Accounting for missing actors in interaction network inference from abundance data
Stéphane Robin and
Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 5, 1230-1258
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.
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:70:y:2021:i:5:p:1230-1258
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