simsart {CDatanet}  R Documentation 
simsart
is used to simulate censored data with social interactions (see details). The model is presented in Xu and Lee(2015).
simsart(
formula,
contextual,
Glist,
theta,
tol = 1e15,
maxit = 500,
RE = FALSE,
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 subadjacency matrix. 
theta 
the parameter value as 
tol 
the tolerance value used in the Fixed Point Iteration Method to compute 
maxit 
the maximal number of iterations in the Fixed Point Iteration Method. 
RE 
a Boolean which indicates if the model if under rational expectation of not. 
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 leftcensored variable \mathbf{y}
is generated from a latent variable \mathbf{y}^*
.
The latent variable 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)
.
The censored variable y_i
is then define that is y_i = 0
if
y_i^* \leq 0
and y_i = y_i^*
otherwise.
A list consisting of:
yst 
ys (see details), the latent variable. 
y 
the censored variable. 
yb 
expectation of y under rational expectation. 
Gy 
the average of y among friends. 
Gyb 
Average of expectation of y among friends under rational expectation. 
marg.effects 
the marginal effects. 
iteration 
number of iterations performed by subnetwork in the Fixed Point Iteration Method. 
Xu, X., & Lee, L. F. (2015). Maximum likelihood estimation of a spatial autoregressive Tobit model. Journal of Econometrics, 188(1), 264280, doi:10.1016/j.jeconom.2015.05.004.
# Groups' size
M < 5 # Number of subgroups
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 < simsart(formula = ~ x1 + x2  x1 + x2, Glist = Glist,
theta = theta, data = data)
y < ytmp$y
# plot histogram
hist(y)