run_phase2_multiple function

Phase 2 wrapper for multiple observation

run_phase2_multiple(
  partitions,
  estimates.phase1,
  inv.zcov,
  inv.scaling,
  z.obs,
  presence.tables,
  nodes,
  effects,
  objects,
  burnin,
  thining,
  num.steps,
  gainfactors,
  r.truncation.p2,
  min.iter,
  max.iter,
  multiplication.iter,
  neighborhood,
  fixed.estimates,
  numgroups.allowed,
  numgroups.simulated,
  sizes.allowed,
  sizes.simulated,
  double.averaging,
  parallel = FALSE,
  cpus = 1,
  verbose = FALSE
)

Arguments

• partitions: observed partitions
• estimates.phase1: vector containing parameter values after phase 1
• inv.zcov: inverted covariance matrix
• inv.scaling: scaling matrix
• z.obs: observed statistics
• presence.tables: data frame to indicate which times nodes are present in the partition
• 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")
• burnin: integer for the number of burn-in steps before sampling
• thining: integer for the number of thining steps between sampling
• num.steps: number of sub-phases in phase 2
• gainfactors: vector of gain factors
• r.truncation.p2: truncation factor
• min.iter: minimum numbers of steps in each subphase
• max.iter: maximum numbers of steps in each subphase
• multiplication.iter: used to calculate min.iter and max.iter if not specified
• neighborhood: vector for the probability of choosing a particular transition in the chain
• fixed.estimates: if some parameters are fixed, list with as many elements as effects, these elements equal a fixed value if needed, or NULL if they should be estimated
• 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)
• double.averaging: boolean to indicate whether we follow the double-averaging procedure (often leads to better convergence)
• parallel: boolean to indicate whether the code should be run in parallel
• cpus: number of cpus if parallel = TRUE
• verbose: logical: should intermediate results during the estimation be printed or not? Defaults to FALSE.

Returns

a list