Model Validation for hurdle GLMM with count data in glmmTMB

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Summary

The problem at hand involves model validation for a zero-inflated hurdle GLMM with count data in the glmmTMB package. The model aims to analyze the drivers of presence/absence of conifer seedlings and the drivers of seedling abundance. The model structure includes a random effect of “unit” and an offset of plot area. The user is struggling to find a way to perform model diagnostics/validation for this complex model structure.

Root Cause

The root cause of the issue is the complexity of the model structure, which includes two different distributions: Bernoulli for presence/absence and truncated negative binomial for positive seedling counts. This complexity makes it challenging to use traditional model validation packages like DHARMa and performance.

Why This Happens in Real Systems

This issue occurs in real systems because:

  • Zero-inflated data is common in ecological and environmental studies
  • Complex model structures are often necessary to account for multiple factors and relationships
  • Limited guidance is available for model diagnostics/validation of complex models

Real-World Impact

The impact of this issue is:

  • Difficulty in validating model results, which can lead to incorrect conclusions and poor decision-making
  • Increased time and effort spent on manual calculations for model validation
  • Limited ability to compare models and select the best one

Example or Code

pico_model <- glmmTMB(
  PICO_count ~ scale(MAP) + scale(SHM) + offset(log(Plot_Area)) + (1 | unit), 
  ziformula = ~ scale(MAP) + scale(MSP) + scale(Shannon_Index) + scale(SDI_Value), 
  family = truncated_nbinom2(), 
  data = pico_model_data
)

How Senior Engineers Fix It

Senior engineers fix this issue by:

  • Breaking down the model into its components and validating each part separately
  • Using simulation-based methods to evaluate model performance
  • Developing custom code to perform model diagnostics/validation
  • Collaborating with statisticians and other experts to ensure best practices

Why Juniors Miss It

Juniors may miss this issue because:

  • Lack of experience with complex model structures and zero-inflated data
  • Limited knowledge of model diagnostics/validation techniques
  • Overreliance on automated packages and tools
  • Insufficient understanding of the underlying statistical concepts and assumptions

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