Evaluation of the critical points of the most adequate nonlinear model in adjusting growth data of ‘green dwarf’ coconut fruits

Silva, Édipo Menezes da; Frühauf, Ariana Campos; Silva, Edilson Marcelino; Muniz, Joel Augusto; Fernandes, Tales Jesus; Silva, Victor Ferreira da

doi:10.1590/0100-29452021726

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…No referido trabalho, mostra-se que o modelo de regressão linear simples não é o mais apropriado para a análise dos dados, porém, pela análise gráfica, foi possível ajustar uma curva de crescimento que melhor se adéque aos dados. O termo curva de crescimento é geralmente utilizado para descrever curvas que apresentam o comportamento de medidas de dimensão ao longo do tempo (Silva et al, 2021). Curvas de crescimento de animais, plantas e frutos são comumente estudadas por regressão não lineares, como em Fernandes et al (2019), Jane et al (2019) e Silva et al (2020), respectivamente.…”

Section: Introductionunclassified

O modelo normal não linear

Fonseca

2022

RSD

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Quando nos referimos à análise de dados, existe uma gama de modelos que se ajustam aos dados, como modelos de regressão linear e também modelos de regressão não linear. Na estatística a regressão não linear é uma forma da análise de regressão, em que inicialmente uma certa função matemática é ajustada aos dados, é importante ressaltar que esse ajustamento pode ser feito por meio qualitativo usando um diagrama de dispersão. O objetivo principal deste artigo será abordar a técnica de regressão normal não linear, onde será abordado alguns métodos para a estimação dos parâmetros do modelo normal não linear, a precisão dos estimadores, intervalos de confiança, testes de hipóteses, alguns resultados assintóticos e a estimação da variância. Por fim, será analisado um conjunto de dados referente a Coelhos Europeus para demonstrar a aplicabilidade do modelo não linear, em que inicialmente tentamos ajustar o conjunto de dados ao modelo normal linear onde não é obtido êxito no ajustamento e em seguida é considerado o modelo normal não linear, que foi o modelo que melhor se ajustou aos dados.

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Section: Introductionunclassified

O modelo normal não linear

Fonseca

2022

RSD

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“…Brazil is the world's largest coffee producer and exporter, accounting for about 35% of all coffee trade, and is the second largest consumer market (Voltolini et al, 2020). Studies describing growth curves of fruits with identifications of his critical points can help the producer to identify possible points of intervention during fruit development, thus obtaining a better final quality of the product (Silva et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

et al. 2022

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Coffee is one of the main products of Brazilian agriculture and the country is currently the largest producer and exporter in the world. The coffee fruit has a double sigmoidal growth pattern, however, as well as in other fruits that also show such a growth pattern, the authors generally do not estimate parameters of regression models to describe such curve. In the study of fruit growth curves, the sample size is generally small, so the estimation of the parameters should preferably be done by the Bayesian methodology, since a priori information is incorporated, reducing the effects of having few observations. The Markov Chain Monte Carlo algorithms are the most used computational tool in Bayesian statistics. However, these generate dependent samples, can be complicated to implement and, mainly, to teach. There are also other alternatives to the MCMC algorithms to obtain approximations of integrals of interest in Bayesian inference, the main ones are based on the importance resampling techniques. The objective of this work is to use Bayesian inference with the weighted importance resampling technique in the estimation of parameters of double sigmoidal nonlinear regression models to the description of coffee fruit growth. The double nonlinear logistic model was used in the description of the accumulation of fresh weight in coffee fruits. All prioris used have Beta distribution and were obtained by the called prior of specialist technique. Bayesian methodology was efficient, since it provided parameters with practical interpretation to coffee fruit growth, consistent with the reality. Thus, Bayesian inference by weighted importance resampling was a good alternative for the parameters estimation of nonlinear double sigmoid regression models. The logistic model showed that the growth of coffee fruits is more intense in the first sigmoid (until 162 DAF)of the growth curve and stabilizes in its final weight after 262 daf.

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Adjustment of mixed nonlinear models on Blackberry fruit growth

Silva,

Tadeu,

Silva

et al. 2023

Rev. Bras. Frutic.

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Blackberry fruits belong to the genus Rubus, are fruits more cultivated in temperate climate in the summer, with low luminosity and low temperature in the winter. These fruits have as characteristic the quickperishingafter harvest and regression models, more specifically, nonlinear models, single or double sigmoid growth curve, are more recommended to model the growth of living beings. Several authors have used these models, considering only the average data of individuals under study; however, they do not consider the variability between them. One way to better capture the variability between individuals is by using mixed-effects nonlinear models that, by definition, combine the fixed and random part in the same model. Data used in this work were diameter and length of ‘Choctaw’ blackberry fruits, . The random effects of models were tested on parameters, with some steps, in order to reach the most appropriate model. For fixed-effects models, the least squares method was used, and for mixed models, the restricted likelihood was used. To reach the model that best fits data, the fit quality criteria (R2, AIC c and TRV) were used. For fruit diameter, the simple sigmoid nonlinear model was the logistic with random effect in ß1 and ß2, and for fruit length, the model was the Logistic + Logistic, with random effect in ß1 and ß4.

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Evaluation of the critical points of the most adequate nonlinear model in adjusting growth data of ‘green dwarf’ coconut fruits

Cited by 10 publications

References 30 publications

O modelo normal não linear

O modelo normal não linear

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

Adjustment of mixed nonlinear models on Blackberry fruit growth

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