Critical points on growth curves in autoregressive and mixed models

Detalhes bibliográficos
Autor(a) principal: Pinho,Sheila Zambello de
Data de Publicação: 2014
Outros Autores: Carvalho,Lídia Raquel de, Mischan,Martha Maria, Passos,José Raimundo de Souza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004
Resumo: Adjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.
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spelling Critical points on growth curves in autoregressive and mixed modelsAdjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.Escola Superior de Agricultura "Luiz de Queiroz"2014-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004Scientia Agricola v.71 n.1 2014reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162014000100004info:eu-repo/semantics/openAccessPinho,Sheila Zambello deCarvalho,Lídia Raquel deMischan,Martha MariaPassos,José Raimundo de Souzaeng2014-03-06T00:00:00Zoai:scielo:S0103-90162014000100004Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2014-03-06T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Critical points on growth curves in autoregressive and mixed models
title Critical points on growth curves in autoregressive and mixed models
spellingShingle Critical points on growth curves in autoregressive and mixed models
Pinho,Sheila Zambello de
title_short Critical points on growth curves in autoregressive and mixed models
title_full Critical points on growth curves in autoregressive and mixed models
title_fullStr Critical points on growth curves in autoregressive and mixed models
title_full_unstemmed Critical points on growth curves in autoregressive and mixed models
title_sort Critical points on growth curves in autoregressive and mixed models
author Pinho,Sheila Zambello de
author_facet Pinho,Sheila Zambello de
Carvalho,Lídia Raquel de
Mischan,Martha Maria
Passos,José Raimundo de Souza
author_role author
author2 Carvalho,Lídia Raquel de
Mischan,Martha Maria
Passos,José Raimundo de Souza
author2_role author
author
author
dc.contributor.author.fl_str_mv Pinho,Sheila Zambello de
Carvalho,Lídia Raquel de
Mischan,Martha Maria
Passos,José Raimundo de Souza
description Adjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.
publishDate 2014
dc.date.none.fl_str_mv 2014-02-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0103-90162014000100004
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
dc.source.none.fl_str_mv Scientia Agricola v.71 n.1 2014
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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