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The University of Nebraska-Lincoln Department of Statistics Spatio-temporal workshop - October 15 - 16, 2012

Pre-course material

We encourage participants to bring a laptop and follow along with the illustrative R sessions. However, this level of participation is purely optional. For those who are not familiar with R, please review "An Introduction to R" (PDF, HTML). This introductory tutorial is ideal for beginners. It contains a description of data types, commands, and basic statistical analysis. The latest release of R is necessary for those who choose to participate in the computing sessions. Also, the latest releases of the various R packages used in the illustrative sessions are necessary. The majority of these packages can be installed all at once using the Spatial CRAN Task View.

  • Linux users -- some packages used in the sessions require lower-level c/c++ libraries. Here is a brief howto on installing these supporting libraries.

  • Windows users -- should be able to install the necessary packages by running the Packages.R script within R.

  • Mac users -- should be able to install all of the binary packages from CRAN, except for rgdal. Instructions for installing rgdal are given on the package's CRAN page here.

  • We will be using spBayes_0.2-4 in all of the exercises.

Please note that handouts of slides and annotated R sessions will not be provided. Rather, if desired, you can print them out beforehand and bring them along. Slide handouts can be downloaded at once here or by topic below. The R code with and without annotation and supporting data files (in compressed form) are also provided below.

Course Materials

We will likely not have time to cover Module 2, rather it is offered here to support self-study. Also, some of these materials might change between now and the beginning of the course.

Module 1

Introduction to spatial data and models

Basics of point-referenced (geostatistical) datasets (handout)

R session on spatial data exploration and graphics

annotated, code, and session data as tar.gz or zip archive.

Module 2

Principles of Bayesian inference

1: Basic principles (handout)

2: Linear models and computing (handout)

Module 3

Hierarchical models for spatial data

1: Spatial autoregressive models (handout)

2: Spatial point-referenced models (handout)

Bugs and R session on univariate Gaussian spatial models

1: Illustration of areal model using Bugs code.

2: annotated, code, and session data as tar.gz or zip archive.

3: annotated, code, and session data as tar.gz or zip archive.

Module 4

Models for non-Gaussian spatial data

Generalized linear spatial models and illustration (handout)

R session on generalized linear spatial models

1: annotated, code, and session data as tar.gz or zip archive.

2: Illustration of binomial model with multiple trials at each location code.

Module 5

Models for multivariate spatial data

Multivariate spatial models and illustration (handout)

R session on multivariate Gaussian spatial models

annotated, code, and session data as tar.gz or zip archive.

Module 6

Models for space time data

Spatial-temporal models (handout)

Module 7

Models for large spatial datasets

Spatial predictive process models and illustration (handout)

R session on predictive process models

1: annotated, code, and session data as tar.gz or zip archive.

2: annotated, code, and session data as tar.gz or zip archive.

Module 8

Computing environment and R/C/C++ API

2: Computing notes (handout)

API examples as tar.gz or zip

Course instructors

Dr. Andrew Finley, Department of Forestry, Geography, and Statistics & Probability, Michigan State University.

Dr. Sudipto Banerjee, Division of Biostatistics, School of Public Health, University of Minnesota.

  Andrew O Finley