similar to: problem installing MCMCpack

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

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

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")

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 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'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 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 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

Dear R gurus, I am interested in permutations-based cpu-intensive methods so I had to pay a little attention to Random Number Generators (RNG). For my needs, RNGs have to: 1) be fast. I profiled my algorithms, and for some the bottleneck was the RNG. 2) be scalable. Meaning that I want the RNG to remain fast as I add threads. 3) offer a long cycle length. Some basic generators have a

Dear R users, I am leaning MCMC sampling, and have a problem while trying to sample exponential r.v.'s via the following code: samp <- MCMCmetrop1R(dexp, theta.init=1, rate=2, mcmc=5000, burnin=500, thin=10, verbose=500, logfun=FALSE) I tried other distribtions such as Normal, Gamma with shape>1, it works perfectly fine. Can someon

Hello, I am trying to install rjags on R 3.0.0 that is running on CentOS 6.4. Jags is installed and seems to be working correctly. I get the following error when trying to install from source or CRAN, configure: error: "Problem with header file /usr/include/JAGS/Console.h " See `config.log' for more details JAGS was intalled with an rpm. Does not look like the rpm included

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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 =

r-help, The R manual lists two types of memory: transient and user-controlled. If I have transient blocks reachable from the globals only by traversal through user-controlled blocks, will they be correctly preserved? Secondly, what are the ways to mark user controlled blocks as "roots" for the garbage collector, so that transient blocks they reference stay uncollected? So far I

r-help, The R manual lists two types of memory: transient and user-controlled. If I have transient blocks reachable from the globals only by traversal through user-controlled blocks, will they be correctly preserved? Secondly, what are the ways to mark user controlled blocks as "roots" for the garbage collector, so that transient blocks they reference stay uncollected? So far I