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适用平台Windows
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Morisita-Horn指数对小物种的影响较小,这在很大程度上是因为该方法抗欠采样的原因。同时,它使该方法对小物种携带的模式不敏感。
在perMANOVA和指示物种分析中增加块或组的大数目到1,000
新Summary | Write距离矩阵选项
物种面积曲线的自举置信区间
PCA选项将预测方程写入文本文件
PCA选项将特征值从随机化写入电子表格
使用Peres-Neto et al.(2006)方法在CCA中增加R²perm调整方差
使用Peres-Neto et al.(2006)方法在RDA中增加Ezekiel和R²perm调整方差
添加了拟合方法,包括基于随机的方法到NMS中
Dust bunny指数从多元正态到dust bunny分布作为出发度量
新Summary | Write距离矩阵选项:
√ 只写次对角线距离(对于连续样本)
√ 写对子对角线距离(配对样本)
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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.
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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
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Categorical Counts
Categorical Counts提供一种用给定范畴值跟踪案例数量的方法(行,通常指示例单元)。默认情况下,对选定矩阵中的所有分类变量都执行此操作。提供了快速评估类别的频率,对于实验设计中的平衡或不同类别的采样有效性等问题是有用的。
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