simcdnet {CDatanet}  R Documentation 
simcdnet
is used simulate counting data with rational expectations (see details). The model is presented in Houndetoungan (2022).
simcdnet(
formula,
contextual,
Glist,
theta,
deltabar,
delta = NULL,
rho = 0,
tol = 1e10,
maxit = 500,
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 true value of the vector 
deltabar 
the true value of 
delta 
the true value of the vector 
rho 
the true value of 
tol 
the tolerance value used in the Fixed Point Iteration Method to compute the expectancy of 
maxit 
the maximal number of iterations in the Fixed Point Iteration Method. 
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 
Following Houndetoungan (2022), the count data \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 \mathbf{E}(\bar{\mathbf{y}}\mathbf{X},\mathbf{G}) + \mathbf{x}_i'\beta + \mathbf{g}_i\mathbf{X}\gamma + \epsilon_i,
where \epsilon_i \sim N(0, 1)
.
Then, y_i = r
iff a_r \leq y_i^* \leq a_{r+1}
, where
a_0 = \inf
, a_1 = 0
, a_r = \sum_{k = 1}^r\delta_k
.
The parameter are subject to the constraints \delta_r \geq \lambda
if 1 \leq r \leq \bar{R}
, and
\delta_r = (r  \bar{R})^{\rho}\bar{\delta} + \lambda
if r \geq \bar{R} + 1
.
A list consisting of:
yst 
ys (see details), the latent variable. 
y 
the observed count data. 
yb 
ybar (see details), the expectation of y. 
Gyb 
the average of the expectation of y among friends. 
marg.effects 
the marginal effects. 
rho 
the return value of rho. 
Rmax 
infinite sums in the marginal effects are approximated by sums up to Rmax. 
iteration 
number of iterations performed by subnetwork in the Fixed Point Iteration Method. 
Houndetoungan, E. A. (2022). Count Data Models with Social Interactions under Rational Expectations. Available at SSRN 3721250, doi:10.2139/ssrn.3721250.
# Groups' size
M < 5 # Number of subgroups
nvec < round(runif(M, 100, 1000))
n < sum(nvec)
# Parameters
lambda < 0.4
beta < c(1.5, 2.2, 0.9)
gamma < c(1.5, 1.2)
delta < c(1, 0.87, 0.75, 0.6)
delbar < 0.05
theta < c(lambda, beta, gamma)
# 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", "delta", "delbar"))])
ytmp < simcdnet(formula = ~ x1 + x2  x1 + x2, Glist = Glist, theta = theta,
deltabar = delbar, delta = delta, rho = 0, data = data)
y < ytmp$y
# plot histogram
hist(y, breaks = max(y))