### Example 1: Unconstrained ordination
## NMDS
data(varespec)
data(varechem)
ord <- metaMDS(varespec)
plot(ord, type = "t")
## Fit environmental variables
ef <- envfit(ord, varechem)
ef
plot(ef, p.max = 0.05)
### Example 2: Constrained ordination (RDA)
## The example uses formula interface to define the model
data(dune)
data(dune.env)
## No constraints: PCA
mod0 <- rda(dune ~ 1, dune.env)
mod0
plot(mod0)
## All environmental variables: Full model
mod1 <- rda(dune ~ ., dune.env)
mod1
plot(mod1)
## Automatic selection of variables by permutation P-values
mod <- ordistep(mod0, scope=formula(mod1))
mod
plot(mod)
## Permutation test for all variables
anova(mod)
## Permutation test of "type III" effects, or significance when a term
## is added to the model after all other terms
anova(mod, by = "margin")
## Plot only sample plots, use different symbols and draw SD ellipses
## for Managemenet classes
plot(mod, display = "sites", type = "n")
with(dune.env, points(mod, disp = "si", pch = as.numeric(Management)))
with(dune.env, legend("topleft", levels(Management), pch = 1:4,
title = "Management"))
with(dune.env, ordiellipse(mod, Management, label = TRUE))
## add fitted surface of diversity to the model
ordisurf(mod, diversity(dune), add = TRUE)
### Example 3: analysis of dissimilarites a.k.a. non-parametric
### permutational anova
adonis2(dune ~ ., dune.env)
adonis2(dune ~ Management + Moisture, dune.env)
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