Package: douconca 1.2.2

Bart-Jan van Rossum

douconca: Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using library vegan 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 douconca::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>.

Authors:Cajo J.F ter Braak [aut], Bart-Jan van Rossum [aut, cre]

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NEWS

# Install 'douconca' in R:
install.packages('douconca', repos = c('https://cajoterbraak.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cajoterbraak/douconca/issues

Datasets:
  • dune_trait_env - Dune meadow data with plant species traits and environmental variables

On CRAN:

3.00 score 1 stars 3 scripts 172 downloads 9 exports 34 dependencies

Last updated 5 days agofrom:7e1c23e83d. Checks:OK: 1 NOTE: 6. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winNOTENov 01 2024
R-4.5-linuxNOTENov 01 2024
R-4.4-winNOTENov 01 2024
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R-4.3-macNOTENov 01 2024

Exports:anova_sitesanova_speciesdc_CAfCWM_SNCgetPlotdataplot_dcCA_CWM_SNCplot_species_scores_bkscoreswrda

Dependencies:cliclustercolorspacefansifarverggplot2ggrepelgluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepermutepillarpkgconfigR6RColorBrewerRcpprlangscalestibbleutf8vctrsveganviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Utility function: community-level permutation test in Double Constrained Correspondence Analysis (dc-CA)anova_sites
Utility function: Species-level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)anova_species
Community- and Species-Level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)anova.dcca
Permutation Test for weighted redundancy analysisanova.wrda
Coefficients of double-constrained correspondence analysis (dc-CA)coef.dcca
Performs (weighted) double constrained correspondence analysis (dc-CA)dc_CA
Dune meadow data with plant species traits and environmental variablesdune_trait_env
Calculate community weighted means and species niche centroids for double constrained correspondence analysisfCWM_SNC
Fitted values of double-constrained correspondence analysis (dc-CA)fitted.dcca
Utility function: extracting data from a 'dc_CA' object for plotting a single axis by your own code or 'plot.dcca'.getPlotdata
Plot the CWMs and SNCs of a single dc-CA axis.plot_dcCA_CWM_SNC
Vertical ggplot2 line plot of ordination scoresplot_species_scores_bk
Plot a single dc-CA axis with CWMs, SNCs, trait and environment scores.plot.dcca
Prediction for double-constrained correspondence analysis (dc-CA)predict.dcca
Print a summary of a dc-CA object.print.dcca
Print a summary of a wrda objectprint.wrda
Extract results of a double constrained correspondence analysis (dc-CA)scores.dcca
Extract results of a weighted redundancy analysis (wrda)scores.wrda
Performs a weighted redundancy analysiswrda