Multivariate Generalized Linear Mixed Models (MGLMMs) in R

Multivariate Generalized Linear Mixed Models (MGLMMs) are an advanced class of statistical models designed to analyze multiple correlated response variables that follow non-Gaussian distributions and arise from hierarchical or clustered data structures. These models extend Generalized Linear Mixed Models (GLMMs) by simultaneously modeling several outcomes while accounting for within-subject or within-cluster correlations.

MGLMMs are especially useful in domains such as biostatistics, psychometrics, and ecology, where repeated measurements, longitudinal data, or nested sampling designs are common. By incorporating both fixed effects (systematic influences) and random effects (subject-specific variability), MGLMMs provide a flexible and robust framework for inference.

Advantages of MGLMMs:

Handle correlated outcomes.
Accommodate non-normal response distributions (e.g., binary, count).
Incorporate hierarchical structures via random effects.
Joint modeling improves efficiency and consistency of parameter estimates.

Model Specification

Let $Yij=(Yij1,Yij2,…,Yijp)TY_{ij} = (Y_{ij1}, Y_{ij2}, \ldots, Y_{ijp})^T$ denote a vector of $pp$ response variables for subject $ii$ at occasion $jj$ . The MGLMM can be written as:

$gk(E[Yijk∣bi])=XijkTβk+ZijkTbik,k=1,…,pg_k(\mathbb{E}[Y_{ijk} | b_i]) = \mathbf{X}_{ijk}^T\boldsymbol{\beta}_k + \mathbf{Z}_{ijk}^T\mathbf{b}_{ik}, \quad k = 1, \ldots, p$

Where:

$gk(⋅)g_k(\cdot)$ : Link function for the $kk$ -th outcome (e.g., logit, log).
$Xijk\mathbf{X}_{ijk}$ : Covariates associated with fixed effects $βk\boldsymbol{\beta}_k$ .
$Zijk\mathbf{Z}_{ijk}$ : Covariates associated with random effects $bik\mathbf{b}_{ik}$ .
$bi=(bi1,…,bip)∼N(0,D)\mathbf{b}_i = (\mathbf{b}_{i1}, \ldots, \mathbf{b}_{ip}) \sim \mathcal{N}(0, \mathbf{D})$ : Multivariate normal random effects capturing within-subject correlation.

Assumptions:

Responses are conditionally independent given the random effects.
$Var(Yijk∣bi)=ϕkVk(μijk)\text{Var}(Y_{ijk} | b_i) = \phi_k V_k(\mu_{ijk})$ , where $ϕk\phi_k$ is a dispersion parameter.
Cross-covariance between random effects models indicates dependencies among outcomes.

Multivariate Generalized Linear Mixed Models (MGLMMs) in R

Download:

Implementation in R

Several R packages support MGLMMs. Below is a step-by-step guide using glmmTMB, MCMCglmm, and brms for Bayesian approaches.

Data Preparation

library(glmmTMB)
data(Salamanders)
str(Salamanders) # Binary response: Presence/absence across sites and species

Fitting a Bivariate Model (e.g., Count and Binary Responses)

# Example using glmmTMB for two outcomes with random effects
fit <- glmmTMB(cbind(count, binary) ~ spp * mined + (1 | site),
               data = mydata,
               family = list(poisson(), binomial()))
summary(fit)

Using MCMCglmm for Multivariate Bayesian GLMMs

library(MCMCglmm)
prior <- list(R = list(V = diag(2), nu = 0.002),
              G = list(G1 = list(V = diag(2), nu = 0.002)))

fit <- MCMCglmm(cbind(trait1, trait2) ~ trait - 1 + trait:(fixed_effects),
                random = ~ us(trait):ID,
                rcov = ~ us(trait):units,
                family = c("categorical", "poisson"),
                data = mydata,
                prior = prior,
                nitt = 13000, burnin = 3000, thin = 10)
summary(fit)

Model Diagnostics

Check convergence (trace plots, effective sample size)
Use DHARMa for residual diagnostics with glmmTMB
Posterior predictive checks with bayesplot or pp_check in brms

Case Study: Predicting Educational Outcomes

Dataset: Simulated dataset with students (nested in schools), outcomes: math score (Gaussian) and pass/fail (binary).

Research Question:

How do student-level and school-level predictors influence academic performance and passing probability?

Modeling:

fit <- glmmTMB(cbind(math_score, passed) ~ gender + SES + (1 | school_id),
               data = edu_data,
               family = list(gaussian(), binomial()))
summary(fit)

Interpretation:

Fixed effects show the average association of covariates with each outcome.
Random effects estimate school-specific deviations.
Correlation structure shows how math scores and passing status co-vary within schools.

Visualization:

library(ggplot2)
# Predicted vs Observed
edu_data$pred_math <- predict(fit)[,1]
ggplot(edu_data, aes(x = pred_math, y = math_score)) +
  geom_point() + geom_smooth()

Challenges and Solutions

Common Issues:

Convergence problems: Simplify model, check starting values, use penalized likelihood.
Non-identifiability: Avoid overparameterization; regularize random effects.
Model misspecification: Perform residual diagnostics; compare with nested models.

Expert Tips:

Always examine the random effects structure.
Use informative priors in Bayesian settings.
Scale predictors to improve convergence.

Extensions and Alternatives

GEEs: Useful for marginal models but less flexible for hierarchical data.
Bayesian hierarchical models: Rich inference, handles uncertainty better.
Joint modeling: For longitudinal and survival data.

MGLMMs are most appropriate when multiple correlated outcomes are influenced by shared covariates and random effects structures.

References

McCulloch, C. E., Searle, S. R., & Neuhaus, J. M. (2008). Generalized, Linear, and Mixed Models. Wiley.
Brooks, M. E., Kristensen, K., van Benthem, K. J., et al. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal.
Hadfield, J. D. (2010). MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. Journal of Statistical Software.
Gelman, A., et al. (2013). Bayesian Data Analysis. CRC Press.
Bolker, B. M., et al. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution.

For advanced users, packages such as brms and rstanarm offer flexible Bayesian interfaces for MGLMMs, enabling greater control over model specification and inference.

Download (PDF)