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Linear Mixed Models
Overview
Overview
Overview
A general overview of traditional nonlinear models
Table of Contents
🚀 Introduction to Linear Mixed Models
Key Assumptions of LMMs
Comparison to Other Approaches
Limitations of Traditional Approaches
How Do LMMs Address These Issues?
🚀 Core Components of LMMs
Fixed vs. Random Effects
Hierarchical Structure
Covariance Structures
Guidelines for Choosing the Right Structure
🚀 Practical Considerations and Model Implementation
Handling Missing Data in LMMs
Model Selection & Interpretation
How to Decide What Random Effects to Include?
Computational Challenges: When LMMs Become Complex
Interpreting LMM Outputs
Common Pitfalls & Best Practices
Summary of Practical Tips
Final Thoughts
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