StartNewsamples {ggdmc}

Start new model fits

Description

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.

Usage

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)

Arguments

data

data model instance(s)

prior

prior objects. For hierarchical model, this must be a list with three sets of prior distributions. Each is respectively named, "pprior", "location", and "scale".

nmc

number of Monte Carlo samples

thin

thinning length

nchain

number of chains

report

progress report interval

rp

tuning parameter 1

gammamult

tuning parameter 2. This is the step size.

pm0

probability of migration type 0 (Hu & Tsui, 2010)

pm1

probability of migration type 1 (Turner et al., 2013)

block

Only for hierarchical modeling. A Boolean switch for update one parameter at a time

ncore

Only for non-hierarchical, fixed-effect models with many subjects.

samples

posterior samples.

add

Boolean whether to add new samples