Seleção de covariáveis para ajuste de regressão logística na análise de abundância de invertebrados edáficos em diferentes agroecossistemas

• Ano de defesa: 2011
• Autor(a) principal: Oliveira, Luciane da Silva
• Orientador(a): Cecon, Paulo Roberto
• Banca de defesa: Carneiro, Antônio Policarpo Souza , Jucksch, Ivo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Regressão logística; Seleção de covariáveis; Invertebrados edáficos
• Palavras-chave em Inglês: Logistic regression; Covariates selection; Edaphic invertebrates
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS
• Link de acesso: http://locus.ufv.br/handle/123456789/4040

Logistic regression is the analysis usual statistical method used to verify the relationship between a dichotomous variable response and the interest explanatory variables. This work aimed to carry out a study about the factors influencing the invertebrates abundance on the soil under different management forms, using the logistic regression. This objective is that these invertebrates are considered excellent indicators of the use type and soil quality, working in several fundamental processes for maintaining the soil fertility and quality in agroecosystems and natural ecosystems, according to Brown et al. (1998), Hendrix et al. (2006), and Souza (2010). For covariates selection, the Collett (1994) proposal was used and the involved parameters estimators in each model, their interpretations, statistical properties, and some criteria for judging the suitability of the selected models were presented. The methodology presented by this work was applied to two real datasets (dry and rainy season). In the final adjusted model for the analyzed dataset in the dry season, it was verified that the covariates System Type, Calcium in litter, Soil organic matter, Potassium in litter, and the interaction between Calcium and Potassium in litter were important to explain the presence of more than nine individuals on the soil. In the final adjusted model for the analyzed dataset in the rainy season, the significant covariates to explain the presence of one hundred and one individuals on average on the soil were Magnesium in litter, Total organic carbon in the litter, Litter organic matter, and Ambient temperature. For two mentioned models, there were a good discriminatory performance and excellent areas under the ROC (Receiver Operating Characteristic) curve, thus confirming the validity of using logistic regression techniques for the models construction to describe the analyzed data.