simulate_hergm_within {bigergm}

Sample within cluster networks

Description

Obtains network statistics based on MCMC simulations including only the within-blocks connections.

Usage

simulate_hergm_within(
  formula_for_simulation,
  data_for_simulation,
  colname_vertex_id,
  colname_block_membership,
  coef_within_block,
  seed_edgelist = NULL,
  output = "stats",
  ergm_control = ergm::control.simulate.formula(),
  seed = NULL,
  n_sim = 1,
  verbose = 0,
  ...
)

Arguments

formula_for_simulation

formula for simulating a network

data_for_simulation

a data frame that contains vertex id, block membership, and vertex features.

colname_vertex_id

a column name in the data frame for the vertex ids

colname_block_membership

a column name in the data frame for the block membership

coef_within_block

a vector of within-block parameters. The order of the parameters should match that of the formula.

seed_edgelist

an edgelist used for creating a seed network. It should have the "edgelist" class

output

The desired output of the simulation (any of stats, network or edgelist). Defaults to stats

ergm_control

auxiliary function as user interface for fine-tuning ERGM simulation

seed

seed value (integer) for network simulation.

n_sim

number of networks generated

verbose

If this is TRUE/1, the program will print out additional information about the progress of simulation.

...

arguments to be passed to low level functions

Value

A 'data.frame' object where the columns relate to the sufficient statistics specified in formula_for_simulation and each row relates to one of the n_sim simulations.

Examples

data(toyNet) 
# Specify the model that you would like to estimate.
model_formula <- toyNet ~ edges + nodematch("x") + nodematch("y") 
# Estimate the model
nodes_data <- data.frame(
  node_id = 1:toyNet$gal$n,
  x = toyNet %v% "x",
  y = toyNet %v% "y",
  block = toyNet %v% "block"
)
list_feature_matrices <- 
   get_list_sparse_feature_adjmat(toyNet, model_formula)
toyNet <- network::as.edgelist(toyNet)

simulate_hergm_within(formula_for_simulation = model_formula, 
                      data_for_simulation = nodes_data,
                      colname_vertex_id = "node_id",
                      colname_block_membership = "block",
                      coef_within_block = c(-2,0.1,0.2),
                      n_sim = 10)