simulate_burninthining_multiple function

Simulate burnin thining multiple

Function that simulates the Markov chain for a given model and a set of transitions (the neighborhood), for multiple partitions. It calculates the autocorrelation of statistics for different thinings and the average statistics for different burn-ins.

simulate_burninthining_multiple(
  partitions,
  presence.tables,
  theta,
  nodes,
  effects,
  objects,
  num.steps,
  neighborhood,
  numgroups.allowed,
  numgroups.simulated,
  sizes.allowed,
  sizes.simulated,
  max.thining,
  verbose = FALSE
)

Arguments

• partitions: Observed partitions
• presence.tables: to indicate which nodes were present when
• 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
• neighborhood: Way of choosing partitions: probability vector (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: maximal number of simulated steps in the thining
• verbose: logical: should intermediate results during the estimation be printed or not? Defaults to FALSE.

Returns

A list