Data/hora: 04/06/2024 00:18:23

Provedor de dados: | Ciência e Agrotecnologia
País: | Brazil
Título: | Eucalyptus cloeziana seed count data: a comparative analysis of statistical models
Autores: | Michelon,Thomas Bruno; Taconeli,Cesar Augusto; Vieira,Elisa Serra Negra; Panobianco,Maristela.
Data: | 2019-01-01
Ano: | 2019
Palavras-chave: | Generalized linear models; Poisson; ANOVA; Germination test.
Resumo: | ABSTRACT Generalized linear models (GLMs) are an extension of the linear model and include the normal, Poisson, and negative binomial distributions. Although GLMs were introduced in 1972, most seed technology studies, especially those involving count data, such as germination tests of seeds from the genus Eucalyptus, still using the analysis of variance, without analysis of the fit of other models. Thus, this study aimed to evaluate the most appropriate model in the GLM class for seed count data of Eucalyptus cloeziana. Data were obtained from a germination test using seeds from three lots of E. cloeziana. Each lot was separated by sieving into three material fractions based on size: small (&lt;0.84 mm), medium (from 1.18 to 1.00 mm), and large (&gt;1.18 mm). The data analysis was based on the use of GLMs adjusted to normal, Poisson, and negative binomial distributions, and the models were evaluated by the Akaike and Bayesian Schwartz criteria and Cook’s distance and half-normal diagnostic graphs. Compared to other adjustments, the normal distribution adjustment differed in the configuration of means submitted to the Tukey test, and although the data met all normality assumptions, the adjustment with the Poisson distribution was the most suitable for the count data from a germination test of E. cloeziana seeds.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542019000100243
Editor: | Editora da Universidade Federal de Lavras
Relação: | 10.1590/1413-7054201943020619
Formato: | text/html
Fonte: | Ciência e Agrotecnologia  v.43 2019
Fonte: | info:eu-repo/semantics/openAccess