demo/dune_dcCA.R

In douconca: Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology

data("dune_trait_env")

# rownames are carried forward in results
rownames(dune_trait_env$comm) <- dune_trait_env$comm$Sites
abun <- dune_trait_env$comm[, -1]  # must delete "Sites"
mod <- dc_CA(formulaEnv = abun ~ A1 + Moist + Mag + Use + Manure,
             formulaTraits = ~ SLA + Height + LDMC + Seedmass + Lifespan,
             dataEnv = dune_trait_env$envir,
             dataTraits = dune_trait_env$traits,
			 verbose = FALSE)

print(mod) # same output as with verbose = TRUE (the default of verbose).																		 
anova(mod, by = "axis")
# For more demo on testing, see demo dune_test.r

mod_scores <- scores(mod)
# correlation of axes with a variable that is not in the model
scores(mod, display = "cor", scaling = "sym", which_cor = list(NULL, "X_lot"))

cat("head of unconstrained site scores, with meaning\n")
print(head(mod_scores$sites))

mod_scores_tidy <- scores(mod, tidy = TRUE)
print("names of the tidy scores")
print(names(mod_scores_tidy))
cat("\nThe levels of the tidy scores\n")
print(levels(mod_scores_tidy$score))

cat("\nFor illustration: a dc-CA model with a trait covariate\n")
mod2 <- dc_CA(formulaEnv = abun ~ A1 + Moist + Mag + Use + Manure,
              formulaTraits = ~ SLA + Height + LDMC + Lifespan + Condition(Seedmass),
              dataEnv = dune_trait_env$envir,
              dataTraits = dune_trait_env$traits)

cat("\nFor illustration: a dc-CA model with both environmental and trait covariates\n")
mod3 <- dc_CA(formulaEnv = abun ~ A1 + Moist + Use + Manure + Condition(Mag),
              formulaTraits = ~ SLA + Height + LDMC + Lifespan + Condition(Seedmass),
              dataEnv = dune_trait_env$envir,
              dataTraits = dune_trait_env$traits, 
			  verbose = FALSE)

cat("\nFor illustration: same model but using dc_CA_object = mod2 for speed, ", 
    "as the trait model and data did not change\n")
mod3B <- dc_CA(formulaEnv = abun ~ A1 + Moist + Use + Manure + Condition(Mag),
               dataEnv = dune_trait_env$envir,
               dc_CA_object = mod2, 
			   verbose= FALSE)
cat("\ncheck on equality of mod3 (from data) and mod3B (from a dc_CA_object)\n",
    "the expected difference is in the component 'call'\n ")

print(all.equal(mod3[-c(5,12)], mod3B[-c(5,12)])) #  only the component call differs
print(mod3$inertia[-c(3,5),]/mod3B$inertia) #        and mod3 has two more inertia items