Understanding Marginal Effects in GLMMs

Oct 13, 2025 by thesweetdesigns.com 46 views

Introduction

When working with count data in a repeated measures setting, it's common to use mixed-effects models to account for the variability between and within subjects. The GLMMadaptive package in R provides a convenient way to fit these models and estimate marginal effects, which are essential for understanding the relationship between the predictor variables and the response variable. However, when calculating the 95% confidence interval (CI) for the marginal coefficients using GLMMadaptive::confint and GLMMadaptive::marginal_coef, you may encounter strange results. In this article, we'll explore how to calculate a 95% CI on negative-binomial marginal coefficients and discuss the potential issues with these functions.

Understanding Marginal Effects in GLMMs

Before diving into the calculation of 95% CIs, let's briefly review what marginal effects are in the context of generalized linear mixed models (GLMMs). Marginal effects represent the change in the expected value of the response variable for a one-unit change in a predictor variable, while holding all other variables constant. In other words, they provide a measure of the effect of a predictor variable on the response variable, averaged over all levels of the other variables.

In the context of negative-binomial models, the marginal effects can be calculated using the marginal_coef function from the GLMMadaptive package. This function estimates the marginal effects by taking into account the random effects and the variance structure of the model.

Calculating 95% CIs with GLMMadaptive::confint

To calculate the 95% CI for the marginal coefficients using GLMMadaptive::confint, you can use the following syntax:

confint(mod, coef = "marginal_coef")

This will return the 95% CI for the marginal coefficients, which can be used to determine the significance of the effects.

However, you may encounter strange results, such as:

The CI may not be symmetric around the point estimate.
The CI may not be within the expected range (e.g., negative values for a coefficient that should be positive).
The CI may not be consistent with the results obtained using other methods (e.g., bootstrapping).

Calculating 95% CIs with GLMMadaptive::marginal_coef

Alternatively, you can use the marginal_coef function to estimate the marginal effects and then calculate the 95% CI using the confint function from the stats package:

marginal_coef(mod)
confint(stats::confint(mod, coef = "marginal_coef"))

This approach may provide more accurate results, but it requires additional steps and may be more computationally intensive.

Potential Issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef

There are several potential issues that may contribute to the strange results obtained using GLMMadaptive::confint and GLMMadaptive::marginal_coef:

Model misspecification: If the model is misspecified (e.g., incorrect variance structure, omitted variables), the estimates and CIs may be biased or inconsistent.
Non-normality of residuals: If the residuals are not normally distributed, the CIs may not be accurate.
Small sample size: With small sample sizes, the CIs may be wide and unreliable.
Numerical issues: The GLMMadaptive package uses numerical methods to estimate the marginal effects and CIs, which can lead to numerical issues (e.g., convergence problems, overflow errors).

Workarounds and Alternatives

To overcome the potential issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef, you can try the following workarounds and alternatives:

Use alternative packages: Consider using other packages, such as lme4 or brms, which may provide more accurate results or alternative methods for calculating CIs.
Use bootstrapping: Bootstrapping can provide a more robust estimate of the CIs, but it requires additional computational resources and may be more time-consuming.
Use Monte Carlo methods: Monte Carlo methods can provide a more accurate estimate of the CIs, but they require additional computational resources and may be more time-consuming.
Check model assumptions: Verify that the model assumptions are met (e.g., normality of residuals, independence of observations).

Conclusion

Calculating 95% CIs for negative-binomial marginal coefficients using GLMMadaptive::confint and GLMMadaptive::marginal_coef can be challenging due to potential issues with model misspecification, non-normality of residuals, small sample size, and numerical issues. By understanding the potential issues and using workarounds and alternatives, you can obtain more accurate and reliable results. Remember to always verify the model assumptions and check the results using alternative methods to ensure the validity of your conclusions.

Example Code

Here's an example code that demonstrates how to calculate the 95% CI for the marginal coefficients using GLMMadaptive::confint and GLMMadaptive::marginal_coef:

# Load necessary libraries
library(GLMMadaptive)
library(ggplot2)
mod <- mixed_model(fixed = freq ~ timepoint + binary_factor1 + ...,
random = ~ 1 | subject,
family = negative_binomial())
confint(mod, coef = "marginal_coef")
marginal_coef(mod)
confint(stats::confint(mod, coef = "marginal_coef"))

Note that this is just an example code, and you should modify it to fit your specific needs and data.

Introduction

In our previous article, we discussed how to calculate 95% confidence intervals (CIs) on negative-binomial marginal coefficients using the GLMMadaptive package in R. However, we also highlighted some potential issues with the GLMMadaptive::confint and GLMMadaptive::marginal_coef functions. In this Q&A article, we'll address some common questions and concerns related to calculating 95% CIs on negative-binomial marginal coefficients with GLMMadaptive.

Q: What are the potential issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef?

A: The potential issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef include model misspecification, non-normality of residuals, small sample size, and numerical issues. These issues can lead to biased or inconsistent estimates and CIs.

Q: How can I verify that my model assumptions are met?

A: To verify that your model assumptions are met, you can use diagnostic plots and tests, such as:

Residual plots: Plot the residuals against the fitted values to check for non-normality and non-constant variance.
Q-Q plots: Plot the residuals against the quantiles of a normal distribution to check for normality.
Levene's test: Test for non-constant variance using Levene's test.
Breusch-Pagan test: Test for non-normality of residuals using the Breusch-Pagan test.

Q: What are some alternative methods for calculating 95% CIs on negative-binomial marginal coefficients?

A: Some alternative methods for calculating 95% CIs on negative-binomial marginal coefficients include:

Bootstrapping: Use bootstrapping to estimate the CIs.
Monte Carlo methods: Use Monte Carlo methods to estimate the CIs.
Alternative packages: Use alternative packages, such as lme4 or brms, which may provide more accurate results or alternative methods for calculating CIs.

Q: How can I troubleshoot issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef?

A: To troubleshoot issues with GLMMadaptive::confint and GLMMadaptive::marginal_coef, you can try the following:

Check the model assumptions: Verify that the model assumptions are met.
Check the data: Verify that the data are correctly formatted and that there are no missing values.
Check the model specification: Verify that the model is correctly specified and that the fixed and random effects are correctly defined.
Check the output: Verify that the output is correct and that the CIs are within the expected range.

Q: Can I use GLMMadaptive::confint and GLMMadaptive::marginal_coef with other types of models?

A: Yes, you can use GLMMadaptive::confint and GLMMadaptive::marginal_coef with other types of models, such as:

Poisson models: Use GLMMadaptive::confint and GLMMadaptive::marginal_coef with Poisson models to estimate the CIs on the marginal coefficients.
Binomial models: Use GLMMadaptive::confint and GLMMadaptive::marginal_coef with binomial models to estimate the CIs on the marginal coefficients.

Q: How can I cite the GLMMadaptive package in my research?

A: To cite the GLMMadaptive package in your research, you can use the following citation:

@Manual{GLMMadaptive,
  title = {GLMMadaptive: Generalized Linear Mixed Models using Adaptive Quadrature},
  author = {Sebastian Meyer},
  year = {2022},
  note = {R package version 3.0.0},
  url = {https://CRAN.R-project.org/package=GLMMadaptive}
}

Note that this is just an example citation, and you should check the package documentation for the most up-to-date citation information.

Conclusion

Calculating 95% CIs on negative-binomial marginal coefficients with GLMMadaptive can be challenging due to potential issues with model misspecification, non-normality of residuals, small sample size, and numerical issues. By understanding the potential issues and using alternative methods and workarounds, you can obtain more accurate and reliable results. Remember to always verify the model assumptions and check the results using alternative methods to ensure the validity of your conclusions.