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使用线性回归方法来评估来自非线性模型的预测的人口准确性
Haipeng Yu1, Rohan L Fernando2, Jack C M Dekkers2
1Department of Animal Sciences, University of Florida, Gainesville, FL, United States.
Frontiers in genetics
|June 19, 2024
概括
线性回归 (LR) 方法准确地估计了基因评估的预测准确度和模型充分性,即使有非线性预测. 一个LR统计有效地识别了各种数据分区策略中的不充分模型.
科学领域:
- 量化遗传学 量化遗传学
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 传统的交叉验证方法在估计预测准确度方面存在局限性.
- 线性回归 (LR) 方法是为了解决这些局限性而开发的,它假定模型的正确性,并提供模型充分性的统计数据.
- 之前的研究证实了LR方法用于线性预测,但不用于非线性场景.
研究的目的:
- 以数学证明LR方法对基于条件平均值的非线性预测的有效性.
- 评估LR方法在检测充足与不充足模型方面的能力.
- 建立数据分区的指导方针,以优化LR方法在识别不充分模型方面的性能.
主要方法:
- 用条件平均预测器 (线性和非线性) 来证明LR方法的有效性的数学证明.
- 模拟研究使用三个不同的数据分区场景 (动物之间,根据动物内的年龄,动物之间和年龄).
- 评估两种LR统计数据,以检测它们在不同的分区条件下检测模型不足的能力.
主要成果:
- 在数学上,LR方法已被证明是有效的,用于估计条件平均预测器的人口准确性,包括表型的非线性函数.
- 一个LR统计有效地检测了所有三种模拟数据分区场景中的不充分模型.
- 其他LR统计数据的检测不充分模型的能力取决于数据分区策略,需要训练和验证集之间的预测变量差异.
结论:
- 在遗传评估中,LR方法是估计预测准确度和评估模型充分性的有效方法,特别是在处理非线性预测时.
- 一个特定的LR统计表明在识别不充分的模型方面表现优异,无论数据分区方法如何.
- 适当的数据分区对于有效应用LR方法至关重要,特别是对于依赖于培训和验证数据之间的差异的统计数据.
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