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Accounting for missing actors in interaction network inference from abundance data

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  • Raphaëlle Momal
  • Stéphane Robin
  • Christophe Ambroise

Abstract

Network inference aims at unravelling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies. In the context of count data, we introduce a mixture of Poisson log‐normal distributions with tree‐shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological data sets. The corresponding R package is available from github.com/Rmomal/nestor.

Suggested Citation

  • Raphaëlle Momal & Stéphane Robin & Christophe Ambroise, 2021. "Accounting for missing actors in interaction network inference from abundance data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1230-1258, November.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1230-1258
    DOI: 10.1111/rssc.12509
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    References listed on IDEAS

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    2. Dray, Stéphane & Dufour, Anne-Béatrice, 2007. "The ade4 Package: Implementing the Duality Diagram for Ecologists," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 22(i04).
    3. Popovic, Gordana C. & Hui, Francis K.C. & Warton, David I., 2018. "A general algorithm for covariance modeling of discrete data," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 86-100.
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