simSAR: Simulating Linear Peer Effect Models

In PartialNetwork: Estimating Peer Effects Using Partial Network Data

Simulating Linear Peer Effect Models

Description

simSAR simulates linear peer effect models.

Usage

simSAR(formula, Glist, parms, lambda, beta, epsilon, nthreads = 1, data)

Arguments

formula	An object of class formula: a symbolic description of the model. formula should be specified as, for example, ~ x1 + x2, where x1 and x2 are control variables, which can include contextual variables such as averages or quantiles among peers.
Glist	The adjacency matrix. For networks consisting of multiple subnets (e.g., schools), Glist must be a list of subnets, with the m-th element being an n_m \times n_m adjacency matrix, where n_m is the number of nodes in the m-th subnet.
parms	A vector defining the true values of (\lambda', \beta')', where \lambda is the peer effect parameter and \beta is the parameter vector of control variables (including contextual variables if any). The parameters \lambda and \beta can also be specified separately using the arguments lambda, and beta.
lambda	The true value of the vector \lambda.
beta	The true value of the vector \beta.
epsilon	A vector of idiosyncratic error terms. If not specified, it will be simulated from a standard normal distribution.
nthreads	A strictly positive integer indicating the number of threads to use.
data	An optional data frame, list, or environment (or an object that can be coerced by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which simSAR is called.

Value

A list containing:

y	The simulated variable.
Gy	the average of y among friends.

See Also

smmSAR, mcmcSAR

Examples

set.seed(123)
ngr  <- 50
nvec <- rep(30, ngr)
n    <- sum(nvec)
G    <- lapply(1:ngr, function(z){
  Gz <- matrix(rbinom(nvec[z]^2, 1, 0.3), nvec[z])
  diag(Gz) <- 0
  Gz/rowSums(Gz) # Row-normalized network
})
X    <- cbind(rnorm(n), rpois(n, 2))
l    <- 0.5
b    <- c(2, -0.5, 1)

out  <- simSAR(formula = ~ X, Glist = G, lambda = l, beta = b)
summary(out$y)

PartialNetwork documentation built on Nov. 10, 2025, 5:07 p.m.