run.jags(model, monitor = NA, data = NA, n.chains = NA, inits = NA, burnin = 4000, sample = 10000, adapt = 1000, noread.monitor = NULL, datalist = NA, initlist = NA, jags = runjags.getOption(jagspath), silent.jags = runjags.getOption(silent.jags), modules = runjags.getOption(modules), factories = runjags.getOption(factories), summarise = TRUE,.
So R2jags::jags is compiling the model with jags.model, adapting it with adapt, then iterating and sampling from the posterior with coda.samples. Your two calls aren’t exactly equivalent. In the first you: compile and adapt with jags.model, update for 100 iterations with update, then, run.jags (model, monitor = NA, data = NA, n.chains = NA, inits = NA, burnin = 4000, sample = 10000, adapt = 1000, noread.monitor = NULL, datalist = NA, initlist = NA, jags = runjags.getOption (jagspath), silent.jags = runjags.getOption (silent.jags), modules = runjags.getOption (modules), factories = runjags.getOption (factories), summarise = TRUE,.
4/15/2016 · The run.jags call starts the sampling by JAGS, using 1000 steps to adapt the model and 4000 steps to burn-in (defaults). After that, the model is run for 10000 iterations. The results are given as a table (truncated):, # Create, initialize, and adapt the model: jagsModel = jags.model( TEMPmodel.txt , data = dataList, inits = initsList, n.chains = nChains, n.adapt = adaptSteps) # Burn-in: cat( Burning in.
7/5/2017 · Welcome to the JAGS forums 149 Help. Formatting Help Iterations for burnin, sample, and adapt . …
8/9/2012 · The dgamma function in JAGS/BUGS and R is parameterized by shape and rate parameters, not by mean, mode, or sd. Therefore we must reparameterize the shape and rate into equivalent mean, mode or sd. If we want to reparameterize by the mean of the gamma distribution, a JAGS/BUGS model statement could look like this: model { for ( i in 1:N )
