Last data update: 2014.03.03

R: Return a perfect sample from a centered autologistic model.
rautologisticR Documentation

Return a perfect sample from a centered autologistic model.


Return a perfect sample from a centered autologistic model.


rautologistic(X, A, theta)



the design matrix.


the adjacency matrix for the underlying graph.


the vector of parameter values: θ = (β', η)'.


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


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


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.


## Not run: 

# 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.
mu = exp(X %*% beta)
mu = mu / (1 + mu)                              # Compute the independence expectations.
col1 = "white"
col2 = "black"
colfunc = colorRampPalette(c(col1, col2))

# Now produce a level plot of the independence expectations. This shows that the large-scale
# structure corresponds to a probability gradient that increases as one moves from southwest
# to northeast.
lattice::levelplot(mu ~ x * y, aspect = "iso", col.regions = colfunc(n^2))

# 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)
Z = rautologistic(X, A, theta)

# Create a level plot of the simulated data.
lattice::levelplot(Z ~ x * y, aspect = "iso", col.regions = c("white", "black"), colorkey = FALSE)

## End(Not run)