Short Course for JSM 2009 - Sunday, August 2, 2009
Hierarchical Modeling and Analysis of Spatial-Temporal Data: Emphasis in Forestry, Ecology, and Environmental Sciences
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 (detailed in the Course Schedule) 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 in this howto which references the supporting libraries and R packages found here. Also, please note that we will be using spBayes_0.1-3, the Mac binary for which was posted on CRAN June 30th.
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 Schedule (Tentative)
8:00 - 8:45 |
Introduction to spatial data and models |
|
Basics of point-referenced (geostatistical) datasets (handout) |
8:45 - 9:30 |
R session on spatial data exploration and graphics |
|
annotated,
code,
and session data as
tar.gz or
zip archive.
|
9:30 - 10:15 |
Introduction to Bayesian inference |
|
1: Basic principles (handout) |
|
2: Linear models and computing (handout) |
10:15 - 10:30 |
Morning break |
10:30 - 11:20 |
Hierarchical models for point-referenced data |
|
Univariate spatial models (handout) |
11:20 - 12:00 |
R session on univariate Gaussian spatial 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.
|
12:00 - 12:30 |
Models for non-Gaussian spatial data |
|
Generalized linear spatial models and illustration (handout) |
12:30 - 2:00 |
Lunch |
2:00 - 2:30 |
R session on generalized linear spatial models |
|
annotated,
code,
and session data as
tar.gz or
zip archive.
|
2:30 - 3:15 |
Models for multivariate spatial data |
|
Multivariate spatial models and illustration (handout) |
3:15 - 3:30 |
Afternoon refreshment break |
3:30 - 4:00 |
R session on multivariate Gaussian spatial models |
|
annotated,
code,
and session data as
tar.gz or
zip archive.
|
4:00 - 4:15 |
Models for space time data |
|
Spatial-temporal models (handout) |
4:15 - 5:00 |
Models for large spatial datasets |
|
Spatial predictive process models and illustration (handout) |
Time permitting |
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.
|
|
3: Computing notes (handout) |
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.
|
|
 |
