rautologistic: Return a perfect sample from a centered autologistic model.

In ngspatial: Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data

Description

Return a perfect sample from a centered autologistic model.

Usage

rautologistic(X, A, theta)

Arguments

X	the design matrix.
A	the adjacency matrix for the underlying graph. The matrix need not be binary, but it must be numeric and symmetric.
theta	the vector of parameter values: θ = (β', η)'.

Details

This function implements a perfect sampler for the centered autologistic model. The sampler employs coupling from the past.

Value

A vector that is distributed exactly according to the centered autologistic model with the given design matrix and parameter values.

References

Moller, J. (1999) Perfect simulation of conditionally specified models. Journal of the Royal Statistical Society, Series B, Methodological, 61, 251–264.

Propp, J. G. and Wilson, D. B. (1996) Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures and Algorithms, 9(1-2), 223–252.

Examples

# Use the 20 x 20 square lattice as the underlying graph.

n = 20
A = adjacency.matrix(n)

# Assign coordinates to each vertex such that the coordinates are restricted to the unit square
# centered at the origin.

x = rep(0:(n - 1) / (n - 1), times = n) - 0.5
y = rep(0:(n - 1) / (n - 1), each = n) - 0.5
X = cbind(x, y)                                 # Use the vertex locations as spatial covariates.
beta = c(2, 2)                                  # These are the regression coefficients.

# Simulate a dataset with the above mentioned regression component and eta equal to 0.6. This
# value of eta corresponds to dependence that is moderate in strength.

theta = c(beta, eta = 0.6)
set.seed(123456)
Z = rautologistic(X, A, theta)

ngspatial documentation built on July 2, 2020, 12:01 a.m.