Longitudinal model for categorical data applied in an agriculture experiment about elephant grass

Detalhes bibliográficos
Autor(a) principal: Menarin, Vinícius
Data de Publicação: 2017
Outros Autores: Lara, Idemauro Antonio Rodrigues de, Silva, Sila Carneiro da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/132793
Resumo: Experiments where the response is a categorical variable are usually carried out in many fields such as agriculture. In addition, in some situations this response has three or more levels without an order between them characterizing a multinomial (nominal) response. Statistical models for scenarios where the observations of a nominal response can be considered independent have an extensive literature, such as the baseline-category logit models. However, situations where this assumption is violated (as in longitudinal studies) require specific models that take into consideration the dependence between observations. In this paper, a fairly new extension of the generalized estimating equations is applied to analyze an experiment carried out to investigate the type of vegetation observed in an elephant grass pasture, according to some management conditions over time. This extension uses local odds ratios to explain the dependence among the categories of the outcome over the repeated measurements. Two different structures were compared to describe this dependence, and the Wald test was used to select the significant variables. Further, we built confidence intervals for the predicted probabilities of occurrence of each category and assessed the results comparing observed/predicted values and using the diagnostic analysis. The results allowed to conclude that there are various significant effects for treatments and for time. The structure of local odds ratio also proved as a good way to describe the dependence between categorical responses over time.
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spelling Longitudinal model for categorical data applied in an agriculture experiment about elephant grasstype of vegetationlongitudinal multinomial datageneralized estimating equationslocal odds ratioExperiments where the response is a categorical variable are usually carried out in many fields such as agriculture. In addition, in some situations this response has three or more levels without an order between them characterizing a multinomial (nominal) response. Statistical models for scenarios where the observations of a nominal response can be considered independent have an extensive literature, such as the baseline-category logit models. However, situations where this assumption is violated (as in longitudinal studies) require specific models that take into consideration the dependence between observations. In this paper, a fairly new extension of the generalized estimating equations is applied to analyze an experiment carried out to investigate the type of vegetation observed in an elephant grass pasture, according to some management conditions over time. This extension uses local odds ratios to explain the dependence among the categories of the outcome over the repeated measurements. Two different structures were compared to describe this dependence, and the Wald test was used to select the significant variables. Further, we built confidence intervals for the predicted probabilities of occurrence of each category and assessed the results comparing observed/predicted values and using the diagnostic analysis. The results allowed to conclude that there are various significant effects for treatments and for time. The structure of local odds ratio also proved as a good way to describe the dependence between categorical responses over time.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/13279310.1590/1678-992x-2016-0067Scientia Agricola; v. 74 n. 4 (2017); 265-274Scientia Agricola; Vol. 74 Núm. 4 (2017); 265-274Scientia Agricola; Vol. 74 No. 4 (2017); 265-2741678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/132793/128819Copyright (c) 2017 Scientia Agricolainfo:eu-repo/semantics/openAccessMenarin, ViníciusLara, Idemauro Antonio Rodrigues deSilva, Sila Carneiro da2017-06-12T11:35:11Zoai:revistas.usp.br:article/132793Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2017-06-12T11:35:11Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
title Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
spellingShingle Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
Menarin, Vinícius
type of vegetation
longitudinal multinomial data
generalized estimating equations
local odds ratio
title_short Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
title_full Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
title_fullStr Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
title_full_unstemmed Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
title_sort Longitudinal model for categorical data applied in an agriculture experiment about elephant grass
author Menarin, Vinícius
author_facet Menarin, Vinícius
Lara, Idemauro Antonio Rodrigues de
Silva, Sila Carneiro da
author_role author
author2 Lara, Idemauro Antonio Rodrigues de
Silva, Sila Carneiro da
author2_role author
author
dc.contributor.author.fl_str_mv Menarin, Vinícius
Lara, Idemauro Antonio Rodrigues de
Silva, Sila Carneiro da
dc.subject.por.fl_str_mv type of vegetation
longitudinal multinomial data
generalized estimating equations
local odds ratio
topic type of vegetation
longitudinal multinomial data
generalized estimating equations
local odds ratio
description Experiments where the response is a categorical variable are usually carried out in many fields such as agriculture. In addition, in some situations this response has three or more levels without an order between them characterizing a multinomial (nominal) response. Statistical models for scenarios where the observations of a nominal response can be considered independent have an extensive literature, such as the baseline-category logit models. However, situations where this assumption is violated (as in longitudinal studies) require specific models that take into consideration the dependence between observations. In this paper, a fairly new extension of the generalized estimating equations is applied to analyze an experiment carried out to investigate the type of vegetation observed in an elephant grass pasture, according to some management conditions over time. This extension uses local odds ratios to explain the dependence among the categories of the outcome over the repeated measurements. Two different structures were compared to describe this dependence, and the Wald test was used to select the significant variables. Further, we built confidence intervals for the predicted probabilities of occurrence of each category and assessed the results comparing observed/predicted values and using the diagnostic analysis. The results allowed to conclude that there are various significant effects for treatments and for time. The structure of local odds ratio also proved as a good way to describe the dependence between categorical responses over time.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/132793
10.1590/1678-992x-2016-0067
url https://www.revistas.usp.br/sa/article/view/132793
identifier_str_mv 10.1590/1678-992x-2016-0067
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/132793/128819
dc.rights.driver.fl_str_mv Copyright (c) 2017 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Scientia Agricola
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 74 n. 4 (2017); 265-274
Scientia Agricola; Vol. 74 Núm. 4 (2017); 265-274
Scientia Agricola; Vol. 74 No. 4 (2017); 265-274
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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