Principal components provide an effective method for reducing the number of variables in large-scale correlation studies. With fewer variables, patterns that may exist in the data are more readily ...

Compositional data, consisting of vectors of proportions, have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. Moreover ...