这是一个漫长的道路到顶部 (如果你想biplot):一个回归到基础的视角,在R的主要组件biplots的实施
Ettore Settanni1, Jagjit Singh Srai1
1Institute for Manufacturing, University of Cambridge, 17 Charles Babbage Road, Cambridge, CB3 0FS UK.
Quality & quantity
|February 23, 2026
概括
主要组件分析 (PCA) 和双图是常见的,但它们的R实现可能会产生差异. 这项研究揭示了R中的微妙计算选择如何导致意想不到的结果,即使是在真实世界的数据中.
科学领域:
- 统计 统计 统计 统计
- 数据可视化 数据可视化
- 计算科学 计算科学
背景情况:
- 主要组件分析 (PCA) 和双图是广泛使用的统计技术.
- 它们在像R这样的统计软件中的实现是常见的,导致用户假设一致的输出.
- 了解潜在的计算过程对于准确的解释至关重要.
研究的目的:
- 为了比较PCA和双地图在R.的实施情况.
- 识别和说明从计算选择中产生的潜在差异.
- 为评估这些实施的准确性提供一个框架.
主要方法:
- 利用对PCA和双图的常见计算构建块的软件不可知论理解.
- 分析R实现中的差异.
- 使用从临床试验供应链数据中简化案例研究进行评估.
- 开发一个评估网格来评估计算逻辑.
主要成果:
- 在R中PCA和双图输出中的差异可能来自看似微不足道的计算选择.
- 对于那些只依赖软件输出的用户来说,这些差异可能是难以捉摸的.
- 现实世界的数据,例如临床试验供应链,突出了这些实施特定的变化.
- 预期的理论等价性往往在实践中没有保留.
结论:
- 用户应该意识到在R.内PCA和双线实现中的潜在差异.
- 对于可靠的数据分析,需要对计算基础有更深入的理解.
- 该研究强调了审查软件输出的重要性,而不仅仅是表面水平的结果.
- 对于软件特定的计算选择进行进一步的调查是有必要的,以确保稳健的统计实践.
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