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Short Course for ENAR 2009 - Sunday, March 15, 2009

Hierarchical Modeling and Analysis of Spatial-Temporal Data: Emphasis in Forestry, Ecology, and Environmental Sciences [Description]


Course Schedule

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

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

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

12:00 - 1:00

Lunch, on your own

1:00 - 1:30

Models for non-Gaussian spatial data

Generalized linear spatial models and illustration (handout)

1:30 - 2:30

Models for multivariate spatial data

Multivariate spatial models and illustration (handout)

2:30 - 3:00

R session on GLM and multivariate Gaussian spatial models

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

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

3:00 - 3:15

Models for space time data

Spatial-temporal models (handout)

3:15 - 3:30

Afternoon refreshment break

3:30 - 4:15

Models for large spatial datasets

1: Spatial predictive process models and illustration (handout)

2: Computing notes

4:15 - 5:00

R session on predictive process models

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

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

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.

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. For 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.

  Andrew O Finley