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首页> 外文期刊>Scientia Agricola >Genetic parameters for post weaning growth of Nellore cattle using polinomyals and trigonometric functions in random regression models
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Genetic parameters for post weaning growth of Nellore cattle using polinomyals and trigonometric functions in random regression models

机译:随机回归模型中使用政治学和三角函数的内罗尔牛断奶后生长的遗传参数

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Covariance functions and random regression models have been considered as an alternative for data adjustment, in sequence, stemming from the same animal along time and which presents a structured pattern of covariance. Aiming to evaluate the performance of random regression models based on the Legendre, modified Jacobi and trigonometric functions, data concerning the weights of Nellore breed animals were used from birth to the 800th day of life, in models that assumed direct additive and animal permanent environmental effects coefficients. The Schwarz Bayesian information criterion (BIC) led to the selection of the models Legendre of order six (ML6), Jacobi of order five (MJ5) and trigonometric of order six (MT6), the ML6 model presenting the lowest BIC. At the extremity of the interval, the MJ5 model presented lower variance of component estimates than those obtained through the ML6 model, however the estimates were in accordance to the medium part of the interval; while the estimates from the MT6 model were oscillating and different from those obtained through the other models. At the extremity of the interval, the heritability coefficient estimates (2) obtained through the MJ5 model were lower than those obtained through the ML6 model, however, in the medium part of the interval, they were in accordance, remaining between 0.2 and 0.3. The values obtained through the MT6 model were different from those obtained through the other models, remaining between 0.35 and 0.40 on the first 285th days and then dropping to 0.01 on the 800th days of life. The means of the estimated growth curves started to distance from the data mean tendency from the 470th days on, and in this interval, the MT6 model was the most suitable.
机译:协方差函数和随机回归模型已被认为是顺次从同一只动物身上顺次进行数据调整的一种替代方法,并呈现出协方差的结构化模式。为了评估基于Legendre,改良的Jacobi函数和三角函数的随机回归模型的性能,在假设直接加性和动物永久环境影响的模型中,使用了从出生到出生的第800天有关Nellore品种动物体重的数据系数。 Schwarz贝叶斯信息准则(BIC)导致选择了六阶勒让德(ML6),五阶Jacobi(MJ5)和六阶三角(MT6)的模型,而ML6模型的BIC最低。在间隔的最末端,MJ5模型的分量估计值方差比通过ML6模型获得的估计值低,但是估计值是根据间隔的中间部分得出的;而来自MT6模型的估算值在波动,并且与通过其他模型获得的估算值不同。在间隔的最末端,通过MJ5模型获得的遗传系数估计值(2)低于通过ML6模型获得的遗传系数估计值,但是,在间隔的中间部分,它们一致,保持在0.2到0.3之间。通过MT6模型获得的值与通过其他模型获得的值不同,在生命的第285天保持在0.35到0.40之间,然后在生命的第800天下降到0.01。从第470天开始,估算的生长曲线的平均值开始偏离数据平均趋势,在此间隔内,MT6模型最合适。

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