages install nicely. >From what I can gather, the build fails due to a mismatch of include files. I *think* it wants to use the include files that are contained within the MCMCpack package (but I could be wrong on this - if this

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 everyone, I am running an ordinal probit using the Bayesian MCMCpack and I am getting an error saying "attempt for find suitable starting values failed" Here is my code: > posterior <- MCMCoprobit(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 +x9 + x10 + x11 + x12 +x13 , beta.start=c(-10, 0.05, 0.02, 0.04, 0.98, 0.61, -0.29, 0.91, -0.82, 1.34, 0.68, 0.57, 0.09, 0.5), mcmc=10000)

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, 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

Op 9-2-2019 om 19:27 schreef Giacomo via dovecot: > I got a core file this morning. > > opening it with gdb I get this: > > (gdb) core imap.core > Core was generated by `dovecot/imap'. > Program terminated with signal 6, Aborted. > #0? 0x0000000011c1347a in ?? () > (gdb) bt > #0? 0x0000000011c1347a in ?? () > #1? 0x0000000011c13444 in ?? () > #2?

Dear all, Not a problem that is very specific to R, but I think that it is not only of interest to me... So I hope that someone finds the time to provide me with some clues on this probably rather basic issue. I'm performing an analysis of experimental data with a categorical response variable. Until now I have been using a classical maximum likelihood approach. The most basic output measure

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

I've just enabled core dumps on the involved FreeBSD system. Let's see if it dumps something.. Il giorno dom 20 gen 2019 alle ore 19:16 Stephan Bosch <stephan at rename-it.nl> ha scritto: > Hi Giacomo, > > Op 21/12/2018 om 16:16 schreef Giacomo: > > The bug happens not very often, it might need a week to get a core file. > > Any luck getting a core file? >

Dear R-help members, I'd like to run a binomial logistic stepwise regression with ten explanatory variables and as many interaction terms as R can handle. I'll come up with the right R command sooner or later, but my real question is whether and how the criterion for the evaluation of the different models can be set to be the probability of the residual deviance in the Chi-Square

I got a core file this morning. opening it with gdb I get this: (gdb) core imap.core Core was generated by `dovecot/imap'. Program terminated with signal 6, Aborted. #0 0x0000000011c1347a in ?? () (gdb) bt #0 0x0000000011c1347a in ?? () #1 0x0000000011c13444 in ?? () #2 0x0000000000018dee in ?? () #3 0x306db139575f3b0d in ?? () #4 0x00007fffffffc144 in ?? () #5 0x0000000000000000 in