Fit a hierarchical or a fixed-effect model, using Bayeisan optimisation. We use a specific type of pMCMC algorithm, the DE-MCMC. This particular sampling method includes crossover and two different migration operators. The migration operators are similar to random-walk algorithm. They wouold be less efficient to find the target parameter space, if been used alone.
StartNewsamples(data, prior = NULL, nmc = 200, thin = 1,
nchain = NULL, report = 100, rp = 0.001, gammamult = 2.38,
pm0 = 0.05, pm1 = 0.05, block = TRUE, ncore = 1)run(samples, nmc = 500, thin = 1, report = 100, rp = 0.001,
gammamult = 2.38, pm0 = 0, pm1 = 0, block = TRUE, ncore = 1,
add = FALSE)
data model instance(s)
prior objects. For hierarchical model, this must be a list with three sets of prior distributions. Each is respectively named, "pprior", "location", and "scale".
number of Monte Carlo samples
thinning length
number of chains
progress report interval
tuning parameter 1
tuning parameter 2. This is the step size.
probability of migration type 0 (Hu & Tsui, 2010)
probability of migration type 1 (Turner et al., 2013)
Only for hierarchical modeling. A Boolean switch for update one parameter at a time
Only for non-hierarchical, fixed-effect models with many subjects.
posterior samples.
Boolean whether to add new samples