Why analyze germination experiments using Generalized Linear Models?

• Autor(a) principal: Carvalho,Fábio Janoni
• Data de Publicação: 2018
• Outros Autores: Santana,Denise Garcia de, Araújo,Lúcio Borges de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of Seed Science
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2317-15372018000300281
• topic: AIC; ANOVA assumptions; Copaifera langsdorffii Desf; forest species

Abstract: We compared the goodness of fit and efficiency of models for germination. Generalized Linear Models (GLMs) were performed with a randomized component corresponding to the percentage of germination for a normal distribution or to the number of germinated seeds for a binomial distribution. Lower levels of Akaikes’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) combined, data adherence to simulated envelopes of normal plots and corrected confidence intervals for the means guaranteed the binomial model a better fit, justifying the importance of GLMs with binomial distribution. Some authors criticize the inappropriate use of analysis of variance (ANOVA) for discrete data such as copaiba oil, but we noted that all model assumptions were met, even though the species had dormant seeds with irregular germination.