rss_clustering: Residual Sum of Squares of a given clustering model
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value

Take performance, occurrence matrix and a clustering model, then compute the corresponding Residual Sum of Squares.

1	rss_clustering(fobs, affectElt, mOccur, xpr, opt.mean, opt.model )

`fobs`	a numeric vector. The vector `fobs` contains the quantitative performances of assemblages.
`affectElt`	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).
`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"`.