Linear Mixed-Effects Models with R

🧠 Dive into the World of Linear Mixed Models with R!

🚀 Course Title: Linear Mixed-Effects Models with R

👩‍🏫 Instructor: Geoffrey Hubona, Ph.D.

Your Journey into Advanced Statistical Modeling Begins Here!

Are you ready to master the art of linear mixed-effects models (LMEMs) with R? This 7-session course is meticulously crafted for statisticians, researchers, and data analysts who wish to speculate, fit, interpret, evaluate, and compare estimated parameters within the framework of LMEMs.

What You'll Learn:

• Understanding LMEMs: These models are crucial when dealing with longitudinal, repeated measures, or nested data structures where traditional regression assumptions do not hold due to correlational dependencies among observations.

• Model Flexibility: We'll explore additive, non-linear, and exponential models, among others, and learn how to choose the right model for your dataset.

• Practical Application: With a focus on hands-on learning, you'll apply these concepts directly in R, with real datasets and live demonstrations.

Course Highlights:

• 📊 Model Selection: Learn how to pick the most appropriate linear model for your research question.

• 🔍 R Representation: Discover how to represent your chosen model in R's syntax, making the most of its powerful capabilities.

• 🧠 Parameter Estimation & Interpretation: Gain insights into estimating parameters within LMEMs and understand what they mean for your data and research context.

• 🔄 Model Comparison & Validation: Understand how to compare different models, interpret and report results effectively, and validate the model assumptions to ensure accuracy.

• 🌍 Real Data Application: Work with actual datasets to see how LMEMs function in real-world scenarios.

No Prior R Experience Required!

• 📚 R Primer: The course kicks off with a comprehensive primer on using R, ensuring that you're comfortable with the statistical commands and scripts needed for the rest of the course.

Why This Course?

This course is specifically designed to address the nuances of mixed-effects models, which are critical for accurate analysis in a wide range of disciplines, including psychology, education, medicine, genetics, and more. By the end of this course, you will have a solid understanding of how to handle complex data structures in R with confidence.

Course Structure:

1. Introduction to LMEMs: Understanding the theory behind mixed-effects models and their significance.

2. Choosing the Right Model: Strategies for selecting an appropriate linear model based on your data.

3. Implementation in R: Steps to represent your chosen model in R, including the use of packages like lme4 and nlme.

4. Parameter Estimation: Techniques for accurately estimating parameters within the LMEM framework.

5. Interpreting Results: Mastering the interpretation of parameter estimates and understanding their implications.

6. Model Comparison & Evaluation: Methods for comparing models, assessing model fit, and evaluating assumptions.

7. Applying Models to Real Data: Utilize your new skills on real datasets to see the practical application of LMEMs in R.

Enroll now to embark on a transformative journey into the realm of linear mixed-effects models with R! 🌟