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Ordination analysis

Monte Carlo permutation test (constrained ordination)

R functions

  • anova.cca (library vegan) - tests the significance of the variation in species composition explained by explanatory variables in constrained ordination (RDA, CCA), using Monte Carlo permutation test. It can test the significance of
    • the global model (default setting), i.e. all variables included in the analysis;
    • only the first constrained axis (adding argument first = TRUE);
    • individual axes (by = “axis”), sequentially from the first to the last (this is done by using samples scores on the n-th axis as explanatory variables, while using scores of the axis 1, 2, ... n as covariables);
    • individual terms (explanatory variables) added sequentially in the order in which they appear in the formula or data frame (by = “terms”; note that this variation depends on the order of variables in which they enter the model);
    • variation explained by individual explanatory variables after removing variation of all other variables in the model (by = “margin”; here the variation does not depend on the order of variables in the model).
en/monte_carlo_r.1548504057.txt.gz · Last modified: 2019/01/26 20:00 by David Zelený