anova.dcca {douconca} | R Documentation |
Community- and Species-Level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)
Description
anova.dcca
performs the community- and species-level permutation tests
of dc-CA and combines these with the 'max test', which takes the maximum of
the P-values. The function arguments are similar to (but more
restrictive than) those of anova.cca
.
Usage
## S3 method for class 'dcca'
anova(object, ..., permutations = 999, by = c("omnibus", "axis"))
Arguments
object |
an object from |
... |
unused. |
permutations |
a list of control values for the permutations for
species and sites (species first, sites second, for traits and environment)
as returned by the function |
by |
character The interpretation of this inertia is, at the species-level, the
environmentally constrained inertia explained by the traits (without trait
covariates) and, at the community-level, the trait-constrained inertia
explained by the environmental predictors (without covariates). The
default ( |
Details
In the general case of varying site abundance totals
(divideBySiteTotals = FALSE
) both the community-level test and the
species-level test use residualized predictor permutation (ter Braak 2022),
so as to ensure that anova.dcca
is robust against differences
in species and site total abundance in the response
(ter Braak & te
Beest, 2022). The community-level test uses anova_sites
. For
the species-level test, anova_species
is used.
With equal site weights, obtained with divide.by.site.total = TRUE
,
the community-level test is obtained by applying anova
to
object$RDAonEnv
using anova.cca
.
This performs residualized response permutation which performs about equal
to residualized predictor permutation in the equi-weight case.
The function anova.cca
is implemented in C and therefore
a factor of 20 or so quicker than the native R-code used in
anova_sites
.
Value
A list of 3 of structures as from anova.cca
. The
elements are c("species", "sites", "max")
References
ter Braak, C.J.F. & te Beest, D.E. 2022. Testing environmental effects on taxonomic composition with canonical correspondence analysis: alternative permutation tests are not equal. Environmental and Ecological Statistics. 29 (4), 849-868. doi:10.1007/s10651-022-00545-4
ter Braak, C.J.F. (2022) Predictor versus response permutation for significance testing in weighted regression and redundancy analysis. Journal of statistical computation and simulation, 92, 2041-2059. doi:10.1080/00949655.2021.2019256
Examples
data("dune_trait_env")
# rownames are carried forward in results
rownames(dune_trait_env$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$traits,
verbose = TRUE)
anova(mod)
a_species <- anova_species(mod)
a_species
# anova_species can be used for manual forward selection of
# trait variables, as done for environmental variables in the demo
# dune_FS_dcCA.r, based on the first eigenvalue and its significance
# and adding the first axis of the constrained species scores from mod to
# the Condition of a new mod.
(eig1_and_pval <- c(eig = a_species$eig[1], pval = a_species$table$`Pr(>F)`[1]))
a_species$eig
anova_sites(mod)