Estimating Spatial Probit Models in R

Authors

Stefan Wilhelm, University of Basel
Miguel Godinho de Matos, Carnegie Mellon University

Date of this Version

6-2013

Document Type

Article

Citation

The R Journal (June 2013) 5(1); Editor: Hadley Wickham

Comments

Copyright 2013, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance gains through parallelization of the Markov Chain Monte Carlo approach.