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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.
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.
NMS Scree Plot graph example
A hilltop plot is a way of showing more than one nonlinear response surface at a time on a single graph. The first use that we are aware of was in Nelson et al. (2015). In PC-ORD, response surfaces are superimposed on an ordination as an overlay. This enables simultaneous measurement and display of one- and two-dimensional, non-linear community–trait–environment associations.
Thank you for the wonderful news that v. 7 has now been released. Even though practically every multivariate statistical technique/method could alternatively be done using R language, I still prefer using PC-ORD for teaching ecology students multivariate statistics. PC-ORD is very user-friendly and offers, in one self-contained package, many powerful methods and a great toolbox (“Modify Data”) that ecology students really need and can quickly utilize in their research. An excellent feature is the textbook-like, built-in documentation explaining theoretical background in detail.
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