rcopCAR {copCAR} | R Documentation |

## Simulate areal data.

### Description

`rcopCAR`

simulates areal data from the copCAR model.

### Usage

```
rcopCAR(rho, beta, X, A, family)
```

### Arguments

`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 |

### Details

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`

.

### Value

A vector of length `n`

distributed according to the specified copCAR model.

### Examples

```
# 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))
```

*copCAR*version 2.0-4 Index]