simulate_sbm_plus_srm_network_with_measurement_bias: A function to simulate single layer directed networks using a...

In ctross/STRAND: An R package for simulating and analyzing social network data in R and Stan

A function to simulate single layer directed networks using a combined stochastic block and social relations model

Description

This is a function to simulate single layer network data with a stochastic block structure, sender-receiver effects, and dyadic reciprocity. This function is essentially the union of a social relations model and a stochastic block model.

Usage

simulate_sbm_plus_srm_network_with_measurement_bias(
  N_id = 30,
  B = NULL,
  V = 3,
  groups = NULL,
  sr_mu = c(0, 0),
  sr_sigma = c(0.3, 1.5),
  sr_rho = 0.6,
  dr_mu = c(0, 0),
  dr_sigma = 1,
  dr_rho = 0.7,
  exposure_mu = 1.9,
  exposure_sigma = 0.01,
  exposure_max = 50,
  censoring_mu = 1.9,
  censoring_sigma = 0.01,
  N_trials = 20,
  mode = "binomial",
  individual_predictors = NULL,
  dyadic_predictors = NULL,
  exposure_predictors = NULL,
  censoring_predictors = NULL,
  individual_effects = NULL,
  dyadic_effects = NULL,
  exposure_effects = NULL,
  censoring_effects = NULL
)

Arguments

N_id	Number of individuals.
B	List of matrices that hold intercept and offset terms. Log-odds. The first matrix should be 1 x 1 with the value being the intercept term.
V	Number of blocking variables in B.
groups	Dataframe of the block IDs of each individual for each variable in B.
sr_mu	Mean vector for sender and receivier random effects. In most cases, this should be c(0,0).
sr_sigma	A standard deviation vector for sender and receivier random effects. The first element controls node-level variation in out-degree, the second in in-degree.
sr_rho	Correlation of sender-receiver effects (i.e., generalized reciprocity).
dr_mu	Mean vector for dyadic random effects. In most cases, this should be c(0,0).
dr_sigma	Standard deviation for dyadic random effects.
dr_rho	Correlation of dyad effects (i.e., dyadic reciprocity).
exposure_mu	Intercept term for log-odds of encounter.
exposure_sigma	Standard deviation for exposure random effects.
exposure_max	Max sample size of observations for a given focal.
censoring_mu	Intercept term for log-odds of censoring.
censoring_sigma	Standard deviation for censoring random effects.
N_trials	Number of binomial trials in follow-up detectability experiment.
mode	Outcome mode: only "binomial" is supported.
individual_predictors	An N_id by N_individual_parameters matrix of covariates.
dyadic_predictors	An N_id by N_id by N_dyadic_parameters array of covariates.
exposure_predictors	An N_id by N_individual_parameters matrix of covariates.
censoring_predictors	An N_id by N_individual_parameters matrix of covariates.
individual_effects	A 2 by N_individual_parameters matrix of slopes. The first row gives effects of focal characteristics (on out-degree). The second row gives effects of target characteristics (on in-degree).
dyadic_effects	An N_dyadic_parameters vector of slopes.
exposure_effects	An N_parameters vector of slopes.
censoring_effects	An N_parameters vector of slopes.

Value

A list of objects including: network (an adjacency matrix of binary outcomes), tie_strength (an adjacency matrix with probability weights), group_ids (a vector of length N_id, giving the group of each individual), individual_predictors (the supplied covariate data is saved along with the network data), and dyadic_predictors (the supplied covariate data is saved along with the network data).

ctross/STRAND documentation built on Nov. 14, 2024, 11:50 p.m.