The function checks the inputs,
then switch on different methods of component clustering
according to the option opt.method.
Three methods (opt.method = c("sort", "divisive", "agglomerative"))
generate a hierarchical tree.
The last method (opt.method = "cluster")
generates a non-hierarchical tree of clustering.
fit_ftree(fobs, mOccur,
xpr = stats::setNames(rep(1, length(fobs)),rep("a", length(fobs))),
affectElt = rep(1, dim(mOccur)[2]),
opt.method = "divisive",
opt.mean = "amean",
opt.model = "byelt",
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 string that specifies the method to use.
opt.method = c("divisive", "agglomerative", "complete").
All the methods generate a hierarchical tree.
Each tree is complete, running from a unique trunk
to as many leaves as components.
If opt.method = "divisive", the components are clustered
according to a hierarchical process
by using a divisive method,
from the trivial cluster where all components are together,
towards the clustering where each component is a cluster.
If opt.method = "agglomerative", the components are clustered
according to a hierarchical process
by using an agglomerative method,
from the trivial clustering where each component is a clsuter,
towards the cluster where all components are together.
The method that gives the best result is opt.method = "divisive".
If opt.method = "complete", affectElt must be specified.
affectElt determines a level of component clustering,
and a tree is built:
(i) by using opt.method = "divisive"
from the defined level in tree towards as many leaves as components;
(ii) by using opt.method = "agglomerative"
from the defined level in tree towards the trunk of tree.
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 a primary tree of clustering of components for their effect on the performances of component assemblages.
None.