We proceed by grouping, varying the number of functional groups of components from the number of components until to 1. All components are initially dispersed into a singleton, as many singletons as components. At each step, one of the functional groups is grouped with another functional group: the new functional groups selected are those that minimize the Residual Sum of Squares of the clustering. The process stops when all components are grouped into a large, unique functional group. As a whole, the process generates a hierarchical aggloimerative tree of component clustering, whose RSS decreases monotonically with the number of functional groups.
agglomerative_ftree(fobs, mOccur, xpr, opt.mean, opt.model)a numeric vector. The vector fobs contains the
quantitative performances of assemblages.
a matrix of occurrence (occurrence of elements).
Its first dimension equals to length(fobs). Its second dimension
equals to the number of elements.
a vector of numerics of length(fobs).
The vector xpr contains the weight of each experiment,
and the labels (in names(xpr)) of different experiments.
The weigth of each experiment is used
in the computation of the Residual Sum of Squares
in the function rss_clustering.
The used formula is rss
if each experiment has the same weight.
The used formula is wrss
(barycenter of RSS for each experiment)
if each experiment has different weights.
All assemblages that belong to a given experiment
should then have a same weigth.
Each experiment is identified by its names (names(xpr))
and the RSS of each experiment is weighted by values of xpr.
The vector xpr is generated
by the function stats::setNames.
a character equals to "amean" or "gmean".
Switchs to arithmetic formula if opt.mean = "amean".
Switchs to geometric formula if opt.mean = "gmean".
Modelled performances are computed
using arithmetic mean (opt.mean = "amean")
or geometric mean (opt.mean = "gmean")
according to opt.model.
a character equals to "bymot" or "byelt".
Switchs to simple mean by assembly motif if opt.model = "bymot".
Switchs to linear model with assembly motif if opt.model = "byelt".
If opt.model = "bymot",
modelled performances are means
of performances of assemblages
that share a same assembly motif
by including all assemblages that belong to a same assembly motif.
If opt.model = "byelt",
modelled performances are the average
of mean performances of assemblages
that share a same assembly motif
and that contain the same components
as the assemblage to predict.
This procedure corresponds to a linear model within each assembly motif
based on the component occurrence in each assemblage.
If no assemblage contains component belonging to assemblage to predict,
performance is the mean performance of all assemblages
as in opt.model = "bymot".
Return an object "tree",
that is a list containing
(i) tree$aff: an integer square-matrix of
component affectation to functional groups,
(ii) tree$cor: a numeric vector of
coefficient of determination.
At each hierarchical level of the agglomerative tree, the clustering of the existing functional groups into new functional groups proceeds as follows. Each existing functional group is successively grouped with other functional groups. The component clustering that minimizes RSS is kept.