An a priori clustering of components is given, therefore coerced by the user. We generate a hierarchical tree coerced by the a priori component clustering. We proceeds in two steps: (i) by division from the coerced tree-level towards the leaves, (i) by grouping from the coerced tree-level towards the trunk.
complete_ftree(fobs, mOccur, xpr, affectElt, opt.mean, opt.model,
opt.nbMax = dim(mOccur)[2] )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 vector of integers
of length(affectElt) == dim(mOccur)[1],
that is the number of components.
The vector contains the labels of different functional clusters
to which each component belongs.
Each functional cluster is labelled as an integer, and
each component must be identified by its name in names(affectElt).
The number of functional clusters defined in affectElt
determines an a priori level of component clustering
(level <- length(unique(affectElt))).
If affectElt = NULL (value by default),
the option opt.method must be filled out.
A tree is built,
from a unique trunk to as many leaves as components
by using the specified method.
If affectElt is specified,
the option opt.method does not need to be filled out.
affectElt determines an a priori
level of component clustering,
and a tree is built:
(i) by using opt.method = "divisive"
from the a priori defined level in tree towards
as many leaves as components;
(ii) by using opt.method = "agglomerative"
from the a priori defined level in tree towards the tree trunk
(all components are together withi a trivial singleton).
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".
an integer, comprizes between 1 and nbElt,
that indicates the last level of hierarchical tree to compute.
This option is very useful to shorten computing-time
in the test-functions
ftest_components, ftest_assemblages,
ftest_performances, fboot_assemblages,
fboot_performances or ftest
where the function fit_ftree is run very numerous times.
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.
"divisive": We proceed by division,
varying the number of functional groups of components
from 1 to the number of components.
All components are initially regrouped
into a single, large, trivial functional group.
At each step, one of the functional groups is split
into two new functional groups: the new functional groups selected are
those that minimize the Residual Sum of Squares of the clustering.
The process stops when each component is isolated in a singleton,
that is when there are so many clsyters as components.
As a whole, the process generates a hierarchical divisive tree
of component clustering, whose RSS decreases monotonically
with the number of functional groups.
At each hierarchical level of the divisive tree, the division of the existing functional groups into new functional groups proceeds as follows. Each existing functional group is successively split into two new functional groups. To do that, each component of the functional group is isolated into a singleton: the singleton-component that minimizes RSS is selected as the nucleus of the new functional group. Each of the other components belonging to the existing functional group is successively moved towards the new functional group: the component clustering that minimizes RSS is kept. Moving component into the new functional group continues as long as the new component clustering decreases RSS.
"agglomerative": 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.
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.