Critical points on growth curves in autoregressive and mixed models

Critical points on growth curves in autoregressive and mixed models

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...

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Main Authors: Sheila Zambello de Pinho, Lídia Raquel de Carvalho, Martha Maria Mischan, José Raimundo de Souza Passos
Format: Article
Language:English
Published: Universidade de São Paulo 2014-02-01
Series:Scientia Agricola
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004&lng=en&tlng=en
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spelling doaj-94cfc77634c24185b1695885b0deb26b2020-11-24T22:43:16ZengUniversidade de São PauloScientia Agricola1678-992X2014-02-01711303710.1590/S0103-90162014000100004S0103-90162014000100004Critical points on growth curves in autoregressive and mixed modelsSheila Zambello de Pinho0Lídia Raquel de Carvalho1Martha Maria Mischan2José Raimundo de Souza Passos3Universidade Estadual PaulistaUniversidade Estadual PaulistaUniversidade Estadual PaulistaUniversidade Estadual PaulistaAdjusting 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004&lng=en&tlng=en
collection DOAJ
language English
format Article
sources DOAJ
author Sheila Zambello de Pinho
Lídia Raquel de Carvalho
Martha Maria Mischan
José Raimundo de Souza Passos
spellingShingle Sheila Zambello de Pinho
Lídia Raquel de Carvalho
Martha Maria Mischan
José Raimundo de Souza Passos
Critical points on growth curves in autoregressive and mixed models
Scientia Agricola
author_facet Sheila Zambello de Pinho
Lídia Raquel de Carvalho
Martha Maria Mischan
José Raimundo de Souza Passos
author_sort Sheila Zambello de Pinho
title Critical points on growth curves in autoregressive and mixed models
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
publisher Universidade de São Paulo
series Scientia Agricola
issn 1678-992X
publishDate 2014-02-01
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.
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100004&lng=en&tlng=en
work_keys_str_mv AT sheilazambellodepinho criticalpointsongrowthcurvesinautoregressiveandmixedmodels
AT lidiaraqueldecarvalho criticalpointsongrowthcurvesinautoregressiveandmixedmodels
AT marthamariamischan criticalpointsongrowthcurvesinautoregressiveandmixedmodels
AT joseraimundodesouzapassos criticalpointsongrowthcurvesinautoregressiveandmixedmodels
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