run_phase2_single {ERPM}

Phase 2 wrapper for single observation

Description

Phase 2 wrapper for single observation

Usage

run_phase2_single(
  partition,
  estimates.phase1,
  inv.zcov,
  inv.scaling,
  z.obs,
  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

partition

observed partition

estimates.phase1

vector containing parameter values after phase 1

inv.zcov

inverted covariance matrix

inv.scaling

scaling matrix

z.obs

observed statistics

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.

Value

a list