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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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使用非线性函数建模低地羊的生长.

Numan Sharif1,2, Fiona M McGovern2, Noirin McHugh3

  • 1School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland.

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概括

使用各种功能在绵羊中的生长建模显示,Gompertz和von Bertalanffy模型最好地描述了体重概况. 这些模型为遗传评估提供了良好的匹配,融合和生物学上合理的参数.

关键词:
相关性 相关性 相关性增长曲线的增长曲线增长功能 增长功能 增长功能成熟的体重 成熟的体重非线性回归是一种非线性回归.

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科学领域:

  • 动物科学动物科学
  • 量化遗传学 量化遗传学
  • 数学的建模数学建模

背景情况:

  • 准确的羊生长建模对于理解体重变化和遗传评估程序至关重要.
  • 连续测量人体重量为推导生物学上重要的参数提供了有价值的数据.

研究的目的:

  • 评估六种不同的数学函数 (Brody,Gompertz,Logistic,负指数,Richards和von Bertalanffy) 的性能,以建模低地羊的生长.
  • 探索这些函数内和跨这些函数的模型参数之间的关系.

主要方法:

  • 配备了6个生长功能,用于158,463个体重记录,来自13,090只雌性低地绵羊.
  • 通过确定系数 (R2),根平均平方误差 (RMSE) 和收率来评估模型匹配.
  • 分析了跨函数的估计增长参数 (A,B,K) 之间的相关性.

主要成果:

  • 戈珀茨和·伯塔兰菲的功能表现出了很好的适应性和收性 (100%),与生物学上有意义的参数一起.
  • 理查兹函数显示了最高的R2 (0.98),但收率较低 (82.39%).
  • 异常权重 (A) 和成熟率 (K) 参数之间的相关性在各个函数中一般是负的.

结论:

  • 建议使用Gompertz和von Bertalanffy函数来建模爱尔兰雌性低地绵羊的生长概况.
  • 这些模型为在绵羊繁殖计划中对遗传参数估计提供了强大的框架.
  • 模型选择应考虑参数的合适性,收性和生物解释性.