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Hierarchical and Mixed Effects Models in R
4.7(186)23k studentsADVANCEDEnglish
Korshub's take
Overview
This advanced R course covers linear and generalized linear mixed-effects models for complex data structures. Starting with a review of slopes and intercepts, you learn to incorporate random effects, work with binary and count data, and handle repeated-measures analysis when subjects are observed over time intervals.
Who It Is For
Advanced R users, statisticians, and data analysts who work with hierarchical, grouped, or longitudinal datasets requiring mixed-effects modeling.
Key Takeaways
- Model random effects to capture variation across groups and complex data structures.
- Apply linear and generalized linear mixed-effect regressions for binary and count responses.
- Conduct repeated-measures analysis for longitudinal data collected over time intervals.
About this course
This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You'll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you'll work with real data to answer interesting questions using mixed-effects models.
Price
Free
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