Spatial Data Analysis with INLA
Spatial Data Analysis with INLA
Abstract
This session will show how to fit mixed-effects models to spatial lattice data using the integrated nested Laplace approximation with the R-INLA package. This will cover some standard models such as the conditionally autoregressive (CAR) specification. The examples will be illustrated using the New York leukemia dataset and a few hints on how to produce maps to display the results will be given as well.
Requirements
The following CRAN packages are required:
pkgs <- c("sp", "spdep", "maptools", "rgdal", "gstat", "DClusterm", "spData")
pkgs <- c(pkgs, "viridis", "RColorBrewer")
pkgs <- c(pkgs, "tmap")
install.packages(pkgs)
INLA is also required, but it is not available on CRAN:
install.packages("INLA", repos = "https://inla.r-inla-download.org/R/stable")
About the speaker
Virgilio Gómez-Rubio is an associate professor in the Department of Mathematics, Universidad de Castilla-La Mancha (Albacete, Spain). He has developed several packages on spatial and Bayesian statistics that are available on CRAN, as well as co-authored books on spatial data analysis and INLA.
