Description Usage Arguments Details Value Examples
rcopCAR
simulates areal data from the copCAR model.
1 |
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 |
This function simulates data from the copCAR model with the given spatial dependence parameter ρ, regression coefficients β, design matrix X, and adjacency structure A. For negative binomial marginal distributions, a value for the dispersion parameter θ>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.
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 | # 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))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.