compute_ftree_stats: Valid hierarchical tree and Statistics of model...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value

Take a matrix of calibrations, a matrix of predictions, the vector of observed performances, the number of observed assembly motifs, and return a matrix of statistics for model goodness-of-fit.

1	compute_ftree_stats(mCal, mPrd, mStats, fobs, xpr, nbK)

`mCal`	a numeric matrix. This matrix is the matrix of performances predicted by the model.
`mPrd`	a numeric matrix. This matrix is the matrix of performances predicted by cross-validation.
`mStats`	a numeric matrix. This matrix is the matrix of statistics of model goodness-of-fit.
`fobs`	a numeric vector. This vector is the vector of observed performances.
`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`.
`nbK`	an integer. This integer corresponds to the number of observed assembly motifs.

Be careful, the matrix order is not ramdon. The first argument mCal is matrix of modelled values. The second argument mPrd is matrix of values predicted by cross-validation. The fourth argument fobs is the vector of observed values.

tCal: a matrix of the valid part of hierarchical tree, that is the part of tree that increases predictive ability of model,
tCal and tPrd: the valid part of hierarchical tree, that is the part of tree that increases predictive ability of model,
tStats: statistics of tree model goodness-of-fit,
tNbcl: the number of clusters used or computing each performance.