print.dcca {douconca} | R Documentation |
Print a summary of a dc-CA object.
Description
Print a summary of a dc-CA object.
Usage
## S3 method for class 'dcca'
print(x, ...)
Arguments
x |
a dc-CA object from |
... |
Other arguments passed to the function (currently ignored). |
Details
x <- print(x)
is more efficient for scores.dcca
than
just print(x)
if dc_CA
is called with
verbose = FALSE
).
Value
No return value, results are printed to console.
Examples
data("dune_trait_env")
# rownames are carried forward in results
rownames(dune_trait_env$comm) <- dune_trait_env$comm$Sites
mod <- dc_CA(formulaEnv = ~A1 + Moist + Mag + Use + Manure,
formulaTraits = ~ SLA + Height + LDMC + Seedmass + Lifespan,
response = dune_trait_env$comm[, -1], # must delete "Sites"
dataEnv = dune_trait_env$envir,
dataTraits = dune_trait_env$traits)
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 = ~ A1 + Moist + Mag + Use + Manure,
formulaTraits = ~ SLA + Height + LDMC + Lifespan + Condition(Seedmass),
response = dune_trait_env$comm[, -1], # must delete "Sites"
dataEnv = dune_trait_env$envir,
dataTraits = dune_trait_env$traits)
cat("\nFor illustration: a dc-CA model with both environmental and trait covariates\n")
mod3 <- dc_CA(formulaEnv = ~A1 + Moist + Use + Manure + Condition(Mag),
formulaTraits = ~ SLA + Height + LDMC + Lifespan + Condition(Seedmass),
response = dune_trait_env$comm[, -1], # must delete "Sites"
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 = ~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, mod3B)) # only the component call differs
[Package douconca version 1.2.0 Index]