Fall 2017: APPLIED MULTIVARIATE STATISTICS USING R
|Office||407 Hardin Hall|
|Office hours||Drop by or by appointment|
Course Description This course provides students with an understanding of how to apply multivariate statistical methods. Emphasis is placed on developing conceptual and practical understanding of how to apply these techniques with real-world data. While many of the examples will be with ecological data, the information is applicable to other disciplines. Topics include introduction to coding and use of the statistical program R, screening data, distance matrices, classification approaches, ordination, multivariate regression. No previous experience in R is required.
By the end of this course, students should be able to:
- Demonstrate a working knowledge of the coding in the statistical program R.
- Demonstrate a working knowledge of manipulating and summarizing data sets and producing presentation or publication graphics in R
- Use appropriate techniques to screen and standardize multivariate data.
- Use the appropriate analysis technique to identify and characterize groups in multivariate data.
- Use the appropriate analysis techniques in the ordination of multivariate data.
- Demonstrate an ability to model multivariate data using the appropriate techniques.
This course will:
- Provide a forum for discussion on problem solving with R and multivariate data.
- Enable students to utilize approaches to evaluate questions using multivariate data
- Familiarize students with common R packages (e.g., psych, vegan) to conduct multivariate analysis in R
- Provide each student a suite of analytical and coding skills that will can be applied in graduate school and their careers.
- No text book is required
- Readings, data sets, and problem sets will be available on the courses github page
This schedule is tentative and we may change the schedule a depending on the interest of the class.
|1||25-Aug||Introduction to R and coding; Data structures in R; Importing data|
|2||1-Sep||Data manipulation and summarization|
|3||8-Sep||Graphing with ggplot2|
|4||15-Sep||Introduction to multivariate techniques; Multivariate data, Data standardization, Distance matrices|
|5||22-Sep||Identifying groups in multivariate data; Cluster analysis, Discrimination among groups|
|6||29-Sep||Characterizing multivariate data; Factor analysis, Latent class analysis|
|7||6-Oct||Testing groups; Multivariate analysis of variance (MANOVA), Multi-response permutation procedure (MRPP), analysis of group similarities (ANOSIM), and Mantel’s test (MANTEL)|
|8||13-Oct||Testing groups (continued)|
|9||19-Oct||Unconstrained ordination; Principal components analysis (PCA), Principal coordinate Analysis (PCoA), and multidimensional scaling (MDS/NMDS)|
|10||27-Oct||Constrained ordination; Correspondence analysis (CA/DCA)|
|11||2-Nov||Indirect Gradient analysis|
|12||9-Nov||Direct Gradient analysis, Canonical correspondence analysis (CCA, Redundancy analysis (RDA)|
|14||25-Nov||No class – Thanksgiving break|
|15||1-Dec||Dealer’s choice #1|
|16||8-Dec||Dealer’s choice #2|