control.iglm: Set Control Parameters for iglm Estimation

Description

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

Usage

control.iglm(
  estimate_model = TRUE,
  fix_x = FALSE,
  display_progress = FALSE,
  return_samples = TRUE,
  offset_nonoverlap = 0,
  var = FALSE,
  non_stop = FALSE,
  tol = 0.001,
  max_it = 100,
  return_x = FALSE,
  return_y = FALSE,
  return_z = FALSE,
  accelerated = TRUE,
  cluster = NULL,
  exact = FALSE,
  updated_uncertainty = TRUE
)

Value

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

Arguments

estimate_model: (logical) If `TRUE` (default), the main model parameters are estimated. If `FALSE`, estimation is skipped and only the preprocessing is done.
fix_x: (logical) If `TRUE`, the 'x' predictor is held fixed during estimation/simulation (fixed design in regression). Default is `FALSE`.
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: (logical) If `TRUE`, attempt to calculate and return the variance-covariance matrix of the estimated parameters. Default is `FALSE`.
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).
cluster: A parallel cluster object (e.g., from the `parallel` package) to use for potentially parallelizing parts of the estimation or simulation. Default is `NULL` (no parallelization).
exact: (logical) If `TRUE`, potentially use an exact calculation method of the pseudo Fisher information for assessing the uncertainty of the estimates. Default is `FALSE`.
updated_uncertainty: (logical) If `TRUE` (default), potentially use an updated method for calculating uncertainty estimates (based on the mean-value theorem as opposed to the Godambe Information).

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.