Introduction
Theory, Examples & Exercises
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capscale
(library vegan
) - without environmental variables, the function calculates PCoA, while with environmental variables it calculates distance-based RDA. Input could be either species composition matrix (samples x species) or distance matrix (in that case, the species scores will not be available, unless the original species composition matrix is provided as argument comm
). By default distance = “euclidean”
, which returns results identical to PCA. Note that even if no environmental variables are included, the formula structure is still required (e.g. capscale (spe ~ 1, distance = 'bray')
).cmdscale
(basic library stats
) - calculates PCoA on matrix of distances among samples (this could be calculated e.g. by function vegdist
from library vegan
). Use function ordiplot
to project the ordination diagram.wcmdscale
(library vegan
) - based on cmdscale
function, but allows to weight the importance of samples in the PCoA. If arguments eig = TRUE
or x.ret = TRUE
, the function returns an object of class “wcmdscale” with print, plot, scores, eigenvals and stressplot methods.pcoa
(library ape
) - another way how to achieve PCoA analysis. Use biplot.pcoa
function (or simply generic biplot
) to project ordination diagram. Does not work with vegan
's functions ordiplot
or scores
.metaMDS
(library vegan
) - rather advanced function, composed of many subroutine steps. See example below for details.stressplot
(library vegan
) - draws Shepards stress plot, which is the relationship between real distances between samples in resulting m dimensional ordination solution, and their particular compositional dissimilarities expressed by selected dissimilarity measure.goodness
(library vegan
) - returns goodness-of-fit of particular samples. See example how can be this result visualized (inspired by Borcard et al. 2011).