The Problem with Mixed Effects Models

Oct 13, 2025 by talynt.org 107 views

Introduction

As a researcher, creating publication-quality regression tables is an essential aspect of data analysis and presentation. The tab_model() function in R has been a popular choice for this purpose, offering a convenient and user-friendly way to generate tables. However, when working with mixed effects models, users often encounter issues with the default output, which displays random-effects conditional coefficients instead of the desired population-averaged marginal coefficients. In this article, we will explore the possibility of adding a toggle to display marginal coefficients in tab_model() and discuss alternative solutions for achieving this goal.

The Problem with Mixed Effects Models

Mixed effects models are a powerful tool for analyzing data with both fixed and random effects. However, when it comes to displaying the results, the default output of tab_model() can be misleading. By default, tab_model() displays the random-effects conditional coefficients, which can be confusing for users who are interested in the population-averaged marginal coefficients. This is where the GLMMadaptive package comes in, which provides a function called marginal_coef() to calculate marginal coefficients.

The GLMMadaptive Package

The GLMMadaptive package is a popular choice for analyzing mixed effects models in R. One of its key features is the marginal_coef() function, which calculates marginal coefficients for mixed effects models. However, as we will discuss later, this function is not directly compatible with tab_model().

The Modelsummary Package

The modelsummary package is another popular choice for creating publication-quality regression tables in R. One of its key features is the ability to display marginal coefficients in the output table. However, as we will discuss later, this package does not support the GLMMadaptive package.

Current Workarounds

So, what are the current workarounds for displaying marginal coefficients in tab_model()? There are a few options available:

Use the marginal_coef() function from the GLMMadaptive package: This function can be used to calculate marginal coefficients, but it is not directly compatible with tab_model().
Use the modelsummary package: This package provides a way to display marginal coefficients in the output table, but it does not support the GLMMadaptive package.
Manually overwrite the coefficient values: This involves manually extracting the coefficient values from the marginal_coef() function and overwriting them in the tab_model() output table.

Proposed Solution

Given the limitations of the current workarounds, a proposed solution would be to add a toggle to tab_model() to display marginal coefficients. This would involve modifying the tab_model() function to accept an additional argument, such as marginal = TRUE, which would trigger the display of marginal coefficients.

Implementation

To implement this solution, the following steps would be required:

Modify the tab_model() function: The tab_model() function would need to be modified to accept an additional argument, such as marginal = TRUE.
Add a check for marginal coefficients: The function would need to check if the marginal argument is set to TRUE and, if so, display the marginal coefficients instead of the default random-effects conditional coefficients.
Integrate with the GLMMadaptive package: The function would need to be integrated with the GLMMadaptive package to allow for the calculation of marginal coefficients using the marginal_coef() function.

Example Code

Here is an example of how the modified tab_model() function could be implemented:

tab_model <- function(model, marginal = FALSE) {
  # Check if marginal argument is set to TRUE
  if (marginal) {
    # Calculate marginal coefficients using GLMMadaptive package
    marginal_coef <- GLMMadaptive::marginal_coef(model)
    # Display marginal coefficients
    print(marginal_coef)
  } else {
    # Display default output
    print(tab_model_default(model))
  }
}

Conclusion

In conclusion, displaying marginal coefficients in tab_model() is a feature that would greatly enhance the usability of this function. By adding a toggle to display marginal coefficients, users would be able to easily switch between the default random-effects conditional coefficients and the population-averaged marginal coefficients. This would make tab_model() an even more powerful tool for creating publication-quality regression tables in R.

Future Work

Future work could involve further modifying the tab_model() function to allow for more flexibility in displaying marginal coefficients. For example, users could be able to specify which coefficients to display or how to display them. Additionally, the function could be integrated with other packages, such as modelsummary, to provide a more comprehensive solution for creating publication-quality regression tables.

References

Drizopoulos, D. (2020). GLMMadaptive: Generalized Linear Mixed Models using Adaptive Quadrature. R package version 2.4.0.
Leeb, H., & Pötscher, B. M. (2005). Model selection and inference: Facts and fiction. Econometric Theory, 21(1), 21-59.
Zeileis, A. (2004). Object-oriented computation of sandwich estimators. Journal of Statistical Software, 9(10), 1-16.
Q&A: Displaying Marginal Coefficients in tab_model() =====================================================

Q: What is the main issue with displaying marginal coefficients in tab_model()?

A: The main issue is that tab_model() displays random-effects conditional coefficients by default, whereas users often want to display population-averaged marginal coefficients.

Q: What is the difference between random-effects conditional coefficients and marginal coefficients?

A: Random-effects conditional coefficients are the coefficients that are estimated for each level of the random effect, whereas marginal coefficients are the coefficients that are estimated for the population as a whole.

Q: How can I calculate marginal coefficients using the GLMMadaptive package?

A: You can use the marginal_coef() function from the GLMMadaptive package to calculate marginal coefficients.

Q: Can I use the modelsummary package to display marginal coefficients?

A: Yes, the modelsummary package provides a way to display marginal coefficients in the output table. However, it does not support the GLMMadaptive package.

Q: How can I manually overwrite the coefficient values in the tab_model() output table?

A: You can manually extract the coefficient values from the marginal_coef() function and overwrite them in the tab_model() output table.

Q: What is the proposed solution for displaying marginal coefficients in tab_model()?

A: The proposed solution is to add a toggle to tab_model() to display marginal coefficients. This would involve modifying the tab_model() function to accept an additional argument, such as marginal = TRUE, which would trigger the display of marginal coefficients.

Q: How would the modified tab_model() function work?

A: The modified tab_model() function would check if the marginal argument is set to TRUE and, if so, display the marginal coefficients instead of the default random-effects conditional coefficients. It would also integrate with the GLMMadaptive package to allow for the calculation of marginal coefficients using the marginal_coef() function.

Q: What are the benefits of displaying marginal coefficients in tab_model()?

A: The benefits include:

Improved accuracy: Marginal coefficients provide a more accurate representation of the population-averaged effects.
Increased flexibility: Users can choose to display either random-effects conditional coefficients or marginal coefficients.
Enhanced usability: The modified tab_model() function would provide a more user-friendly experience for users who want to display marginal coefficients.

Q: What are the next steps for implementing the proposed solution?

A: The next steps would involve:

Modifying the tab_model() function to accept the marginal argument.
Integrating the tab_model() function with the GLMMadaptive package.
Testing the modified function to ensure it works as expected.

Q: What are the potential challenges and limitations of the proposed solution?

A: The potential challenges and limitations include:

Complexity: Modifying the tab_model() function to accept the marginal argument and integrating it with the GLMMadaptive package could be complex.
Compatibility: The modified function may not be compatible with all versions of R or the GLMMadaptive package.
Performance: The modified function may have performance issues if it is not optimized correctly.

Q: What are the potential future developments for displaying marginal coefficients in tab_model()?

A: Potential future developments include:

Further modifying the tab_model() function to allow for more flexibility in displaying marginal coefficients.
Integrating the tab_model() function with other packages, such as modelsummary, to provide a more comprehensive solution for creating publication-quality regression tables.
Developing new functions or packages that provide more advanced features for displaying marginal coefficients.