The function plots primary and secondary hierarchical trees of component clustering.
plot_ftrees(fres, nbcl = 0, main = "Title", opt.tree = NULL )an object generated by the function fclust.
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$nbOpt of component clusters.
a string, that is used as the first, reference part of the title of each graph.
a list, that can include
opt.tree = list("cal", "prd", cols, "zoom", window, "all").
This option list manages the plot of primary and secondary trees
of component clustering,
simplified or not, focussed on the main component clusters or not,
coloured by the user or not.
The item order in list is any.
"cal" plots the primary tree of component clustering,
from trunk until leaves.
At trunk level, when all components are clustered
into a large, trivial cluster,
the coefficient of determination R2 is low.
At the leaves level, when each component is isolated in a singleton,
the coefficient of determination is always equal to 1.
The primary tree is therefore necessarily over-fitted
near the leaves level.
The optimum number fres$nbOpt of component clusters
is determined by the minimum AICc.
The blue dashed line indicates the level
(optimum number fres$nbOpt of component clusters)
where the tree must be optimally cut up.
The red solid line indicates the value of tree efficiency E
at the nbcl-level.
The component clusters are named by lowercase letters,
from left to right as "a", "b", "c", ...:
the name and content of each component cluster
is written on the following page.
"prd" plots the validated,
secondary tree of component clustering,
from trunk until validated leaves.
Secondary tree is the primary tree cut
at the level of the optimal number nbOpt of component clusters.
nbOpt is determined
by the first lowest value of AIC along the primary tree.
The red solid line indicates the value of tree efficiency E.
R2 and E are stored in fres$tStats.
The component clusters are named by lowercase letters,
from left to right as "a", "b", "c", ...:
the name and content of each component cluster
is written on the following page.
cols is a vector of colours, characters or integers,
of same length as the number of components. This option specifies
the colour of each component.
The components labelled by the same integer
have the same colour. If cols is not specified,
the components that belong to a same cluster
a posteriori determined have the same colour.
This option is useful when an a priori clustering is known,
to identify the components a priori clustered
into the a posteriori clustering.
"zoom" if "cal" or "prd" is checked,
this option allows
to only plot the first, significant component clusters.
The cluster on the far right (the cluster named by the last letter)
is most often a large cluster, that includes many components
of which the effects of assemblage performance are not significant.
When the number of components is large, the tree is dense
and the names of components are confusing.
The option is useful to focus on the left, more signficant,
part of the primary or secondary tree.
If "zoom" is checked, window must be informed.
If not, the function stops with an error message.
Note that the large cluster, that includes many components,
is always represented by at least one component.
window an integer, that
specifies the number of components to plot.
window must be informed when "zoom" is checked.
If window is higher than the number of components, it is ignored.
If window is lower than the number of significant components,
it is ajusted in such a way that the large cluster,
that includes many components,
is at least represented by one component.
"all" plots all possible graphs.
This option is equivalent to
opt.tree = list("cal", "prd", "zoom", window = 20).
If the number of components is lower than 20,
the option is equivalent to opt.tree = list("cal", "prd").
Nothing. It is a procedure.
None.
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