Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes

Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) , Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) , and Datta, Banerjee, Finley, and Gelfand (2016) .

spNNGP

spNNGP fits univariate Bayesian spatial regression models for large datasets using nearest neighbor Gaussian processes.

The package supports response and latent NNGP models for Gaussian outcomes, latent NNGP models for binomial outcomes, and conjugate NNGP models.

Installation

Install the development version from GitHub after the repository is published:

remotes::install_github("finleya/spNNGP")

Example

library(spNNGP)

set.seed(1)
n <- 100
coords <- cbind(runif(n), runif(n))
x <- rnorm(n)
y <- 1 + 0.5 * x + rnorm(n)

fit <- spNNGP(
  y ~ x,
  coords = coords,
  method = "latent",
  n.neighbors = 10,
  starting = list(beta = c(0, 0), sigma.sq = 1, tau.sq = 1, phi = 3),
  tuning = list(phi = 0.1),
  priors = list(
    sigma.sq.IG = c(2, 1),
    tau.sq.IG = c(2, 1),
    phi.Unif = c(0.1, 30)
  ),
  n.samples = 100,
  n.omp.threads = 1,
  verbose = FALSE
)

summary(fit)

References

Finley, A. O., Datta, A., and Banerjee, S. (2022). spNNGP R Package for Nearest Neighbor Gaussian Process Models. Journal of Statistical Software, 103(5). doi:10.18637/jss.v103.i05.

Finley, A. O., Datta, A., Cook, B. D., Morton, D. C., Andersen, H. E., and Banerjee, S. (2019). Efficient algorithms for Bayesian nearest neighbor Gaussian processes. Journal of Computational and Graphical Statistics, 28(2), 401-414. doi:10.1080/10618600.2018.1537924.

Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E. (2016). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514), 800-812. doi:10.1080/01621459.2015.1044091.