gridsearch_burninthining_multiple function

Grid - search burnin thining multiple

Function that simulates the Markov chain for a given model and several sets of transitions (the neighborhoods), for multiple partitions. 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_multiple(
  partitions,
  presence.tables,
  theta,
  nodes,
  effects,
  objects,
  num.steps,
  neighborhoods,
  numgroups.allowed,
  numgroups.simulated,
  sizes.allowed,
  sizes.simulated,
  max.thining,
  parallel = FALSE,
  cpus = 1
)

Arguments

• partitions: Observed partitions
• presence.tables: Presence of nodes
• 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