cca0 {douconca} | R Documentation |
Performs a canonical correspondence analysis
Description
cca0
is formula-based implementation of canonical correspondence
analysis.
Usage
cca0(
formula,
response = NULL,
data,
traceonly = FALSE,
cca_object = NULL,
object4QR = NULL
)
Arguments
formula |
one or two-sided formula for the rows (samples) with row
predictors in |
response |
matrix or data frame of the abundance data (dimension
n x m). Rownames of |
data |
matrix or data frame of the row predictors, with rows
corresponding to those in |
traceonly |
logical, default |
cca_object |
a vegan-type cca-object of transposed
|
object4QR |
a vegan-type cca-object with weighted QR's for
|
Details
The algorithm is a wrda on the abundance data after transformation to chi-square residuals.
It is much slower than cca
. The only reason to use
it, is that anova.cca0
does residualized predictor permutation.
It is unknown to the authors of douconca
which method
anova.cca
implements. See anova.cca0
.
Compared to cca
, cca0
does not have residual
axes, i.e. no CA of the residuals is performed.
Value
All scores in the cca0
object are in scaling "sites"
(1):
the scaling with Focus on Case distances.
The returned object has class c("cca0" "wrda")
so that
the methods print
, predict
and scores
can use the wrda
variant.
References
ter Braak C.J.F. and P. Ć milauer (2018). Canoco reference manual and user's guide: software for ordination (version 5.1x). Microcomputer Power, Ithaca, USA, 536 pp.
Oksanen, J., et al. (2022) vegan: Community Ecology Package. R package version 2.6-8. https://CRAN.R-project.org/package=vegan.
See Also
scores.wrda
, anova.cca0
,
print.wrda
and predict.wrda
Examples
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 <- cca0(formula = abun ~ A1 + Moist + Mag + Use + Manure,
data = dune_trait_env$envir)
mod # Proportions equal to those Canoco 5.15
scores(mod, which_cor = c("A1", "X_lot"), display = "cor")
set.seed(123)
anova(mod)
anova(mod, by = "axis")
mod2 <- vegan::cca(abun ~ A1 + Moist + Mag + Use + Manure,
data = dune_trait_env$envir)
anova(mod2, by = "axis")
dat <- dune_trait_env$envir
dat$Mag <- "SF"
predict(mod, type = "lc", newdata = dat)