ftest_performances: Evaluate the weight of each performance on functional...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value References

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

1 2	ftest_performances(fres, opt.nbMax = fres$nbOpt, opt.R2 = FALSE, opt.plot = FALSE)

`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.

Each performance of the dataset is successively removed, the remaining performance collection is functionally analysed, then indices of distance between clustering trees with and without the performance are computed. The performances 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.

a list containing a matrix by clustering index.

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.