Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology
Utility function: community-level permutation test in Double Constrain...
Utility function: Species-level Permutation Test in Double Constrained...
Community- and Species-Level Permutation Test in Double Constrained Co...
Permutation Test for weighted redundancy analysis
Performs (weighted) double constrained correspondence analysis (dc-CA)
The package douconca performs double constrained correspondence analys...
Calculate community weighted means and species niche centroids for dou...
Utility function: extracting data from a dc_CA object for plotting a...
Plot the CWMs and SNCs of a single dc-CA axis.
Vertical ggplot2 line plot of ordination scores
Plot a single dc-CA axis with CWMs, SNCs, trait and environment scores...
Prediction for double-constrained correspondence analysis (dc-CA)
Print a summary of a dc-CA object.
Print a summary of a wrda object
Objects exported from other packages
Extract results of a double constrained correspondence analysis (dc-CA...
Extract results of a weighted redundancy analysis (wrda)
Performs a weighted redundancy analysis
Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the 'vegan' package and native R code. 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 two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>.