How to Perform a Linear Mixed Effects Model (LMM) in R
Explore how to perform and interpret a Linear Mixed Effects Model (LMM) in R Studio with this step-by-step tutorial. Whether you're analyzing biological, ecological, or experimental data, LMM is an essential tool for modeling data with both fixed and random effects. This tutorial is designed for researchers, data scientists, and students looking to expand their statistical analysis skills using R.
In this video, you will learn:
What is a Linear Mixed Effects Model?: Understand the basics of mixed modeling and the difference between fixed and random effects.
How to Implement LMM in R: Step-by-step guide on fitting linear mixed effects models using the lme4 package in R.
Interpreting the Results: Learn how to interpret model output, including fixed effects, random effects, and model diagnostics.
Practical Applications: See a real-world example with biological data to understand how to apply LMM for repeated measures or hierarchical data structures.
Model Validation: Ensure the accuracy of your model by checking residuals and goodness-of-fit statistics.
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Disclaimer
This video is made for the sole purpose of higher education. Care is taken to provide the most accurate information. However, we can’t guarantee the accuracy of all the information in this video. Kindly do your own research before coming to any conclusions or making any decisions.
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📚 Resources:
Download the sample data used in this tutorial:
Linear Mixed Effects Model (LMM) in R: Analyzing Plant Growth by Treatment Type
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