similar to: Installing MCMCpack on Solaris 10 X86

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

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.

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

I installed R-2.3.1 and ran make check without problem, but I am having trouble installing several packages using gcc (GCC) 3.2.3 20030502 (Red Hat Linux 3.2.3-53) on Red Hat Enterprise Linux AS release 3 (Taroon Update 7) Kernel 2.4.21-40.ELsmp on an x86_64 Below are the messages from MCMCpack. Perhaps my systems are messed up? Paul Gilbert > install.packages("MCMCpack")

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 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

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

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 R users, I am using the package MCMCpack and I would compute the Deviance Information Criterion for selecting models, instead of using bayes factors. I know that a similar question have been sent previously to the list by others, but I have found no response. Can somebody help me? best Giacomo

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

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,

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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

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

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 =

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 10.04.2014 13:08 Laine Stump wrote: > On 04/06/2014 08:33 AM, Johannes Kastl wrote: >> There was a bug in libvirt for quite awhile that caused >> locally-unresolved requests for hostnames in the domain given by >> the network's <domain name='xyzzy'/> element to be dropped rather >> than forwarded. Is

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

Full_Name: Lloyd Lubet Version: 2.4.1rc OS: XP Submission from: (NULL) (65.19.17.17) Dear Andrew, I am trying to learn gibbs sampling and Metropolis. When I run your gibbs version of multiple linear regression my session crashes. I have trimmed the dataframe and set my object.size = 1 gigB. Despite my best efforts it still crashes while advising me to contact the MCMC Pack developement team.

Hi all, I'm currently implementing quasi random generation (torus) on R (package randtoolbox available on CRAN). Even if it is not a great idea, I decided to use the machine time to initiate the seed. So when the seed is not specified by the user, the pkg uses the time machine time. Hence the following R code should produce different uniform variates : > for(i in

I'm just starting to learn to use R, and although I'm seeing lots of functions aimed at doing orthodox statistical analyses, I don't see the same for Bayesian analyses. What support does R have for Bayesian statistics?