fboot_assemblages: Evaluate the robustness of a functional clustering to perturbations of data

Description

Evaluate by bootstrapping the robustness of a functional clustering to perturbations of data. The perturbed data can be the number of assemblages taken into account, or the number of performances taken into account.

Usage

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

Arguments

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.

Value

a list containing a matrix by clustering index.

Details

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.

References

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