gridsearch_burninthining_single function

Grid - search burnin thining single

Function that simulates the Markov chain for a given model and several sets of transitions (the neighborhoods), for a single partition. For each neighborhood, it calculates the autocorrelation of statistics for different thinings and the average statistics for different burn-ins. Then the best neighborhood can be selected along with good values for burn-in and thining

gridsearch_burninthining_single(
  partition,
  theta,
  nodes,
  effects,
  objects,
  num.steps,
  neighborhoods,
  numgroups.allowed,
  numgroups.simulated,
  sizes.allowed,
  sizes.simulated,
  max.thining,
  parallel = FALSE,
  cpus = 1
)

Arguments

• partition: A partition (vector)
• theta: Initial model parameters
• nodes: Node set (data frame)
• effects: Effects/sufficient statistics (list with a vector "names", and a vector "objects")
• objects: Objects used for statistics calculation (list with a vector "name", and a vector "object")
• num.steps: Number of samples wanted
• neighborhoods: List of probability vectors (proba actors swap, proba merge/division, proba single actor move)
• numgroups.allowed: vector containing the number of groups allowed in the partition (now, it only works with vectors like num_min:num_max)
• numgroups.simulated: vector containing the number of groups simulated
• sizes.allowed: Vector of group sizes allowed in sampling (now, it only works for vectors like size_min:size_max)
• sizes.simulated: Vector of group sizes allowed in the Markov chain but not necessraily sampled (now, it only works for vectors like size_min:size_max)
• max.thining: Where to stop adding thining
• parallel: False, to run different neighborhoods in parallel
• cpus: Equal to 1

Returns

list