fclust_plot: Plot various graphs of a functional clustering for one or...
In functClust: Functional Clustering of Redundant Components of a System
Description
Usage
Arguments
Details
Value
References
See Also
Examples
Description
The function plots numerous useful graphs
for illustrating results and the ways by which they were obtained:
hierarchical trees of component clustering,
composition and mean performance of assembly motifs,
mean performance of assemblages containing a given components,
observed, simulated and predicted performances of assemblages
labelled by assembly motif,
performances of given assemblages...
Usage
1
2
3
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$nbOpt of component clusters.
main
a string, that is used
as the first, reference part of the title of each graph.
opt.tree
a list, that can include
opt.tree = list("cal", "prd", cols, "zoom", window, "all").
This option list manages the plot of primary and secondary trees
of component clustering,
simplified or not, focussed on the main component clusters or not,
coloured by the user or not.
The item order in list is any.
"cal" plots the primary tree of component clustering,
from trunk until leaves.
At trunk level, when all components are clustered
into a large, trivial cluster,
the coefficient of determination R2 is low.
At the leaves level, when each component is isolated in a singleton,
the coefficient of determination is always equal to 1.
The primary tree is therefore necessarily over-fitted
near the leaves level.
The optimum number fres$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").
opt.perf
a list, that can include
opt.perf = list("stats_I", "stats_II",
"cal", "prd", "missing", "pub", "calprd",
"seq", "ass", "aov", pvalue, "all").
This option list manages the plot
of observed, modelled and predicted performances of assemblages,
and associated statistics. It also allows to plot performances of
some given, identified assemblages.
The item order in list is any.
"stats_I", "stats_II": plot the statistics associated to
fit of primary tree that best accounts
for observed performances ("stats_I"),
and of secondary tree that best predicts
observed performances of assemblages ("stats_II").
Four graphs are plotted:
1. coefficient of determination R2
and efficiency E of models of component clustering
(on y-axis) versus the number of component clusters (on x-axis);
2. the ratio of assemblage perfomances
that cannot be predicted by cross-validation ("predicting ratio");
3. and 4. the Akaike Information Criterion,
corrected AICc or not AIC for small datasets.
The green solid line indicates the first minimum of AIC
that corresponds to the optimum number nbOpt
of component clusters to consider.
"cal", "prd": plot modelled performances
versus observed performances ("cal",
or modelled and predicted by cross-validation performances
versus observed performances ("prd",
for a number of component clusters increasing from 1
until the number of component clusters where efficiency E
is maximum.
Different symbols correspond to different assembly motifs.
The prediction error induced by cross-validation is indicated
by a short vertical line.
The blue dashed lines are mean performances.
The red solid line is 1:1 bissector line.
The number of component clusters is indicated on graph left top.
Predicting ratio and coefficient of determination R2
of the clustering
are indicated on graph right bottom.
If "prd" is checked, efficiency E
and E/R2 ratio are added.
If "aov" is checked, groups significantly different
(at a p-value < pvalue) are indicated by differents letters
on the right of graph.
"missing": the option "prd" plot
modelled and predicted by cross-validation performances
versus observed performances,
using different symbols for different assembly motifs.
The option "missing" plot the same data,
but in using different symbols according to the clustering model
used for predicting the performances of assemblages.
This option allows to identify assemblages
of which the performance cannot be predicted
using the clustering model of the current level.
The assemblages are plotted and named
using the symbol corresponding to the level
of the used clustering model.
The blue dashed lines are mean performances.
The red solid line is 1:1 bissector line.
The number of component clusters is indicated on graph left top.
Predicting ratio and coefficient of determination of the clustering
are indicated on graph right bottom.
If "aov" is checked, groups significantly different
(at a p-value < pvalue) are indicated by differents letters
on the right of graph.
"pub": the option "prd" plot
modelled and predicted by cross-validation performances
versus observed performances,
using different symbols for different assembly motifs.
The option "pub" plot the same data,
but in using only one symbol.
This option is useful for publication.
The blue dashed lines are mean performances.
The red solid line is 1:1 bissector line.
The number of component clusters is indicated on graph left top.
Predicting ratio and coefficient of determination of the clustering
are indicated on graph right bottom.
If "aov" is checked, groups significantly different
(at a p-value < pvalue) are indicated by differents letters
on the right of graph.
"calprd": plot performances predicted by cross-validation
versus performances predicted by clustering model
("modelled performances"). This option is useful
to identify which assembly motifs become difficult
to predict by cross-validation.
The blue dashed lines are mean performances.
The red solid line is 1:1 bissector line.
The number of component clusters is indicated on graph left top.
Predicting ratio and coefficient of determination of the clustering
are indicated on graph right bottom.
If "aov" is checked, groups significantly different
(at a p-value < pvalue) are indicated by differents letters
on the right of graph. The letters are located
at mean(Fprd[motif == label]).
"seq": plot performances of assembly motifs,
from 1 to nbMax 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").
"ass" plot the name of each assemblage
close to its performance. This option can be used with
the options "cal", "prd", "pub"
and "calprd". It must be used only
if the number of assemblages is small.
If the number of assemblages is large,
the following option "opt.ass" is more convenient.
"aov": does a variance analysis of assemblage performances
by assembly motifs, and plot the result on the right of graphs.
Different letters correspond to
groups significantly different at a p-value < pvalue.
If "aov" is checked, pvalue must be informed.
If not, pvalue = 0.001.
pvalue: 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.pref = list("cal", "prd", "pub", "calprd",
"aov", pvalue = 0.001).
opt.ass
a list, that include
opt.ass = list(sample, who).
This option plot modelled and predicted by cross-validation performances
versus observed performances,
for a small sample of assemblages randomly drawn (sample),
or for given, identified assemblages chosen by the user (who).
The item order in list is any.
sample: an integer. This integer
specifies the number of assemblages
to randomly drawn in the assemblage set,
the plot as the option opt.perf = list("prd").
All chosen assemblages are plotted on a same graph.
who: a list of assemblage names.
The list contains the names of assemblages to plot.
Each assemblage is plotted on a specific graph.
This option is useful when ssemblage performances
are observed over several experiments.
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>
opt.comp
a list, that can include
opt.comp = list("tree", "perf", "hor", "ver", cols,
pvalue, "zoom", window, "all").
This option list manages the plot as boxplot
of observed mean performances
of assemblages that contain a given component,
horizontally or vertically,
components sorted by increasing or decreasing mean values,
or components sorted like the clustering tree.
The item order in list is any.
"tree", "perf": plot the observed mean performances
of assemblages that contain a given component as boxplots.
Each set of assemblages that contains a given component
is named by the contained component.
The coloured squares are the mean performances of assemblage sets.
Size (number of observed assemblages) of assemblage sets
is indicated on the left of boxplots.
The red dashed line is the mean performance of assemblage sets.
If "aov" is checked, groups significantly different
(at a p-value < pvalue) are indicated by differents letters
on the right of boxplots.
If "tree": is checked, mean performances
of assemblages that contain a given component
are sorted like the clustering tree.
If "perf" is checked, mean performances
of assemblages that contain a given component
are sorted by increasing mean performances.
"hor": plot boxplots as horizontal boxes:
x-axis corresponds to assemblage performances,
and y-axis corresponds to assemblage sets.
It "hor" is not checked,
boxplots are plotted as vertical boxes:
x-axis corresponds to assemblage sets,
and y-axis corresponds to assemblage performances.
Option "ver" can also be used: "ver" = !"hor".
cols: is a vector of 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.
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("tree", "aov", pvalue = 0.001,
"zoom", window = 20).
opt.all
This option is equivalent to
opt.tree = "all", opt.comp = "all", opt.motif = "all",
opt.perf = "all". This option is convenient to overview
the different options of the function fclust_plot.
Details
If all the options are NULL,
that is opt.tree = NULL, opt.perf = NULL, opt.ass = NULL,
opt.motif = NULL, opt.comp = NULL, opt.all = NULL,
the function plot the main results, that are:
the secondary tree (opt.tree = "prd"),
assembly motifs as horizontal boxplots
(opt.motif = list("obs", "hor"))), and
modelled and predicted by cross-validation mean performances
versus observed performances (opt.perf = "prd").
Value
Nothing. It is a procedure.
References
Jaillard, B., Richon, C., Deleporte, P., Loreau, M. and Violle, C. (2018)
An a posteriori species clustering
for quantifying the effects of species
interactions on ecosystem functioning.
Methods in Ecology and Evolution, 9:704-715.
https://doi.org/10.1111/2041-210X.12920.
Jaillard, B., Deleporte, P., Loreau, M. and Violle, C. (2018)
A combinatorial analysis using observational data
identifies species that govern ecosystem functioning.
PLoS ONE 13(8): e0201135.
https://doi.org/10.1371/journal.pone.0201135.
See Also
fclust: make a functional clustering,
fclust_plot: plot the results of a functional clustering,
fclust_write: save the results of a functional clustering,
fclust_read: read the results of a functional clustering.
plot_ftrees plot primary and secondary trees
resulting from a functional clusterin,
plot_fperf plot observed, modelled and predicted performances
resulting from a 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 a functional clustering.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
res <- CedarCreek.2004.res
# plot the hierarchical tree of functionally redundant components
fclust_plot(res, main = "BioDiv2 2004", opt.tree = "prd")
# plot AIC and AICc versus the number of clusters of components
layout(matrix(c(1,2,3,4), nrow = 2, ncol = 2, byrow = TRUE))
fclust_plot(res, main = "BioDiv2 2004", opt.perf = "stats_II")
layout(1)
# plot the performances modelled and predicted versus observed performances
fclust_plot(res, main = "BioDiv2 2004", opt.perf = "prd")
# plot the performances sorted by assembly motifs
layout(matrix(c(1,2), nrow = 1, ncol = 2, byrow = TRUE))
fclust_plot(res, main = "BioDiv2 2004",
opt.motif = c("obs", "prd", "hor"))
layout(1)
functClust documentation built on Dec. 2, 2020, 5:06 p.m.
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Description Usage Arguments Details Value References See Also Examples
Description
The function plots numerous useful graphs for illustrating results and the ways by which they were obtained: hierarchical trees of component clustering, composition and mean performance of assembly motifs, mean performance of assemblages containing a given components, observed, simulated and predicted performances of assemblages labelled by assembly motif, performances of given assemblages...
Usage
1 2 3 |
Arguments
fres |
an object generated by the function |
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 |
main |
a string, that is used as the first, reference part of the title of each graph. |
opt.tree |
a list, that can include
|
opt.perf |
a list, that can include
|
opt.ass |
a list, that include
|
opt.motif |
a list, that can include
|
opt.comp |
a list, that can include
|
opt.all |
This option is equivalent to
|
Details
If all the options are NULL,
that is opt.tree = NULL, opt.perf = NULL, opt.ass = NULL,
opt.motif = NULL, opt.comp = NULL, opt.all = NULL,
the function plot the main results, that are:
the secondary tree (opt.tree = "prd"),
assembly motifs as horizontal boxplots
(opt.motif = list("obs", "hor"))), and
modelled and predicted by cross-validation mean performances
versus observed performances (opt.perf = "prd").
Value
Nothing. It is a procedure.
References
Jaillard, B., Richon, C., Deleporte, P., Loreau, M. and Violle, C. (2018)
An a posteriori species clustering
for quantifying the effects of species
interactions on ecosystem functioning.
Methods in Ecology and Evolution, 9:704-715.
https://doi.org/10.1111/2041-210X.12920.
Jaillard, B., Deleporte, P., Loreau, M. and Violle, C. (2018)
A combinatorial analysis using observational data
identifies species that govern ecosystem functioning.
PLoS ONE 13(8): e0201135.
https://doi.org/10.1371/journal.pone.0201135.
See Also
fclust: make a functional clustering,
fclust_plot: plot the results of a functional clustering,
fclust_write: save the results of a functional clustering,
fclust_read: read the results of a functional clustering.
plot_ftrees plot primary and secondary trees
resulting from a functional clusterin,
plot_fperf plot observed, modelled and predicted performances
resulting from a 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 a functional clustering.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | res <- CedarCreek.2004.res
# plot the hierarchical tree of functionally redundant components
fclust_plot(res, main = "BioDiv2 2004", opt.tree = "prd")
# plot AIC and AICc versus the number of clusters of components
layout(matrix(c(1,2,3,4), nrow = 2, ncol = 2, byrow = TRUE))
fclust_plot(res, main = "BioDiv2 2004", opt.perf = "stats_II")
layout(1)
# plot the performances modelled and predicted versus observed performances
fclust_plot(res, main = "BioDiv2 2004", opt.perf = "prd")
# plot the performances sorted by assembly motifs
layout(matrix(c(1,2), nrow = 1, ncol = 2, byrow = TRUE))
fclust_plot(res, main = "BioDiv2 2004",
opt.motif = c("obs", "prd", "hor"))
layout(1)
|
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