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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.
NMS Scores
NMS Scores provides a prediction algorithm for non-metric multidimensional scaling (NMS). This is not prediction in the sense of forecasting, but rather statistical prediction in the same way as using multiple regression to estimate a dependent variable. NMS Scores calculates scores for new items based on prior ordinations.
Multi-Response Permutation Procedures (MRPP)
MRPP is a non-parametric procedure for testing the hypothesis of no difference between two or more groups of entities. The groups must be a priori. For example, one could compare species composition between burned and unburned plots to test the hypothesis of no treatment effect. Discriminant analysis is a parametric procedure that can be used on the same general class of questions. However, MRPP has the advantage of not requiring assumptions (such as multivariate normality and homogeneity of variances) that are seldom met with ecological community data. Eight distance measures options are available.
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.
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