Backscoring in Principal Coordinates Analysis
Backscoring in Principal Coordinates Analysis
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DOI：
10.1080/10618600.2012.672097
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发表时间：
2012-06-01
影响因子：
2.4
通讯作者：
Faraway, Julian J.
中科院分区：
文献类型：
--
作者：
Faraway, Julian J.
主坐标分析是指从基于距离矩阵的方法（如多维缩放）获得的数据的低维投影。主成分分析还产生数据的低维投影，并且具有到数据空间和来自数据空间的显式映射的便利性，并且投影得分空间是容易获得的。从数据到分数的映射称为样本外嵌入。我们把从分数到数据的映射称为回溯。我们将讨论如何获得这些映射的主坐标分析和演示应用程序的方向，形状，功能和混合数据。函数数据的应用程序显示了如何相位和幅度变化可以一起描述。回溯法有助于解释分数的意义和模拟新的数据。再现结果所需的数据和R代码作为在线补充材料提供。
Principal coordinates analysis refers to the low-dimensional projection of data obtained from distance-matrix-based methods such as multidimensional scaling. Principal components analysis also produces a low-dimensional projection of data and has the convenience of explicit mappings to and from the data space and the projected score space being readily available. The map from data to score is called called out-of-sample embedding. We call the map from score to data, backscoring. We discuss how these mappings may be obtained for a principal coordinates analysis and demonstrate applications for orientation, shape, and functional and mixed data. The application to functional data shows how both phase and amplitude variation can be described together. Backscoring is helpful for interpreting the meaning of scores and in simulating new data. Data and R code necessary to reproduce the results are provided as online supplemental materials.