predict_amean_byelt

a vector of numeric values (assembly properties).

appFct

a vector of labels of <code>length(fct)</code> (assembly motifs).

appMotifs

a matrix of occurrence (occurrence of components).
Its first dimension equals to <code>length(fct)</code>. Its second dimension
 equals to the number of components.

appOccur

supMotifs

supOccur

internal

Cluster together the components that make up an interactive
system on the basis of their functional redundancy
for one or more collective, systemic performances.
Plot the hierarchical tree of component clusters,
the modelled and predicted performances of component assemblages,
and other results associated with a functional clustering.
Test and prioritize the significance of the different components
that make up the interactive system,
of the different assemblages of components that make up the dataset,
and of the different performances observed on the component assemblages.
The method finds application in ecology, for instance,
where the system is an ecosystem, the components are organisms or species,
and the systemic performance is the production of biomass
or the respiration of the ecosystem.
The method is extensively described in
Jaillard B, Deleporte P, Loreau M, Violle C (2018)
"A combinatorial analysis using observational data identifies species
that govern ecosystem functioning"
<doi:10.1371/journal.pone.0201135>.

Beno<c3><ae>t Jaillard

functClust

Functional Clustering of Redundant Components of a System

Philippe Deleporte

Michel Loreau

Cyrille Violle

predict_amean_byelt function

Prediction of supplementary assemblages computed — predict_amean_byelt

Prediction of supplementary assemblages computed

predict_amean_byelt: Prediction of supplementary assemblages computed

Description

Usage

Arguments

Value

Details