INTERPRETATION OF THE RESULTS OF COMMON PRINCIPAL COMPONENTS ANALYSES
INTERPRETATION OF THE RESULTS OF COMMON PRINCIPAL COMPONENTS ANALYSES
复制标题
共同主要成分分析结果的解释
DOI：
10.1111/j.0014-3820.2002.tb01356.x
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发表时间：
2002
影响因子：
1.6
通讯作者：
P. Galpern
中科院分区：
文献类型：
--
作者：
D. Houle;J. Mezey;P. Galpern
摘要 共同主成分（CPC）分析是比较表型和遗传方差-协方差矩阵的新工具。 CPC 是作为一种数据汇总方法而开发的，但生物学家经常希望使用该方法来检测多个种群或物种中性状相关性的类似模式。为了研究 CPC 的特性，我们模拟了反映一组因果因素的数据。从统计角度来看，CPC 方法的表现符合预期，但经常给出与生物直觉相反的结果。一般来说，CPC 往往会低估矩阵共享结构的程度。由于两个结构相同的数据集中单个因果因素的差异而导致特征方差和协方差的差异，通常会导致 CPC 声明这两个数据集不相关。相反，当因果因素不同时，CPC 可以将数据集识别为具有相同的结构。在分析之前对向量重新排序可以帮助检测模式。我们敦促谨慎对待 CPC 分析结果的生物学解释。通讯编辑：T. Kawecki
Abstract Common principal components (CPC) analysis is a new tool for the comparison of phenotypic and genetic variance-covariance matrices. CPC was developed as a method of data summarization, but frequently biologists would like to use the method to detect analogous patterns of trait correlation in multiple populations or species. To investigate the properties of CPC, we simulated data that reflect a set of causal factors. The CPC method performs as expected from a statistical point of view, but often gives results that are contrary to biological intuition. In general, CPC tends to underestimate the degree of structure that matrices share. Differences of trait variances and covariances due to a difference in a single causal factor in two otherwise identically structured datasets often cause CPC to declare the two datasets unrelated. Conversely, CPC could identify datasets as having the same structure when causal factors are different. Reordering of vectors before analysis can aid in the detection of patterns. We urge caution in the biological interpretation of CPC analysis results. Corresponding Editor: T. Kawecki