图形格式
- 反射/旋转排序
- 叠加侧图拟合包络
- 物种-面积曲线中的置信带
- PCA 双标向量（计算和绘制基于距离的物种或其他变量的双标）
- 将图形保存为 emf、wmf、bmp、jpeg、gif 和 tiff
- 单个图形上的 32 种符号类型或颜色
- 将图形分辨率设置为 dpi 或屏幕上的视图百分比以进行保存和复制
- 选择在轴的内部、外部或跨轴放置刻度线
- 排序点的象限敏感标记（点标签的更智能定位）
- 双标图、联合图和连续向量上的可选箭头和箭头大小
- 具有高斯核平滑的侧散点图上的包络
Ordinations
Bray-Curtis (Polar)
We offer numerous options and improvements beyond Bray and Curtis' original method, such as perpendicularized axes and variance-regression endpoint selection.
Canonical Correspondence Analysis (CCA)
CCA is unique among the ordination methods in PC-ORD in that the ordination of the main matrix (by reciprocal averaging) is constrained by a multiple regression on variables included in the second matrix. In community ecology, this means that the ordination of samples and species is constrained by their relationships to environmental variables. CCA is most likely to be useful when: (1) species responses are unimodal (hump-shaped), and (2) the important underlying environmental variables have been measured.
Detrended Correspondence Analysis (DCA, DECORANA)
DCA is an eigenanalysis ordination technique based on reciprocal averaging (RA; Hill 1973). DCA is geared to ecological data sets and the terminology is based on samples and species. DCA ordinates both species and samples simultaneously.
Non-metric Multidimensional Scaling (NMS)
Non-metric Multidimensional Scaling (NMS, MDS, NMDS, or NMMDS) is an ordination method that is well suited to data that are nonnormal or are on arbitrary, discontinuous, or otherwise questionable scales. NMS is generally the best ordination method for community data. Our auto-pilot feature makes it easy to use. A Monte Carlo test of significance is included.
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