Application of principal component analysis to the interpretation of rainwater compositional data
Application of principal component analysis to the interpretation of rainwater compositional data
复制标题
主成分分析在雨水成分数据解释中的应用
DOI：
10.1016/0003-2670(92)85192-9
复制
发表时间：
1992
影响因子：
6.2
通讯作者：
D. Littlejohn
中科院分区：
文献类型：
--
作者：
Peixun Zhang;Nelley Dudley;A. Ure;D. Littlejohn
主成分分析 (PCA) 基于非线性迭代偏最小二乘法 (NIPALS) 并结合交叉验证方法，应用于从雨水化学分析中获得的数据。获得变量之间的相关性并确定其来源。还研究了通过 PCA 对样本进行分组。还讨论了数据缩放问题以及评估数据中重要成分数量的方法的评估。
Principal component analysis (PCA), based on non-linear iterative partial least squares (NIPALS) coupled with a cross-validation approach, is applied to data obtained from the chemical analysis of rainwater. The correlation between variables is obtained and their sources identified. The classification of samples into groups by PCA is also investigated. The problem of data scaling and the evaluation of methods for assessing the number of significant components in the data are also dicussed.