Research Article Comparing covariance matrices: random skewers method compared to the common principal components model
Research Article Comparing covariance matrices: random skewers method compared to the common principal components model
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DOI：
10.1590/s1415-47572007000300027
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发表时间：
2007-03-01
影响因子：
2.1
通讯作者：
Marroig, Gabriel
中科院分区：
文献类型：
--
作者：
Cheverud, James M.;Marroig, Gabriel
随着对性状之间关系的进化和进化分支表型多样化的兴趣的增长，协方差模式的比较变得越来越普遍。本文采用随机串串（RS）、t统计和共主成分（CPC）方法对14个新大陆猴属的颅骨特征的协方差矩阵相似性进行了平行分析。我们发现CPC方法非常强大，因为有足够的样本量，它可以用来检测矩阵结构的显着差异，甚至是在进化特性几乎相同的矩阵之间，如RS结果所示。我们建议，在许多情况下，人口协方差矩阵是相同的假设是立即拒绝。更有趣和相关的问题是，两个协方差矩阵相对于它们预测的进化反应有多相似？这个问题可以通过这里描述的随机串方法来解决。
Comparisons of covariance patterns are becoming more common as interest in the evolution of relationships between traits and in the evolutionary phenotypic diversification of clades have grown. We present parallel analyses of covariance matrix similarity for cranial traits in 14 New World Monkey genera using the Random Skewers (RS), T-statistics, and Common Principal Components (CPC) approaches. We find that the CPC approach is very powerful in that with adequate sample sizes, it can be used to detect significant differences in matrix structure, even between matrices that are virtually identical in their evolutionary properties, as indicated by the RS results. We suggest that in many instances the assumption that population covariance matrices are identical be rejected out of hand. The more interesting and relevant question is, How similar are two covariance matrices with respect to their predicted evolutionary responses? This issue is addressed by the random skewers method described here.