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douconca

R library douconca analyzes multi-trait multi-environment ecological data by double constrained correspondence analysis (ter Braak et al. 2018) using vegan and native R code. It has a formula interface for the trait- (column-) and environment- (row-) models, which allows to assess, for example, the importance of trait interactions in shaping ecological communities. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The CWM regressions are specified with an environmental formula and the SNC regressions are specified with a trait formula. dcCA finds the environmental and trait gradients that optimize these regressions. The first step uses cca (Oksanen et al. 2022) to regress the transposed abundance data on to the traits and (weighted) redundancy analysis to regress the community-weighted means (CWMs) of the orthonormalized traits, obtained from the first step, on to the environmental predictors. The sample total of the abundance data are used as weights. The redundancy analysis is carried out using rda if sites have equal weights (after division of the rows by their total) or, in the general weighted case, using wrda. Division by the sample total has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted, which may give a puzzling difference between common univariate and this multivariate analysis.

Reference: ter Braak, CJF, Šmilauer P, and Dray S. 2018. Algorithms and biplots for double constrained correspondence analysis. Environmental and Ecological Statistics, 25(2), 171-197. https://doi.org/10.1007/s10651-017-0395-x

Installation

You can install the development version of douconca like so:

install.packages("remotes")
remotes::install_github("CajoterBraak/douconca")

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Install

install.packages('douconca')

Monthly Downloads

222

Version

1.2.1

License

GPL-3

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Maintainer

Bart-Jan van Rossum

Last Published

September 25th, 2024

Functions in douconca (1.2.1)

anova.wrda

Permutation Test for weighted redundancy analysis
plot_dcCA_CWM_SNC

Plot the CWMs and SNCs of a single dc-CA axis.
print.wrda

Print a summary of a wrda object
reexports

Objects exported from other packages
predict.dcca

Prediction for double-constrained correspondence analysis (dc-CA)
print.dcca

Print a summary of a dc-CA object.
scores.wrda

Extract results of a weighted redundancy analysis (wrda)
scores.dcca

Extract results of a double constrained correspondence analysis (dc-CA)
plot_species_scores_bk

Vertical ggplot2 line plot of ordination scores
wrda

Performs a weighted redundancy analysis
plot.dcca

Plot a single dc-CA axis with CWMs, SNCs, trait and environment scores.
fCWM_SNC

Calculate community weighted means and species niche centroids for double constrained correspondence analysis
dune_trait_env

Dune meadow data with plant species traits and environmental variables
douconca-package

The package douconca performs double constrained correspondence analysis for trait-environment analysis in ecology
anova_sites

Utility function: community-level permutation test in Double Constrained Correspondence Analysis (dc-CA)
anova.dcca

Community- and Species-Level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)
anova_species

Utility function: Species-level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)
getPlotdata

Utility function: extracting data from a dc_CA object for plotting a single axis by your own code or plot.dcca.
dc_CA

Performs (weighted) double constrained correspondence analysis (dc-CA)