simulate_iglm: Simulate Responses and Connections

In iglm: Regression under Interference in Connected Populations

Simulate Responses and Connections

Description

Simulate responses and connections.

Usage

simulate_iglm(
  formula,
  basis = NULL,
  coef,
  coef_degrees = NULL,
  sampler = NULL,
  only_stats = TRUE,
  display_progress = FALSE,
  offset_nonoverlap = 0,
  cluster = NULL,
  fix_x = FALSE,
  fix_z = FALSE
)

Arguments

formula	A model 'formula' object. The left-hand side should be the name of a 'iglm.data' object available in the calling environment. See iglm-terms for details on specifying the right-hand side terms.
basis	An optional 'iglm.data' object to serve as the basis for the simulation. If provided, the simulation starts from the state defined in this object. If 'NULL' (default), the initial state is taken from the 'iglm.data' object referenced in the 'formula'.
coef	Numeric vector containing the coefficient values for the structural (non-degrees) terms defined in the 'formula'.
coef_degrees	Numeric vector specifying the degrees coefficient values (expansiveness/attractiveness). This is required only if the 'formula' includes degrees terms. Its length must be 'n_actor' (for undirected networks) or '2 * n_actor' (for directed networks), where 'n_actor' is determined from the 'iglm.data' object in the formula.
sampler	An object of class 'sampler.iglm' (created by 'sampler.iglm()') specifying the MCMC sampling parameters. This includes the number of simulations ('n_simulation'), burn-in iterations ('n_burn_in'), initialization settings ('init_empty'), and component sampler settings ('sampler_x', 'sampler_y', etc.). If 'NULL' (default), default settings from 'sampler.iglm()' are used.
only_stats	(logical). If TRUE (default, consistent with the usage signature), the function returns only the matrix of features calculated for each simulation. The full simulated iglm.data objects are discarded to minimize memory usage. If FALSE, the complete simulated iglm.data objects are created and returned within the samples component of the output list.
display_progress	Logical. If 'TRUE', progress messages or a progress bar (depending on the backend implementation) are displayed during simulation. Default is 'FALSE'.
offset_nonoverlap	Numeric scalar value passed to the C++ simulator. This value is typically added to the linear predictor for dyads that are not part of the 'overlap' set defined in the 'iglm.data' object, potentially modifying tie probabilities outside the primary neighborhood. Default is '0'.
cluster	Optional parallel cluster object created, for example, by “parallel::makeCluster“. If provided and valid, the function performs a single burn-in simulation on the main R process, then distributes the remaining 'n_simulation' tasks across the cluster workers using “parallel::parLapply“. The master seed is offset for each worker to ensure different random streams. If 'NULL' (default), all simulations are run sequentially in the main R process.
fix_x	Logical. If 'TRUE', the simulation holds the 'x_attribute' fixed at its initial state (from the iglm.data object) and only simulates the 'y_attribute' and 'z_network'. If 'FALSE' (default), all components (x, y, z) are simulated according to the model and sampler settings.
fix_z	Logical. If 'TRUE', the simulation holds the 'z_network' fixed at its initial state (from the iglm.data object). If 'FALSE' (default), the network component is simulated according to the model and sampler settings.

Details

Parallel Execution: When a 'cluster' object is provided, the simulation process is adapted:

1. A single simulation run (including burn-in specified by 'sampler$n_burn_in') is performed on the master node to obtain a starting state for the parallel chains.

2. The total number of requested simulations ('sampler$n_simulation') is divided among the cluster workers.

3. “parallel::parLapply“ is used to run simulations on each worker. Each worker starts from the state obtained after the initial burn-in, performs zero additional burn-in ('n_burn_in = 0' passed to workers), and generates its assigned share of the simulations. Component sampler seeds are offset based on the worker ID to ensure pseudo-independent random number streams.

4. Results (simulated objects or statistics) from all workers are collected and combined.

This approach ensures that the initial burn-in phase happens only once, saving time.

Value

A list containing one or two components (depending on 'only_stats'):

'samples'

If 'only_stats = FALSE', this is a list of length 'sampler$n_simulation' where each element is a 'iglm.data' object representing one simulated draw from the model. The list has the S3 class '"iglm.data.list"'. If 'only_stats = TRUE', this component is omitted.

'stats'

A numeric matrix with 'sampler$n_simulation' rows and 'length(coef)' columns. Each row contains the features (corresponding to the model terms in 'formula') calculated for one simulation draw. Column names are set to match the term names.

Errors

The function stops with an error if:

• The length of 'coef' does not match the number of terms derived from 'formula'.

• 'formula_preprocess' fails.

• The 'sampler' object is not of class 'sampler.iglm'.

• The C++ backend 'xyz_simulate_cpp' encounters an error.

• Helper functions like 'XYZ_to_R' or 'is_cluster_active' are not found.

Warnings may be issued if default sampler settings are used.

See Also

iglm for creating the model object, sampler.iglm for creating the sampler object, iglm.data for the data object structure.

iglm documentation built on April 23, 2026, 5:07 p.m.