search for: mcmcmetrop1r

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...

...w much support the data give to each model parameter) * parameter interdependency (to somehow measure effective model complexity) Both I would extract from the MCMC-trace. Sounds simple? It possibly is - just not for me. I compared several MCMC algorithms implemented in R, from Win/OpenBUGS over MCMCmetrop1R (MCMCpack; my current favourite) and metrop (mcmc) to gibbs and rwmetrop (LearnBayes) and gibbs_met (gibbs.met). These implementations differ dramatically in efficiency (MCMCmetrop1R was over 20 times faster than gibbs_met). Since my functions can be complex (mainly ODEs, complex environmental...

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 tell me why? Thank you for your help. Best, Zhongyi [[...

...-I/usr/lib/R/include -DSCYTHE_COMPILE_DIRECT -DSCYTHE_NO_RANGE -c distributions.cc -o distributions.o [...etc. etc. All compilations are ok] g++ -o MCMCpack.so distributions.o ide.o la.o lecuyer.o MCMCdistn.o MCMCdynamicEI.o MCMCfactanal.o MCMCfcds.o MCMChierEI.o MCMCirt1d.o MCMClogit.o MCMCmetrop1R.o MCMCmixfactanal.o MCMCmnlMH.o MCMCmnlslice.o MCMCoprobit.o MCMCordfactanal.o MCMCpanel.o MCMCpoisson.o MCMCprobit.o MCMCprobitres.o MCMCregress.o MCMCrng.o MCMCtobit.o mersenne.o optimize.o rng.o smath.o stat.o -L/usr/lib/R/lib -lR /usr/lib/gcc-lib/i486-linux/3.3.5/../../../crt1.o(.text+0x18):...

Can you write an R function to generate from N samples from the given Gibbs algorithm. Also we must repeat the study for different N values and different L values .And plot iterations vs F1 and iteration vs F2. F1, F2 ~N2 (0, ( 1 L ) ) 1 L --------------------------------- Send a seasonal email greeting and help others. Do good. [[alternative

A naive question from a non-statistician: I'm looking into running a Bayesian analysis of a model with high dimensionality. It's not a standard model (the likelihood requires a lot of code to implement), and I'm using a Linux machine. Was wondering if someone has any thoughts on what the advantages of OpenBugs are as opposed to just R (or should I be thinking WinBUGS under Wine?)?

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible