This is Volume 3 of our book series, The World of Zero-Inflated Models, focusing on generalised linear latent variable models (GLLVMs). While Volumes 1 and 2 explored univariate response variables and their relationships with multiple covariates, this book dives into the analysis of datasets with multiple, correlated response variables.
GLLVMs are powerful tools for analyzing datasets where responses are inherently linked, enabling researchers to simultaneously model correlations and covariate effects. These models are described in detail by Niku et al. (2019), who provide efficient estimation approaches, and further extended by van der Veen et al. (2023) with concurrent ordination techniques that integrate unconstrained and constrained latent variable modeling.
In ecological studies, for instance, datasets often include several response variables—species abundances, behavioural metrics, or environmental factors. Instead of reducing these variables to single metrics like species richness, GLLVMs allow you to model the correlations directly, uncovering shared patterns and underlying drivers. Through practical examples, this book showcases how GLLVMs can replace multiple univariate models and provide deeper insights into complex datasets.
This volume is ideal for researchers working with large ecological, biological, or environmental datasets with more than five response variables. Whether you’re studying species interactions, behavioural ecology, or environmental impacts, this book—guided by foundational work such as Niku et al. (2019) and van der Veen et al. (2023)—will expand your analytical toolkit.
References
Niku, J., Brooks, W., Herliansyah, R., Hui, F. K. C., Taskinen, S., and Warton, D. I. (2019). Efficient estimation of generalized linear latent variable models. PLOS ONE, 14(5):e0216129. Publisher: Public Library of Science.
van der Veen, B., Hui, F. K. C., Hovstad, K. A., and O’Hara, R. B. (2023). Concurrent ordination: Simultaneous unconstrained and constrained latent variable modelling. Methods in Ecology and Evolution, 14(2):683–695. _-eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14035.
