ftest_components: Evaluate the weight of each component on functional...

In functClust: Functional Clustering of Redundant Components of a System

Description

Evaluate by cross-validation (leave-one-out) the effect induced by each component on the result of a functional clustering.

Usage

ftest_components(fres, opt.nbMax = fres$nbOpt,
                 opt.R2 = FALSE, opt.plot = FALSE)

Arguments

fres	an object resulting from a functional clustering obtained with the whole dataset using the function fclust.
opt.nbMax	a logical. If opt.plot = TRUE, at each test, the tree resulting from removing each component, assemblage or performance is plotted.
opt.R2	a logical. If opt.R2 = TRUE, the primary tree is validated and the vectors of coefficient of determination (R^2) and efficiency (E) are computed.
opt.plot	a logical. If opt.plot = TRUE, at each test, the tree resulting from removing each component, assemblage or performance is plotted.

Details

a

Each component of the interactive system in consideration is successively removed from the dataset, the remaining components are functionally clustered, then indices of distance between clustering trees with and without the component are computed. The components can then be hierarchised depending on the distance induced by their removing from dataset. The used distance criteria are : "Czekanowski_Dice", "Folkes_Mallows", "Jaccard", "Kulczynski", "Precision", "Rand", "Recall", "Rogers_Tanimoto", "Russel_Rao", "Sokal_Sneath1" and "Sokal_Sneath2" index. For more informations, see the notice of R-package clusterCrit. The test is time-consuming.

Value

a list containing a matrix by clustering index.

References

Package "clusterCrit": Clustering Indices, by Bernard Desgraupes (University of Paris Ouest - Lab Modal'X)

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