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Parameters Estimation Methods in Generalized Linear Mixed Models (GLMMs) Applied in Ecology: A Critical Review

Bruno E. Lokonon, Romuald Beh Mba, Micheline Gbeha, Romain Glèlè Kakaï

Abstract



The use of GLMMs is widespread in ecology since last decades. However, GLMMs likelihood function is analytically intractable. As a result, various approximation methods have been introduced with different degrees of accuracy. This study assesses the frequency of usage of different GLMMs estimation methods in ecology and makes a comprehensive discussion of these methods to deepen the understanding of users. Original articles in ecology from 2007 to 2016 were identified via keywords searching using web search engines. A total of 802 articles were selected. The usage of GLMMs increases exponentially from 2007 to 2016. Thereafter, 297 papers were sampled through careful reading of their abstracts. Ten estimation methods were reported and the most used were penalized quasi-likelihood (35.02 %) and Laplace Approximation (28.28 %). Useful expected information from GLMMs was not notified in several articles. Random components were not described in 220 articles (74.07%). Overdispersion was evaluated in only 23.23 % of the articles. It is important that users of GLMMs check elementary statistical conditions and report appropriate information from their findings.

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