Thank you for visiting my course notes. Here are some upcoming opportunities to learn from me and my colleagues in person:
|Currently no workshops are planned|
|Instructor:||Dr. Jonathan Templin|
|Office:||614 Joseph R. Pearson Hall|
|Office Hours:||Fridays: 10:30am-12:30pm or by appointment|
|Course Meeting Time:||Wednesdays from 1:30pm-4:20pm|
|Course Meeting Location:||143 Joseph R. Pearson Hall|
Brief Course Description
In this course, contemporary methods for the analysis of data that are not normally distributed are presented. Generalized linear mixed models are linear models (regression/analysis of variance) that map independent or predictor variables onto the any type of outcome space of dependent variable(s). In general, such models involve the choice of distribution for the dependent variable(s) and a link function to map predictors onto key parameters of the dependent variable(s) distribution(s). Topics covered include logistic regression (or linear models for ordinal multinomial distributions), nominal distributions (or unordered multinomial distributions), count distributions (i.e., Poisson and negative binomial regression), and distributions for censored, skewed, or otherwise irregular continuous data. Random effects or latent variables (effects for nested data of all sorts) are included for each type of model.
SAS is available in several ways:
1. For purchase online at the KU webstore (Windows OS only)
2. For free with the use of a virtual machine (all OS) by visiting http://www.sas.com/en_us/software/university-edition.html
3. For free on the KU Advanced Computing Facility Supercomputers. See http://jonathantemplin.com/advanced-computing-facility-supercomputer-information/
For all other specific information regarding general course policies, course evaluation rubrics, and grading systems, please see the course syllabus at the link below.
Helpful R Links and Resources
|Books:||R for Data Science (Grolemund and Wickham, 2015)|
|R for SAS and SPSS Users by Muenchen (2007)|
|An introduction to R by Venables et al. (2013)|
|aRrgh: a newcomer's (angry) guide to R|
|DataCamp Introduction to R|
|Cookbook for R|
|Quick References:||Reference Card #1 (various authors, noted on card)|
|Reference Card #2 (various authors, noted on card)|
Helpful SAS Links and Resources
|Book's SAS Files:||SAS Example and Data Files by Stroup (2012)|
|SAS PROC GLIMMIX Documentation:||SAS 9.3 PROC GLIMMIX|
Tentative Schedule of Course Topics
|Date||Topic & Content Link||Required Reading||Assignments|
|26-Aug||Introduction to Generalized Linear Mixed Models and Mathematical Statistics||Appendices A and B||Homework #0 (Extra Credit)
Due 2 Sep at 1:30pm
|2-Sept||Introduction to GLMMs/Models with Matrices||Chapter 1||#1: Matrices and Distributions (Due 23 Sept 2015)
Homework Data Set
|9-Sept||Research Design / Fundamentals of GLMMs||Chapter 2|
|16-Sept||Fundamentals of GLMMs||Chapter 3|
|23-Sept||Estimation of GLMMs (Part 1)||Chapter 4|
|30-Sept||Information, Standard Errors, and Newton-Raphson/Fisher Scoring||None||#2: Maximum Likelihood (Due 21 Oct 2015)
|7-Oct||The Exponential Family||None|
|21-Oct||Estimation of Generalized Linear Models||Chapter 4||#3: Estimation of Generalized Linear Models (Due 11 Nov 2015)
|28-Oct||Gaussian Linear Mixed Models||Chapter 4|
|4-Nov||ML and REML Estimation||Chapter 4|
|11-Nov||Estimation of GLMMs; Inference in GLMMs||Chapter 5||#4: REML Estimation (Due 2 Dec 2015)
|18-Nov||Examples of Gaussian GLMMs|
|9-Dec||Examples of Non-Gaussian and Doubly Generalized GLMMs|
Lee, Y., & Nelder, J. A. (1996). Hierarchical generalized linear models. Journal of the Royal Statistical Society. Series B (Methodological), 619-678. Paper link from journal.
Lee, Y., & Nelder, J. A. (2006). Double hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society: Series C (Applied Statistics), 55(2), 139-185. Paper link from journal.
Olsen, M. K., & Schafer, J. L. (2001). A two-part random-effects model for semicontinuous longitudinal data. Journal of the American Statistical Association, 96(454), 730-745. Paper link from journal.
Rose, C. E., Matin, S. W., Wannemuehler, K. A., & Plikaytis, B. D. (2006). On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of biopharmaceutical statistics, 16(4), 463-481. Paper link from journal.
Stroup, W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. Chapman & Hall.