`rcopCAR`

simulates areal data from the copCAR model.

1 |

`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 *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))
``` |

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