New robust weighted averaging- and model-based methods for assessing trait-environment relationships: R-code

This is the R-code belonging to the paper <br>New robust weighted averaging- and model-based methods for assessing trait-environment relationships. <br>It contains two new methods, a new weighted averaging method and a new model-based method: 1. The new weighted averaging method combines site-level CWM with a species-level regression of Species Niche Centroids (SNC) on to the trait. The regressions are weighted by Hill's effective number (<i>N</i>₂) of occurrences of each species and the <i>N</i>₂-diversity of a site, and are subsequently combined in a sequential test procedure known as the max-test.2. Using the test statistics of these new methods, the permutation-based max test provides strong statistical evidence for trait-environment association in a plant community dataset, where existing methods show (very) weak evidence. The powers of the two new methods were similar in a simulation study based on this dataset.<br>

本代码对应论文《用于评估性状-环境关联关系的新型稳健加权平均法与基于模型的方法》。本代码包含两种新型分析方法，分别为新型加权平均法与基于模型的方法：1. 新型加权平均法将样方水平的群落加权均值（Community Weighted Mean，CWM）与物种水平的物种生态位中心（Species Niche Centroids, SNC）对性状的回归分析相结合。该回归以每个物种的希尔有效出现数（N₂）以及样方的N₂多样性进行加权，随后通过名为最大检验（max-test）的序贯检验流程完成整合。2. 基于这两种新方法的检验统计量，基于置换的最大检验可为该植物群落数据集的性状-环境关联提供强有力的统计学证据；而在该数据集中，现有方法仅能提供（极为）微弱的证据。基于本数据集开展的模拟研究显示，两种新方法的检验效能相近。