simulate_sbm_plus_srm_network: 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(
  N_id = 99,
  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,
  mode = "bernoulli",
  individual_predictors = NULL,
  dyadic_predictors = NULL,
  individual_effects = NULL,
  dyadic_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 A 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 A 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).
mode	Outcome mode: can be "bernoulli", "poisson", or "binomial".
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.
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.

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).

Examples

## Not run: 
library(igraph)
V = 1            # One blocking variable
G = 3            # Three categories in this variable
N_id = 100       # Number of people

clique = sample(1:3, N_id, replace=TRUE)
B = matrix(-8, nrow=G, ncol=G)
diag(B) = -4.5 # Block matrix

B[1,3] = -5.9
B[3,2] = -6.9

A = simulate_sbm_plus_srm_network(N_id = N_id, B=list(B=B), V=V, 
                         groups=data.frame(clique=factor(clique)),
                         individual_predictor=matrix(rnorm(N_id, 0, 1), nrow=N_id, ncol=1), 
                         individual_effects=matrix(c(1.7, 0.3),ncol=1, nrow=2),
                         sr_sigma = c(1.4, 0.8), sr_rho = 0.5,
                         dr_sigma = 1.2, dr_rho = 0.8,
                         mode="bernoulli"
                               )

Net = graph_from_adjacency_matrix(A$network, mode = c("directed"))
V(Net)$color = c("turquoise4","gray13", "goldenrod3")[A$group_ids$clique]

plot(Net, edge.arrow.size =0.1, edge.curved = 0.3, vertex.label=NA, vertex.size = 5)

## End(Not run)

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