plot_fperf: Plot modelled and predicted performances resulting from...
In functClust: Functional Clustering of Redundant Components of a System
Description
Usage
Arguments
Details
Value
See Also
Description
The function plots
observed, modelled and predicted performances
resulting from functional clustering
Usage
1
plot_fperf(fres, nbcl = 0, main = "Title", opt.perf = 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.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).
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.
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Description Usage Arguments Details Value See Also
Description
The function plots observed, modelled and predicted performances resulting from functional clustering
Usage
1 | plot_fperf(fres, nbcl = 0, main = "Title", opt.perf = NULL )
|
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.perf |
a list, that can include
|
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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