control.iglm: Set Control Parameters for iglm Estimation

In iglm: Regression under Interference in Connected Populations

Set Control Parameters for iglm Estimation

Description

Create a list of control parameters for the 'iglm' estimation algorithm.

Usage

control.iglm(
  estimate_model = TRUE,
  display_progress = FALSE,
  return_samples = TRUE,
  offset_nonoverlap = 0,
  var_method = "Mean-value",
  non_stop = FALSE,
  tol = 0.001,
  max_it = 100,
  return_x = FALSE,
  return_y = FALSE,
  return_z = FALSE,
  accelerated = TRUE,
  exact = TRUE
)

Arguments

estimate_model	(logical) If 'TRUE' (default), the main model parameters are estimated. If 'FALSE', estimation is skipped and only the preprocessing is done.
display_progress	(logical) If 'TRUE', display progress messages or a progress bar during estimation. Default is 'FALSE'.
return_samples	(logical). If TRUE (default), return simulated network/attribute samples (i.e., iglm.data objects) generated during estimation (if applicable).
offset_nonoverlap	(numeric) A value added to the linear predictor for dyads not in the 'overlap' set. Default is '0'.
var_method	(string) Method for variance estimation. Options are "Mean-value" (default), "Godambe", and "Hessian". The mean-value version is described in Section 3.3 of Fritz et al. (2025), the Godambe method is described in Schmid and Hunter (2023), and the "Hessian" option just assumes that the pseudo likelihood is the correct likelihood.
non_stop	(logical) If 'TRUE', the estimation algorithm continues until 'max_it' iterations, ignoring the 'tol' convergence criterion. Default is 'FALSE'.
tol	(numeric) The tolerance level for convergence. The estimation stops when the change in coefficients between iterations is less than 'tol'. Default is '0.001'.
max_it	(integer) The maximum number of iterations for the estimation algorithm. Default is '100'.
return_x	(logical). If TRUE, return the change statistics for the x attribute Default is FALSE. from samples. Default is 'FALSE'. (Note: 'return_samples=TRUE' likely implies this).
return_y	(logical). If TRUE, return the change statistics for the y attribute Default is FALSE.
return_z	(logical). If TRUE, return the change statistics for the z network. Default is FALSE.
accelerated	(logical) If 'TRUE' (default), an accelerated MM algorithm is used based on a Quasi Newton scheme described in the Supplemental Material of Fritz et al (2025).
exact	(logical) If 'TRUE', the pseudo Fisher information is calculated exact for assessing the uncertainty of the estimates. Default is 'FALSE'.

Value

A list object of class '"control.iglm"' containing the specified control parameters.

References

Fritz, C., Schweinberger, M. , Bhadra S., and D. R. Hunter (2025). A Regression Framework for Studying Relationships among Attributes under Network Interference. Journal of the American Statistical Association, to appear.

Schmid, C.S. and D. R. Hunter (2023). Computing Pseudolikelihood Estimators for Exponential-Family Random Graph Models. Journal of Data Science

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