ftest_plot_components: Plot test of the weight of each component 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 component on the result of a functional clustering.

1 2	ftest_plot_components(fres, rtest, main = "Title", opt.crit = "Jaccard", opt.comp = list("sorted.tree"))

`fres`	an object resulting from a functional clustering obtained with the whole dataset using the function `fclust`.
`rtest`	a list of matrices, each containing the results for a clustering index. `rtest` is an object generated by the function `ftest`.
`main`	a string, that is used as the first, reference part of the title of each graph.
`opt.crit`	a list of strings, indicating the clustering indices to plot. The indices can be: "Czekanowski_Dice", "Folkes_Mallows", "Jaccard", "Kulczynski", "Precision", "Rand", "Recall", "Rogers_Tanimoto", "Russel_Rao", "Sokal_Sneath1" or "Sokal_Sneath2". For more informations, see the notice of R-package `clusterCrit`.
`opt.comp`	a list, that can include `opt.comp` = `list("all.together", "fgroups.together", "comps.together", "fgroups.byfg", "comps.byfg", "sorted.tree", "sorted.leg", "all")`. This option list manages the plot of results obtained using the function `ftest` with `opt.var = "components"`. The item order in list is any. `"all.together", "fgroups.together", "comps.together"` plot (i) the general mean index; (ii) the mean indices for each functional group on a same plot; and (iii) the mean index for each components on a same plot, when removing one after one each component from the dataset. This allows to evaluate the raw robustness of functional clustering to perturbation of dataset, and the weight of each cluster on functional clustering. `"fgroups.byfg", "comps.byfg"` plot (i) mean component clusters, functional group by functional group; (ii) a graph by component, functional group by functional group; This allows to evaluate the weight of each component on functional clustering. `"sorted.tree", "sorted.leg"` plot (i) the hierarchical tree of components, with components decreasingly sorted according to their weight on functional clustering within each functional group; (ii) the names of component decreasingly sorted according to their weight on functional clustering within each functional group. `"all"` plot all possible graphs. This option is equivalent to `opt.comp` = `list("all.together", "fgroups.together", "comps.together", "fgroups.byfg", "comps.byfg", "sorted.tree", "sorted.leg")`.