compute_motif_stats

a numeric matrix.
This matrix is the matrix of performances predicted by the valid tree model.

tCal

a numeric matrix.
This matrix is the matrix of performances predicted by cross-validation.

tPrd

a numeric matrix.
This matrix is the matrix of assembly motifs.

mMotifs

Take a matrix of tree calibrations,
 a matrix of tree predictions,
 a matrix of assembly motifs,
 and return statistics of each observed assembly motif.

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>.

compute_motif_stats: Statistics of assembly motifs

Description

Usage

Arguments

Value

Details