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Principal Components Analysis (PCA)
Principal Components Analysis is the basic eigenanalysis technique. It maximizes the variance explained by each successive axis. Although it has severe faults with many community data sets, it is probably the best technique to use when a data set approximates multivariate normality. PCA is usually a poor method for community data, but it is the best method for many other kinds of multivariate data. Broken-stick eigenvalues are provided to help you evaluate statistical significance.
Decision Tree Poster Helps you understand the Advisor Wizard logic
图形功能扩展
应用按钮在不退出偏好对话框的情况下立即预览更改
保存群中心的分数为电子表格或文本文件
将图片保存为TIFF格式
Species Frequency Label Cutoff
tick marks数量控制
Reorder legend
定制工具栏
SumF
A simple but surprisingly effective method of comparing two or more groups of sample units is to calculate a univariate F statistic for each variable, sum those F statistics, then compare the resulting sum to the distribution of F statistics based on randomizing the data under the null hypothesis. This is the core of the SumF method, as suggested by Edginton (1995). Good performance of this method, as compared to distance-based methods, was found by Warton and Hudson (2004). An advantage to this method is that by aggregating a simple, well-known test statistic, the F ratio, into a summary statistic across multiple variables, we simultaneously obtain information about differences between groups both across all variables and for individual variables. Thus for the generic question, "Do communities differ between groups?", the SumF method allows us to report an answer for communities as a whole as well as for individual species.
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