Applied Multivariate Statistical Analysis in the Environmental Sciences
ENTX 6300
COURSE INFORMATION
Description:
The mechanisms behind multivariate techniques used in the
environmental sciences will be explained at the level of the students in those fields. A
wide variety of examples will be presented in class with an emphasis placed on the
codes, functions, and packages necessary for multivariate data analysis in R, and the
interpretation of the R outputs. At the end of the class, the students will be able to apply
the multivariate techniques to their own research.
Instructor: Rodica Gelca (http://www.tiehh.ttu.edu/rgelca/)
Credits: 3
Textbooks:
No textbook will be required. Lecture notes will be provided.
Recommended references:
1. An Introduction to R. Free to download at http://www.r-project.org/.
2. Dalgaard, P. 2002. Introductory Statistics with R. Springer-Verlag.
3. Everitt, B., Hothorn, T. 2011. An Introduction to Applied Multivariate Analysis with
R. Springer (eBook at TTU library).
4. Shaw, P. 2003. Multivariate Statistics for the Environmental Sciences.
Prerequisites: Students are expected to have basic concepts in statistics and to
know how to use R, or permission from the instructor.
Meeting Time: Tuesdays and Thursdays, 3:30 – 5 pm.
Meeting Place: Experimental Science Building, room 120.
Office hours: Tues,Thurs 5:00-6:00 pm (Holden Hall, room 72), or by appointment.
Attendance:
Attendance in this class is expected, but not mandatory. Class
attendance will be to the advantage of the students, for the better understanding of the
concepts presented in class.
Assignments: Assignments will be given on approximately two week intervals.
Lecture Topics:
Introduction to Multivariate Data Analysis
1. R overview
2. Multivariate data and multivariate analysis: a brief history of the development of
multivariate analysis, types of variables, missing values, covariance, correlation,
distances, and the multivariate normal density function.
3. Multivariate data visualization: graphical display, scatterplot, bivariate boxplot, the
convex hull of bivariate data, chi-plot, the bubble and other glyph plots, the
scatterplot matrix, and trellis graphics.
4. Basic concepts in ordination: direct and indirect ordination.
Principal Components Analysis
1.
2.
3.
4.
5.
6.
7.
8.
Introduction
Normalizing the data
The extraction of principal components
Eigenvalues and eigenvectors
Calculating principal components scores
Plotting the principal components
Biplots
Examples in R
Cluster Analysis
1.
2.
3.
4.
5.
6.
Introduction
Agglomerative hierarchical clustering
K-means clustering
Model-based clustering
Displaying clustering solutions graphically
Examples in R
Multidimensional Scaling
1.
2.
3.
4.
5.
6.
Introduction
Models for proximity data
Spatial models for proximities: Multidimensional scaling
Classical multidimensional scaling
Non-metric multidimensional scaling
Examples in R
Correspondence Analysis
1.
2.
3.
4.
5.
6.
Introduction
The correspondence analysis algorithm
Limitations of correspondence analysis
Detrended correspondence analysis
The mechanics of detrended correspondence analysis
Examples in R
Canonical Correspondence Analysis
1. Introduction
2. The mechanics of canonical correspondence analysis
3. Examples in R
Grading:
Assignments:
60%
Final exam:
40%