Simulate areal data.
Description
rcopCAR
simulates areal data from the copCAR model.
Usage
1 
Arguments
rho 
the spatial dependence parameter ρ such that ρ \in [0, 1). 
beta 
the vector of regression coefficients β = (β_1, …, β_p)'. 
X 
the n by p design matrix X. 
A 
the symmetric binary adjacency matrix for the
underlying graph. 
family 
the marginal distribution of the
observations and link function to be used in the model.
This can be a character string naming a family function,
a family function or the result of a call to a family
function. (See 
Details
This function randomly generates Poisson or Bernoulli areal
data with adjacency matrix A from the copCAR model
with the given spatial dependence parameter ρ,
regression coefficients β = (β_1, …,
β_p)', and design matrix X. For more details on
the copCAR model, see copCAR
.
Value
A vector of length n distributed according to the copCAR model with the given design matrix and parameter values.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  ## Not run:
require(lattice)
# Use the 20 x 20 square lattice as the underlying graph.
m = 20
A = adjacency.matrix(m)
# Set dependence parameter and regression coefficients.
rho = 0.8
beta = c(1, 1)
# Create design matrix by assigning coordinates to each vertex
# such that the coordinates are restricted to the unit square.
x = rep(0:(m  1) / (m  1), times = m)
y = rep(0:(m  1) / (m  1), each = m)
X = cbind(x, y)
# Draw Poisson data from copCAR model.
Z = rcopCAR(rho, beta, X, A, family = poisson(link = "log"))
# Create a level plot of the simulated data.
dev.new()
levelplot(Z ~ x * y, aspect = "iso")
# Draw Bernoulli data from copCAR model.
Z2 = rcopCAR(rho, beta, X, A, family = binomial(link = "logit"))
# Create a level plot of the simulated data.
dev.new()
levelplot(Z2 ~ x * y, aspect = "iso")
## End(Not run)
