fboot_performances: Evaluate the robustness of a functional clustering to...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value References

Evaluate by bootstrapping the robustness of a functional clustering to perturbations of data.

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fboot_performances(fres,
                   opt.nbMax = fres$nbOpt, opt.R2 = FALSE, opt.plot = FALSE,
                   nbIter = 1, seed = NULL, rm.number = 0)

`fres`	an object resulting from a functional clustering obtained using the function `fclust`.
`opt.nbMax`	a logical. If `opt.plot = TRUE`, the trees resulting from leaving out each 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`, the primary trees resulting from leaving out each performance are plotted. If `opt.R2 = TRUE`, the secondary trees resulting from leaving out each performance are plotted.
`nbIter`	an integer, that indicates the number of random drawing to do.
`seed`	an integer, that fixes a seed for random drawing.
`rm.number`	an integer, that indicates the number of elements to randomly remove.

The trees obtained by bootstrapping of performances to omit are compared to the reference tree obtained with all components using different criteria : "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.

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.