output.mcmc {BPEC} | R Documentation |
Provides various MCMC tuning parameters, as well as posterior samples for convergence assessment.
output.mcmc(bpecout) ## S3 method for class 'bpec' output.mcmc(bpecout)
bpecout |
R object from |
MCMCparams |
Various MCMC tuning parameters, useful for development. |
codaInput |
Posterior samples from the two MCMC chains for the cluster means, cluster covariance entries, as well as the root haplotype. Note that, since the number of clusters varies from iteration to iteration, some samples are simply draws from the prior (corresponding to empty clusters). This variable can be loaded directly into the |
Ioanna Manolopoulou & Axel Hille
## if you want to load the `mini' example Brown Frog dataset data(MacrocnemisRawSeqs) data(MacrocnemisCoordsLocsMini) rawSeqs <- MacrocnemisRawSeqs coordsLocs <- MacrocnemisCoordsLocsMini dims <- 3 #this is 2 if you only have geographical longitude/latitude. #(add 1 for each environmental or phenotypic covariate) maxMig <- 2 #you will need a higher maximum number of migrations, suggest 7 ds <- 0 #start with ds=0 and increase to 1 and then to 2 iter <- 1000 #you will need far more iterations for convergence, start with 100,000 postSamples <- 100 #you will need at least 100 saved posterior samples #run the Markov chain Monte Carlo sampler bpecout <- bpec.mcmc(rawSeqs,coordsLocs,maxMig,iter,ds,postSamples,dims) output.mcmc(bpecout)