simsar | R Documentation |
simsar
is used to simulate continuous variables with social interactions (see details). The model is presented in Lee(2004).
simsar(formula, contextual, Glist, theta, data)
formula |
an object of class formula: a symbolic description of the model. The |
contextual |
(optional) logical; if true, this means that all individual variables will be set as contextual variables. Set the
|
Glist |
the adjacency matrix or list sub-adjacency matrix. |
theta |
the parameter value as |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables
in the model. If not found in data, the variables are taken from |
The variable \mathbf{y}
is given for all i as
y_i = \lambda \mathbf{g}_i y + \mathbf{x}_i'\beta + \mathbf{g}_i\mathbf{X}\gamma + \epsilon_i,
where \epsilon_i \sim N(0, \sigma^2)
.
A list consisting of:
y |
the observed count data. |
Gy |
the average of y among friends. |
Lee, L. F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica, 72(6), 1899-1925, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1468-0262.2004.00558.x")}.
sar
, simsart
, simcdnet
.
# Groups' size
M <- 5 # Number of sub-groups
nvec <- round(runif(M, 100, 1000))
n <- sum(nvec)
# Parameters
lambda <- 0.4
beta <- c(2, -1.9, 0.8)
gamma <- c(1.5, -1.2)
sigma <- 1.5
theta <- c(lambda, beta, gamma, sigma)
# X
X <- cbind(rnorm(n, 1, 1), rexp(n, 0.4))
# Network
Glist <- list()
for (m in 1:M) {
nm <- nvec[m]
Gm <- matrix(0, nm, nm)
max_d <- 30
for (i in 1:nm) {
tmp <- sample((1:nm)[-i], sample(0:max_d, 1))
Gm[i, tmp] <- 1
}
rs <- rowSums(Gm); rs[rs == 0] <- 1
Gm <- Gm/rs
Glist[[m]] <- Gm
}
# data
data <- data.frame(x1 = X[,1], x2 = X[,2])
rm(list = ls()[!(ls() %in% c("Glist", "data", "theta"))])
ytmp <- simsar(formula = ~ x1 + x2 | x1 + x2, Glist = Glist,
theta = theta, data = data)
y <- ytmp$y
# plot histogram
hist(y)
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