Overdispersed fungus germination data: statistical analysis using R
2018
Fatoretto, Maíra Blumer | Moral, Rafael de Andrade | Demétrio, Clarice Garcia Borges | de Pádua, Christopher Silva | Menarin, Vinicius | Rojas, Víctor Manuel Arévalo | D'Alessandro, Celeste Paola | Delalibera, Italo
Proportion data from dose-response experiments are often overdispersed, characterised by a larger variance than assumed by the standard binomial model. Here, we present different models proposed in the literature that incorporate overdispersion. We also discuss how to select the best model to describe the data and present, using R software, specific code used to fit and interpret binomial, quasi-binomial, beta-binomial, and binomial-normal models, as well as to assess goodness-of-fit. We illustrate applications of these generalized linear models and generalized linear mixed models with a case study from a biological control experiment, where different isolates of Isaria fumosorosea (Hypocreales: Cordycipitaceae) were used to assess which ones presented higher resistance to UV-B radiation. We show how to test for differences between isolates and also how to statistically group isolates presenting a similar behaviour.
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