ftest_plot: Plot the significance of different variables of a functional...

In functClust: Functional Clustering of Redundant Components of a System

Description

Different plots are built according to the tested variable.

Usage

ftest_plot(fres, rtest,
           main     = "Title",
           opt.var  = c("components", "assemblages", "performances"),
           opt.crit = "Jaccard",
           opt.comp = NULL, opt.ass = NULL, opt.perf = NULL)

Arguments

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.var	a string, that indicates the variable to test. The option can be "components", "assemblages" or "performances".
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").
opt.ass	a list, that can include opt.ass = list("all.together", "motifs.together", "assemblages.together", "motifs.bymot", "assemblages.bymot", "sorted.leg", "all"). This option list manages the plot of results obtained using the function ftest with opt.var = "assemblages". The item order in list is any. "all.together", "motifs.together", "assemblages.together" plot (i) the general mean index; (ii) the mean indices for each assembly motif on a same plot; and (iii) the mean index for each assemblages on a same plot, when removing one after one each assemblage from the dataset. This allows to evaluate the raw robustness of functional clustering to perturbation of dataset, and the weight of each assemblage on functional clustering. "motifs.bymot", "assemblages.bymot" plot (i) mean assembly motifs, assembly motif by assembly motif; (ii) a graph by removed assemblage, assembly motif by assembly motif; This allows to evaluate the weight of each assemblage on functional clustering. "sorted.leg" plot the names of assemblages decreasingly sorted according to their weight on functional clustering. "all" plot all possible graphs. This option is equivalent to opt.ass = list("all.together", "motifs.together", "assemblages.together", "motifs.bymot", "assemblages.bymot", "sorted.leg").
opt.perf	a list, that can include a list, that can include opt.comp = list("all.together", "performances.together", "sorted.leg"). This option list manages the plot of results obtained using the function ftest with opt.var = "performances". The item order in list is any. "all.together", "performances.together" plot (i) the general mean index; (ii) the mean indices for each removed performance on a same plot, when removing one after one each performance from the dataset. This allows to evaluate the raw robustness of functional clustering to perturbation of dataset, and the weight of each performance on functional clustering. "sorted.leg" plot the names of performances decreasingly sorted according to their weight on functional clustering. "all" plot all possible graphs. This option is equivalent to opt.comp = list("all.together", "performances.together", "sorted.leg").

Details

The trees obtained by leaving out each element are compared to the reference tree obtained with all element of the variables using different criteria of clustering: "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.

Value

Nothing. It is a procedure.

References

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

Examples

# Plot the hierachical tree of components
layout(matrix(c(1,2,3,4), nrow = 2, ncol = 2, byrow = TRUE))
fclust_plot(fres = CedarCreek.2004.2006.res, main  = "BioDIV2",
            opt.tree = "prd")

# Plot the significance of each component within each components cluster
ftest_plot(fres  = CedarCreek.2004.2006.res,
           rtest = CedarCreek.2004.2006.test.components,
           main  = "BioDIV2",
           opt.var = c("components"), opt.crit = "Jaccard")
layout(1)

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