Skip to contents

spBayes

CRAN status R-CMD-check pkgdown

spBayes fits Bayesian univariate and multivariate spatial and spatio-temporal regression models for point-referenced data. The package provides MCMC-based model fitting, posterior recovery of spatial random effects, prediction at new locations, model diagnostics, and utilities for building spatial covariance matrices.

Installation

Install the CRAN release with:

install.packages("spBayes")

Install the development version from GitHub with:

remotes::install_github("finleya/spBayes")

Basic Use 

library(spBayes)

set.seed(1)
n <- 50
coords <- cbind(runif(n), runif(n))
x <- rnorm(n)
y <- 1 + 0.5 * x + rnorm(n)

fit <- spLM(
  y ~ x,
  coords = coords,
  starting = list(phi = 3, sigma.sq = 1, tau.sq = 1),
  tuning = list(phi = 0.1, sigma.sq = 0.1, tau.sq = 0.1),
  priors = list(
    phi.Unif = c(0.1, 30),
    sigma.sq.IG = c(2, 1),
    tau.sq.IG = c(2, 1)
  ),
  cov.model = "exponential",
  n.samples = 100,
  verbose = FALSE
)

summary(fit$p.theta.samples)

Functionality

The package provides:

  • Gaussian spatial linear models with spLM().
  • Binomial and Poisson spatial generalized linear models with spGLM().
  • Multivariate spatial linear and generalized linear models.
  • Spatially varying coefficient and dynamic spatio-temporal models.
  • Predictive-process models for larger point-referenced data.
  • Posterior recovery, prediction, diagnostics, and covariance utilities.

Citations

If you use spBayes, please cite:

Finley, A. O., Banerjee, S., and Carlin, B. P. (2007). spBayes: An R Package for Univariate and Multivariate Hierarchical Point-Referenced Spatial Models. Journal of Statistical Software, 19(4), 1-24.

Finley, A. O., Banerjee, S., and Gelfand, A. E. (2015). spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models. Journal of Statistical Software, 63(13), 1-28. doi:10.18637/jss.v063.i13.

Finley, A. O. and Banerjee, S. (2020). Bayesian spatially varying coefficient models in the spBayes R package. Environmental Modelling & Software, 125, 104608. doi:10.1016/j.envsoft.2019.104608.