Scientific Reports (Jul 2024)

Comparison of WAIC and posterior predictive approaches for N-mixture models

  • Heather E. Gaya,
  • Alison C. Ketz

DOI
https://doi.org/10.1038/s41598-024-66643-4
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 10

Abstract

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Abstract Hierarchical models are common for ecological analysis, but determining appropriate model selection methods remains an ongoing challenge. To confront this challenge, a suitable method is needed to evaluate and compare available candidate models. We compared performance of conditional WAIC, a joint-likelihood approach to WAIC (WAICj), and posterior-predictive loss for selecting between candidate N-mixture models. We tested these model selection criteria on simulated single-season N-mixture models, simulated multi-season N-mixture models with temporal auto-correlation, and three case studies of single-season N-mixture models based on eBird data. WAICj proved more accurate than the standard conditional formulation or posterior-predictive loss, even when models were temporally correlated, suggesting WAICj is a robust alternative to model selection for N-mixture models.

Keywords