An improved model for spatially correlated binary responses

In this paper, we use covariates and an indication of sampling effort in an autologistic model to improve predictions of probability of presence for lattice data. The model is applied to sampled data where only a small proportion of the available sites have been observed. We adopt a Bayesian set-up and develop a Gibbs sampling estimation procedure. In four examples based on simulated data, we show that the autologistic model with covariates improves predictions compared with the simple logistic regression model and the basic autologistic model (without covariates). Software to implement the methodology is available at no cost from StatLib.