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心理研究中的交叉验证和预测指标:不要忽略离开-一个-离开
Diego Iglesias1, Miguel A Sorrel2, Ricardo Olmos2
1Faculty of Psychology, Department of Social Psychology and Methodology, Universidad Autónoma de Madrid, 6 Iván Pavlov Street, Cantoblanco Campus, 28049, Madrid, Spain. diego.iglesias@uam.es.
Behavior research methods
|February 3, 2025
概括
本研究引入了一种新的交叉验证 (CV) 方法,用于估计心理研究中的预测误差. 抛出一个 (LOO) 方法显示了R平方度量表的优异性能,提高了预测建模的准确性.
科学领域:
- 心理学研究方法 心理学研究方法
- 统计建模 统计建模
- 行为科学 行为科学
背景情况:
- 越来越多的人对将解释性和预测性研究纳入心理学感兴趣.
- 交叉验证 (CV) 对于估计预测错误至关重要,但需要适应特定环境.
- 现有的CV方法对于特定的预测指标 (如R平方) 有局限性.
研究的目的:
- 评估不同CV方法的性能,以估计回归分析中的预测误差.
- 为了解决目前CV实践中的R平方度的限制.
- 提出和验证一个改进的CV方法用于R平方估计.
主要方法:
- 各种CV技术的比较,包括k-fold和leave-one-out (LOO).
- 在回归分析中使用R平方和另一个指标进行性能评估.
- 两个蒙特卡洛模拟研究和分析许多实验室复制项目的数据.
- 在R包中讨论的CV方法的实施OutR2.
主要成果:
- 抛出一个 (LOO) 交叉验证方法始终显示出估计预测错误的最佳性能.
- 拟议的LOO方法克服了R平方度的传统方法的局限性.
- R包OutR2提供了这些CV方法的可访问的实现.
结论:
- 抛出一个方法 (LOO) 是一种可靠且优越的方法,用于估计心理研究中的预测误差,特别是对R平方度量.
- 准确的预测错误估计对于成功整合预测和解释性研究至关重要.
- 该OutR2 R套件促进了在心理学研究中应用先进的CV技术.
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