The options of fclust_plot

Benoît Jaillard

2020-11-30

The function fclust_plot can conveniently plot the primary and secondary trees of component clustering, observed, modelled and predicted performances of assemblages, composition and mean performances of assembly motifs, mean performances of assemblages containing a given components, or performances of some given assemblages.

The option opt.tree

opt.tree manages the plot of primary and secondary trees of component clustering. The option appears as a list of sub-options : opt.tree = list(“cal”, “prd”, “leg”, cols, “zoom”, window, “all”).

apriori <- c("F","C3","L","C4","F","C3","C3","L","F","L","F","C4","L","C3","C4","C4")
res <- fclust(dat.2004, nbElt, opt.mod = "byelt", opt.mean = "gmean")

fclust_plot(res, main = "BioDiv2", opt.tree = list("prd"))
fclust_plot(res, main = "BioDiv2", opt.tree = list("prd", cols = apriori))
Opt.tree manages the plot of primary and secondary treesOpt.tree manages the plot of primary and secondary trees

Opt.tree manages the plot of primary and secondary trees

Both the graphs show the validated, secondary tree pruned above the optimum number of component clusters. However, on left graph components belonging to a cluster are of same colour, on right graph components belonging to clusters a priori defined by the user are of same colour. Here, the meadow species are a priori clustered in Legumes (in red), Forbs (in cyan), C3-grasses (in blue) and C4 grasses (in gold). The x-axis of tree is the system components. The y-axis of tree is the quality criterion of modelling, i.e. the coefficient of determination R2. Modelling efficiency (E-value) is indicated by a red dashed line. Values of R2, E and E/R2 are indicated.

The option opt.perf

opt.perf manages the plot of observed, modelled and predicted performances of assemblages, and associated statistics. The option appears as a list of sub-options : opt.perf = list(“stats_I”, “stats_II”, “cal”, “prd”, “pub”, “missing”, “calprd”, “seq”, “ass”, “aov”, pvalue, “all”).

fclust_plot(res, main = "BioDiv2", opt.perf = list("stats_II"))
Opt.perf=list('stats_xx') manages the plot of statisticsOpt.perf=list('stats_xx') manages the plot of statisticsOpt.perf=list('stats_xx') manages the plot of statisticsOpt.perf=list('stats_xx') manages the plot of statistics

Opt.perf=list(‘stats_xx’) manages the plot of statistics

The first left graph shows coefficient of determination R2 (black circles) and efficiency E (red circles) of validated secondary. The right graph shows the proportion of assemblages of which the performance cannot be predicted by cross-validation only using the clustering model (blue squares). Here all performances are predicted by using the whole secondary tree. Both the lowest graphs show AIC (blue circles) and AICc (red circles) of models. Green dashed line indicates the optimum number of component clusters.

fclust_plot(res, main = "BioDiv2", nbcl = 3, opt.perf = list("prd", "aov", pvalue = 0.01, "pub"))
Opt.perf=list('prd') on left, =list('pub') on rightOpt.perf=list('prd') on left, =list('pub') on right

Opt.perf=list(‘prd’) on left, =list(‘pub’) on right

Both graphs show the model goodness-of-fit, left and right graphs differing only by the used colours and symbols. On left graph, the performances of assemblages belonging to different assembly motifs are plotted with different colours and symbols. The symbols correspond to performances predicted by the model (modelled performances), of which the goodness-of-fit is evaluated by tree coefficient of determination R2. The vertical lines correspond to performances independently predicted by cross-validation (independently predicted performances), of which the quality is evaluated by tree efficiency E. Global statistic are indicated (number of component clusters, number of assembly motifs, R2, E and the ratio E/R2). A variance analysis is indicated on detailled graph, on the left side for modelled performances, on the right side for performances predicted by cross-validation, using a pvalue = 0.01. On both graphs, the solid red line is line 1:1, and the blue dashed lines are mean observed and modelled performances.

fclust_plot(res, main = "BioDiv2", nbcl = 4, opt.perf = list("missing"))
fclust_plot(res, main = "BioDiv2", nbcl = 4, opt.perf = list("calprd", "aov", pvalue = 0.01))
Opt.perf=list('missing') on left, =list('calprd') on rightOpt.perf=list('missing') on left, =list('calprd') on right

Opt.perf=list(‘missing’) on left, =list(‘calprd’) on right

On the left graph, we observe that the performances of all assemblages can be predicted using nbcl = 4 clusters of components, except the assemblages “110”, “211” and “237”. On the right graph, we observe that the performances of all assemblages are as well predicted as modelled, except those of 3 assemblages (2 plotted as red circle, and one as gold square).

The right graph shows predicted performances versus modelled performances.

fclust_plot(res, main = "BioDiv2", opt.perf = list("prd", "seq", "aov", pvalue = 0.01))
Opt.perf=list('prd', 'seq') plots performances predicted from1 to nbOpt component clustersOpt.perf=list('prd', 'seq') plots performances predicted from1 to nbOpt component clustersOpt.perf=list('prd', 'seq') plots performances predicted from1 to nbOpt component clustersOpt.perf=list('prd', 'seq') plots performances predicted from1 to nbOpt component clusters

Opt.perf=list(‘prd’, ‘seq’) plots performances predicted from1 to nbOpt component clusters

The number m of assembly motifs generates by the combination of nbcl component clusters is m = 2^nbcl - 1. It is 1, 3, 7, 15 and 31 for 1, 2, 3, 4 and 5 clusters. Here the optimum number of clusters is 4, and 13 among 15 assembly motifs are observed in the experiment.

fclust_plot(res, main = "BioDiv2", opt.perf = list("prd", "aov", pvalue = 0.01))
fclust_plot(res, main = "BioDiv2", opt.perf = list("prd", "ass"))
Opt.perf=list('prd', 'ass') adds the names of assemblages on plotOpt.perf=list('prd', 'ass') adds the names of assemblages on plot

Opt.perf=list(‘prd’, ‘ass’) adds the names of assemblages on plot

The option opt.ass

opt.ass plot different performances of some given, identified assemblages. The option is activated only when an assemblage performance is observed over several experiments (see vignette The option xpr : multi-functionality in functClust ).

The option opt.motif

opt.motif manages the plot of mean performances of assembly motifs as boxplots. The option is a list, that can include opt.motif = list(“obs”, “cal”, “prd”, cols, “hor”, “seq”, pvalue, “all”):

fclust_plot(res, main = "BioDiv2", opt.motif = list("prd", "hor", "seq", "aov", pvalue = 0.01))
Opt.motif=list('obs', 'seq') plots performances sorted by assembly motifOpt.motif=list('obs', 'seq') plots performances sorted by assembly motifOpt.motif=list('obs', 'seq') plots performances sorted by assembly motifOpt.motif=list('obs', 'seq') plots performances sorted by assembly motif

Opt.motif=list(‘obs’, ‘seq’) plots performances sorted by assembly motif

The boxplots show observed performances of assemblages belonging to different assembly motifs. Means are indicated with different symbols, of same shape and colour as those used in opt.perf. The size (in number of assemblages) of each assembly motifs is indicated on the left side of the graph, and the results of a variance analysis of observed performances by assembly motif is indicated on the right side of the graph. The blue dashed lines are mean modelled performances (opt.motif = “prd”) and red dashed lines are mean observed performances.

The option opt.comp

opt.comp manages the plot as boxplot of observed mean performances of assemblages that contain a given component. The option can include opt.comp = list(“tree”, “perf”, “hor”, cols, pvalue, “zoom”, window, “all”):

fclust_plot(res, main = "BioDiv2", opt.comp = list("tree", "leg", "aov", pvalue = 0.01))
Opt.comp=list('tree', 'leg') plots set of performances that contain a given component, sorted as the hierarchical tree of components