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Master's Dissertation
DOI
10.11606/D.11.2009.tde-14102009-084734
Document
Author
Full name
Michele Barbosa
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2009
Supervisor
Committee
Lima, Cesar Goncalves de (President)
Demetrio, Clarice Garcia Borges
Malheiros, Euclides Braga
Title in Portuguese
Uma abordagem para análise de dados com medidas repetidas utilizando modelos lineares mistos
Keywords in Portuguese
Análise de dados longitudinais
Leite - Experimentos
Medidas repetidas
Modelos lineares
Software livre.
Abstract in Portuguese
No presente trabalho propôs-se uma abordagem simples visando à escolha de um modelo linear misto a ser ajustado a dados com medidas repetidas. A construção do modelo envolveu a escolha dos efeitos aleatórios, dos efeitos fixos e da estrutura de covariâncias utilizando técnicas gráficas e analíticas. O uso do Teste da Razão de Verossimilhança e dos Critérios de Informação de Akaike - AIC e de Schwarz - BIC pode levar a escolhas diferentes da estrutura de covariâncias, o que pode influenciar os resultados das inferências feitas sobre os parâmetros de efeitos fixos. A abordagem foi aplicada a conjuntos de dados resultantes de estudos agropecuários utilizando o software livre R. Foram feitas comparações dos resultados obtidos de modelos implementados com o proc mixed do SAS e com a função lme() do R, observando as vantagens e restrições destes dois softwares.
Title in English
One approach to analyzing data with repeated measures using linear mixed models
Keywords in English
Analysis of longitudinal data
Free software.
Linear models
Milk-experiments
Repeated measures
Abstract in English
In this present work was proposed a simple approach to know how to choose a linear mixed model that can be adjustable to data with repeated measures. The construction of the model involved the choice of random effects, the fixed effects and covariance structure, using graphical and analytical techniques. The use of the Likelihood Ratio Test and the Akaike Information Criteria - AIC and Schwarz - BIC can lead to different choices of the structure of covariance, which may influence the results of inferences made about the parameters of fixed effects. The approach was applied to data sets that was resulted from farming studies using the software R. Comparisons of the results of models implemented were made with the proc mixed of SAS and with the function lme() of R, noting the advantages and limitations of these two softwares.
 
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Michele_Barbosa.pdf (652.18 Kbytes)
Publishing Date
2009-10-23
 
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