plot_fmotif: Plot as boxplot mean performances of assemblages sorted by...

In functClust: Functional Clustering of Redundant Components of a System

Description

The function plots, as vertical or horizontal boxplots, composition and mean performance sorted by assembly motifs.

Usage

plot_fmotif(fres, nbcl = 0, main = "Title", opt.motif = NULL )

Arguments

fres	an object generated by the function fclust.
nbcl	an integer. The integer indicates the number of component clusters to take into account. It can be lower than or equals to the optimum number fres$nbOpt of component clusters.
main	a string, that is used as the first, reference part of the title of each graph.
opt.motif	a list, that can include opt.motif = list("obs", "cal", "prd", cols, "hor", "ver", "seq", pvalue, "all"). This option list manages the plot of mean performances of assembly motifs as boxplots, observed, modelled or predicted by cross-validation, horizontally or vertically, sorted by increasing or decreasing mean values, from 1 to nbOpt clusters of components. The item order in list is any. "obs", "cal", "prd": plot the observed, modelled or predicted by cross-validation mean performances of assembly motifs as boxplots. Assembly motifs are named as the combinations of component clusters (see "opt.tree"). The coloured squares are the mean performances of assembly motifs. Size (number of observed assemblages) of assembly motifs is indicated on the left of boxplots. The red dashed line is the mean performance of assembly motifs. If "aov" is checked, groups significantly different (at a p-value < pvalue) are indicated by differents letters on the right of boxplots. "hor": plot boxplots as horizontal boxes: x-axis corresponds to assemblage performances, and y-axis corresponds to assembly motifs. It "hor" is not checked, boxplots are plotted as vertical boxes: x-axis corresponds to assembly motifs, and y-axis corresponds to assemblage performances. Option "ver" can also be used: "ver" = !"hor". "seq": plot mean performances of assembly motifs, from 2 to nbOpt number of component clusters. Remember that number m of assembly motifs increases with the number nbcl of component clusters (m = 2^nbcl - 1). When the optimal number of component clusters is large, this option is useful to determine a number of component clusters lower than the optimal number of component clusters. Assembly motifs are named as the combinations of component clusters (see "opt.tree"). pvalue = value: a probability used as threshold in the variance analysis. Then pvalue must be higher than 0 and lower than 1. pvalue must be informed when "aov" is checked. Groups significantly different (at a p-value < pvalue) are then indicated by differents letters on the right of boxplots. "all": plot all possible graphs. This option is equivalent to opt.motif = list("obs", "cal", "prd", "seq", "aov", pvalue = 0.001). <c2><b6>

Details

None.

Value

Nothing. It is a procedure.

See Also

plot_ftrees plot primary and secondary trees resulting from functional clustering
plot_fperf plot observed, modelled and predicted performances resulting from functional clustering
plot_fass plot performances of some given assemblages
plot_fmotif plot as boxplot mean performances of assemblages sorted by assembly motifs
plot_fcomp plot as boxplot mean performances of assemblages containing a given component
fclust_plot plot all possible outputs of functional clustering

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