使用期限*
许可形式单机版
原产地美国
介质下载
适用平台Windows
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New Traits Window and Analyses
Provides ways to relate data on species traits (trait matrix) to community samples (main matrix) and environmental data (second matrix).
![如何使用PC-ORD](//l.b2b168.com/2019/07/18/10/201907181030450295824.jpg)
Principal Coordinates Analysis (PCoA)
Principal Coordinates Analysis is an eigenanalysis technique similar to PCA, except that one extracts eigenvectors from a distance matrix among sample units (rows), rather than from a correlation or covariance matrix. In PCoA one can use any square symmetrical distance matrix, including semi-metrics such as Sorensen distance, as well as metric distance measures such as Euclidean distance.
![如何使用PC-ORD](//l.b2b168.com/2019/07/19/15/201907191520215412424.jpg)
Two-way Cluster Analysis
The purpose of our two-way clustering (also known as biclustering) is to graphically expose the relationship between cluster analyses and your individual data points. The resulting graph makes it easy to see similarities and differences between rows in the same group, rows in different groups, columns in the same group, and columns in different groups. You can see graphically how groups of rows and columns relate to each other. Two-way clustering refers to doing a cluster analysis on both the rows and columns of your matrix, followed by graphing the two dendrograms simultaneously, adjacent to a representation of your main matrix. Rows and columns of your main matrix are re-ordered to
![如何使用PC-ORD](//l.b2b168.com/2019/07/18/10/201907181030476922774.jpg)
* Supported Technical support provided by Wild Blueberry Media
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