Critical points on growth curves in autoregressive and mixed models - Pinho, Sheila Zambello de [UNESP]; Carvalho, Lidia Raquel de [UNESP]; Mischan, Martha Maria [UNESP]; Souza Passos, Jose Raimundo de [UNESP] (Universidade de São Paulo (USP), 2014)
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.