|
Abstract:
|
Bayesian hierarchical models are useful for modeling spatial data because they have flexibility to accommodate complicated dependencies that are common to spatial data. In particular intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data that partition a region. However, selection of prior distributions for these spatial parameters presents a challenge to researchers. We present and describe ref.ICAR, an R package that implements an objective Bayes intrinsic conditional autoregressive prior on a vector of spatial random effects. This model, developed by Keefe et al. (2019), provides an objective Bayesian approach for modeling spatially correlated areal data. ref.ICAR can be used to analyze areal data corresponding to a contiguous region, provided a shapefile or neighborhood matrix and data. The ref.ICAR package performs MCMC sampling and outputs posterior medians and intervals as well as trace plots for fixed effect and spatial parameters. It provides regional summaries including medians and credible intervals for the fitted values for each subregion in the data.
|
