A Gentle Introduction to Generalized Linear Mixed Models
Generalized linear mixed models (GLMMs) are incredibly useful—but they’re also a hard nut to crack.
As an extension of generalized linear models, GLMMs include both fixed and random effects. They are particularly useful when an outcome variable and a set of predictor variables are measured repeatedly over time and the outcome variable is a binary, nominal, ordinal or count variable. These models accommodate nesting of subjects in higher level units such as schools, hospitals, etc., and can also incorporate predictor variables collected at these higher levels.
In this webinar, we’ll provide a gentle introduction to GLMMs, discussing issues like:
- What GLMMs are and when to use them
- What to consider when specifying GLMMs
- How to compare competing GLMMs to identify the most adequate model for the data
- How to interpret model fit information
- What types of inference and prediction are supported by GLMMs
- How to check the validity of model assumptions
You’ll become familiar with the major issues involved in working with GLMMs so you can more easily transition to using these models in your work.
Date and Time
Wednesday, October 19, 2016 at 3:00 pm EDT (GMT -4)
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About the Instructor
Dr. Isabella Ghement is the principal of Ghement Statistical Consulting Company Ltd., an independent statistical consulting and training firm in Richmond, British Columbia, Canada.
Since 2006, Dr. Ghement has provided statistical consulting and training to clients from government, academia and industry. Her research expertise covers areas such as partially linear regression modeling, robust regression modeling and mixed treatment comparisons.
Dr. Ghement has presented a number of R short courses at conferences and an advanced regression course using R to graduate students in the Sauder School of Business at the University of British Columbia. She is also a member of the Steering Committee for the American Statistical Association’s Conference on Applied Statistical Practice 2017, where she chairs the Short Courses and Tutorials Sub-Committee.
Dr. Ghement obtained her Ph.D. in Statistics from the University of British Columbia in 2005 with a thesis on the application of partially linear models with correlated errors to the study of the health effects of air pollution in Mexico City.
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