rcopCAR {copCAR} | R Documentation |
rcopCAR
simulates areal data from the copCAR model.
rcopCAR(rho, beta, X, A, family)
rho |
the spatial dependence parameter |
beta |
the vector of regression coefficients |
X |
the |
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 |
This function simulates data from the copCAR model with the given spatial dependence parameter \rho
, regression coefficients \beta
, design matrix X
, and adjacency structure A
. For negative binomial marginal distributions, a value for the dispersion parameter \theta>0
is also required; this value must be passed to the negbinomial
family function. For more details on the copCAR model, see copCAR
.
A vector of length n
distributed according to the specified copCAR model.
# Use the 20 x 20 square lattice as the underlying graph.
m = 20
A = adjacency.matrix(m)
# Create a 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)
# Set the dependence parameter and regression coefficients.
rho = 0.995 # strong dependence
beta = c(1, 1) # the mean surface increases in the direction of (1, 1)
# Simulate Poisson data from the corresponding copCAR model.
z = rcopCAR(rho, beta, X, A, family = poisson(link = "log"))
# Simulate Bernoulli outcomes.
z = rcopCAR(rho, beta, X, A, family = binomial(link = "logit"))
# Set the dispersion parameter.
theta = 10
# Simulate negative binomial outcomes.
z = rcopCAR(rho, beta, X, A, family = negbinomial(theta))