agglomerative_ftree: Hierarchical agglomerative clustering of components
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value

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.

1	agglomerative_ftree(fobs, mOccur, xpr, opt.mean, opt.model)

`fobs`	a numeric vector. The vector `fobs` contains the quantitative performances of assemblages.
`mOccur`	a matrix of occurrence (occurrence of elements). Its first dimension equals to `length(fobs)`. Its second dimension equals to the number of elements.
`xpr`	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`.
`opt.mean`	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`.
`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"`.

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.

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.

functClust documentation built on Dec. 2, 2020, 5:06 p.m.