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.
spBayes, spatial, geoR, sgeostat, spdep, sphet, sna, network, Matrix, sparseM, spatialprobit, McSpatial, LearnBayes, tmvtnorm, mvtnorm, igraph
Spatial, Bayesian, Distributions, Econometrics, SocialSciences, gR, Multivariate, Optimization, SpatioTemporal, Finance, Graphics, NumericalMathematics, Survival
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For attribution, please cite this work as
Wilhelm & Matos, "Estimating Spatial Probit Models in R", The R Journal, 2013
BibTeX citation
@article{RJ-2013-013,
author = {Wilhelm, Stefan and Matos, Miguel Godinho de},
title = {Estimating Spatial Probit Models in R},
journal = {The R Journal},
year = {2013},
note = {https://doi.org/10.32614/RJ-2013-013},
doi = {10.32614/RJ-2013-013},
volume = {5},
issue = {1},
issn = {2073-4859},
pages = {130-143}
}