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functClust (version 0.1.6)

Functional Clustering of Redundant Components of a System

Description

Cluster together the components that make up an interactive system on the basis of their functional redundancy for one or more collective, systemic performances. Plot the hierarchical tree of component clusters, the modelled and predicted performances of component assemblages, and other results associated with a functional clustering. Test and prioritize the significance of the different components that make up the interactive system, of the different assemblages of components that make up the dataset, and of the different performances observed on the component assemblages. The method finds application in ecology, for instance, where the system is an ecosystem, the components are organisms or species, and the systemic performance is the production of biomass or the respiration of the ecosystem. The method is extensively described in Jaillard B, Deleporte P, Loreau M, Violle C (2018) "A combinatorial analysis using observational data identifies species that govern ecosystem functioning" .

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Version

Install

install.packages('functClust')

Monthly Downloads

37

Version

0.1.6

License

GPL-3

Maintainer

Beno<c3><ae>t Jaillard

Last Published

December 2nd, 2020

Functions in functClust (0.1.6)

CedarCreek.2004.2006.res

Functional clustering of species used in Cedar Creek experiment for the years 2004, 2005 and 2006
AICc

AICc of two numeric vectors
CedarCreek.2004.2006.test.components

Test of significance of components (species) used in Cedar Creek experiment for the years 2004, 2005 and 2006
AIC_

AIC of two numeric vectors
CedarCreek.2004.2006.test.assemblages

Test of significance of species assemblages (plots) used in Cedar Creek experiment for the years 2004, 2005 and 2006
CedarCreek.2004.2006.test.performances

Test of significance of different performances (yearly biomass) used in Cedar Creek experiment for the years 2004, 2005 and 2006
CedarCreek.2004.2006.boot.assemblages

Evaluate by bootstrapping the robustness of species clustering to the number of assemblages (plots) used in the cluster analysis.
add_ass_names

Add assemblage names on a plot
CedarCreek.2004.2006.boot.performances

Evaluate by bootstrapping the robustness of species clustering to the number of performances (annual biomass) used in the cluster analysis.
CedarCreek.2004.2006.dat

Data of Cedar Creek experiment for the years 2004, 2005 and 2006
amean_byelt_jack

Arithmetic mean by elements occurring within assembly motif using jackknife method
affect_motifs

Label assemblages by assembly motif
build_random_matrix

Build a matrix of missing indices
R2

Pearson' R2.
calibrate_amean_byelt

Modelling of performances by components occurring within an assembly motif
CedarCreek.2004.res

Functional clustering of species used in Cedar Creek experiment for the year 2004
R2mse

Coefficient of Determination (R2)
calibrate_byminrss

Modelling of performances of assemblages
amean_bymot_LOO

Arithmetic mean by assembly motif using leave-one-out method
calibrate_amean_byelt_xpr

Modelling of performances by components occurring within an assembly motif over several experiments
calibrate_amean_bymot_xpr

Modelling of performances by assembly motif over several experiments
delstr_begin

Delete the beginning of a string
delstr_end

Delete the end of a string
amean_byelt

Arithmetic mean by components occurring within an assembly motif
amean

Arithmetic mean
calibrate_amean_bymot

Modelling of performances by assembly motif
check_plot_options

Check options
check_ftree

Check a matrix of component affectation to functional groups
agglomerative_ftree

Hierarchical agglomerative clustering of components
amean_bymot_jack

Arithmetic mean of performances by assembly motif using jackknife method
argmax

Index of the maximum values of a vector
fboot_performances

Evaluate the robustness of a functional clustering to perturbations of data
amean_byelt_LOO

Predicting the performances by elements occurring within assembly motif using leave-one-out method
fboot_assemblages

Evaluate the robustness of a functional clustering to perturbations of data
argmin

Index of the minimum values of a vector
first_argchr

Index of a first instance of char in a string
fboot_one_point

Evaluate the robustness of a functional clustering to perturbations of data
argchr

Index of different instances of a given char in a string
first_argmax

Index of the first maximum value of a vector
fepsilon

Value of epsilon used in functClust
asd

Arithmetic standard deviation
check_foption

Check an option
char_to_int

Convert a character into integer vector
complete_ftree

Hierarchical clustering of components from an a priori component clustering
compute_fit_stats

Statistics of model goodness-of-fit
fboot_plot

Plot the robustness of a functional clustering evaluated by bootstrapping from 1 to (all-1) observations
compute_ftree_stats

Valid hierarchical tree and Statistics of model goodness-of-fit
ftest_plot_performances

Plot the evaluation of weight of each performance on functional clustering
first_argmin

Index of the first minimum value of a vector
fclust_write

Record a functional clustering for one or several performances
fboot_read

Read the robustness of a functional clustering evaluated by bootstrapping from 1 to (all-1) observations
fboot_read_one_point

Read a test of significance of functional clustering
fcolours

Series of colours used in functClust
fboot_write

Record the robustness of a functional clustering evaluated by bootstrapping from 1 to (all-1) observations
compute_motif_stats

Statistics of assembly motifs
calibrate_gmean_bymot

Modelling of performances by assembly motif
extend_letters

Extends a vector of letters
fclust

Build a functional clustering for one or more performances
divisive_ftree

Hierarchical divisive clustering of components
ftest_read

Read the significance of different variables of a functional clustering
fnbdigits

Number of digits kept when writing in a file
functClust-package

Functional Clustering of Redundant Components of a System
ftest_write

Record the significance of different variables of a functional clustering
fclust_plot

Plot various graphs of a functional clustering for one or several performances
gmean_bymot_jack

Geometric mean by assembly motif using jackknife method
binary_to_logical

Convert a binary into logical matrix
list_in_quote

Concat a list of strings
fwindow

Standard number of components to plot in the package functClust
gsd

Geometric standard deviation
logical_to_binary

Convert a logical into binary matrix
calibrate_gmean_byelt

Modelling the performances by components occurring within an assembly motif
gmean

Geometric mean
plot_fcomp

Plot mean performances of assemblages containing a given component
plot_clusters_content

Write the components belonging to each functional group
cut_ftree

Cut a tree at a given level
calibrate_gmean_bymot_xpr

Modelling of performances by assembly motif over several experiments
concat_by_line

Concat a list of strings segmented by line
calibrate_gmean_byelt_xpr

Modelling the performances by components occurring within an assembly motif over several experiments
compact_index

Compact a vector of indices
check_repeat

Check for identical assemblages
plot_motifs_histo

Plot histogram of assemblage performances by assembly motif
plot_fmotif

Plot as boxplot mean performances of assemblages sorted by assembly motifs
extend_vector

Extends a vector
fboot_write_one_point

Record a test of significance of functional clustering
plot_motifs_content

Write the assemblages belonging to each assembly motif
format_fclust

Format a raw dataset for a functional clustering
fit_ftree

Clustering of components for the performances of assemblages
plot_components_box

Plot mean performances of assemblages that contain a given component
plot_motifs_box

Plot performances of assembly motifs
plot_ftrees

Plot trees resulting from functional clustering
simplify_ftree

Simplify a tree by keeping only significant components
predict_performance

Predicting performances of assemblages by only knowing their elemental composition
pvalue_dependent_R2mse

Test for the dependence of two R2
fstd_colour

Standard colour used in the package functClust
fpvalue

Standard p-value used in the package functClust
ftest_assemblages

Evaluate the weight of each assemblage on functional clustering
read_finputs

Read the file containing initial data of occurrence and performances for a functional clustering
gmean_byelt_jack

Geometric mean by elements occurring within assembly motif using jackknife method
ftest_components

Evaluate the weight of each component on functional clustering
fletters

Series of letters used in functClust
fboot

Evaluate the robustness of a functional clustering by bootstrapping from 1 to (all-1) observations
sort_components

Sort assembly centroids by decreasing or increasing mean performances
mark_string

Mathematical mark of an ordered string vector
gmean_bymot_LOO

Geometric mean by assembly motif using leave-one-out method
test_dependent_R2mse

Test for the dependence of two Coefficients of Determination R2
test_dependent_R2

Test for the dependence of two Pearson' R2
maffect_motifs

Labelling of a whole tree of component clustering by assembly motif
notify_fclust

Message for following functional clustering computation
validate_gmean_byelt_jack_xpr

Predicting the performances by elements occurring within assembly motif using jackknife method over several experiments
validate_gmean_byelt_jack

Predicting the performances by elements occurring within assembly motif using jackknife method
make_fclust

Make the formatted result from a functional clustering
validate_using_cross_validation

Predicting by cross-validation of assembly performances
read_fmatrices

Read the file containing the Matrices resulting from a functional clustering
plot_prediction_LOO

Plot Simulated and Predicted vs Observed performances
fclust_predict

nnnn
wamean

Weighted arithmetical mean
plot_motifs_infos

Plot reference graphs for checking that plot of sorted data are right
mean_fct

Switch mean function.
plot_by_page

Plot a list of strings on several pages
fclust_read

Read a functional clustering for one or several performances
fsymbols

Series of symbols used in functClust
predict_amean_byelt

Prediction of supplementary assemblages computed
predict_amean_bymot

Prediction of supplementary assemblages by motif
reverse_table

Reverse a table along its first dimension
rss_clustering

Residual Sum of Squares of a given clustering model
rss

Residual Sum of Squares (RSS).
ftest_plot_assemblages

Plot the evaluation of weight of each assemblage on functional clustering
ftest_plot_components

Plot test of the weight of each component on functional clustering
validate_amean_byelt_LOO_xpr

Predicting the performances by elements occurring within assembly motif using leave-one-out method over several experiments
rmse

Root Mean Square Error (RMSE)
validate_amean_byelt_LOO

Predicting the performances by elements occurring within assembly motif using leave-one-out method
test_posthoc

Test posthoc of variance analysis
validate_amean_bymot_jack_xpr

Predicting the performances by assembly motif using jackknife method over several experiments
index_inturn

Reverse an indexation
is_binary

Test if a vector is binary
ftest

Test the significance of different variables of a functional clustering
plot_fass

Plot performances of some given assemblages
plot_components_content

Write the components belonging to each functional group
name_motifs

Labelling (by lowercase letters) of assemblages by assembly motif
validate_ftree

Predictions of assembly performances using a species clustering tree
shift_affectElt

Renumber a vector of component affectation
predict_gmean_bymot

Prediction of supplementary assemblages
rss_total

Total Residual Sum of Squares of observed performances
predict_gmean_byelt

Prediction of supplementary assemblages computed gmean = by using geometric mean byelt = by motif WITH taking into account species contribution by including all the assemblages, even the one to predict for any Function (for instance Fobs)
sort_ftree

Sort the resulting file of tree
validate_amean_byelt_jack

Predicting the performances by elements occurring within assembly motif using jackknife method
validate_amean_bymot_jack

Predicting the performances by assembly motif using jackknife method
ftest_performances

Evaluate the weight of each performance on functional clustering
ftest_plot

Plot the significance of different variables of a functional clustering
validate_amean_bymot_LOO_xpr

Predicting the performances by assembly motif using leave-one-out method over several experiments
sort_matrix

sort a matrix
validate_gmean_byelt_LOO

Geometric mean by elements occurring within assembly motif using leave-one-out method
gmean_byelt

Geometric mean by components occurring within an assembly motif
validate_gmean_bymot_LOO

Predicting the performances by assembly motif using leave-one-out method
wasd

Weighted arithmetic standard deviation
gmean_byelt_LOO

Geometric mean by elements occurring within assembly motif using leave-one-out method
validate_gmean_byelt_LOO_xpr

Geometric mean by elements occurring within assembly motif using leave-one-out method over several experiments
nb_tests

Maximal number of clustering models to test
validate_gmean_bymot_LOO_xpr

Predicting the performances by assembly motif using leave-one-out method over several experiments
plot_prediction_simple

Plot Simulated or Predicted vs Observed performances
mse

Mean Square Error (MSE).
plot_ftree

Plot a hierarchical tree
plot_fperf

Plot modelled and predicted performances resulting from functional clustering
pmse

Probability associated with the Coefficient of determination (R2)
name_clusters

Labelling (by lowercase letters) of components by cluster
wgmean

Weighted geometric mean
points_sd

Plot a point with x- and y-error bars
plot_stats

Plot statistics of combinatorial analysis
sort_motifs

Sort assembly motifs
stirling

Number of Stirling of second kind
validate_amean_byelt_jack_xpr

Predicting the performances by elements occurring within assembly motif using jackknife method over several experiments
read_fstats

Read the file containing the statistics of a functional clustering
validate_amean_bymot_LOO

Predicting the performances by assembly motif using leave-one-out method
read_foptions

Read the file containing options for a functional clustering
read_ftrees

Read the file containing the trees resulting from a functional clustering
validate_gmean_bymot_jack

Predicting the performances by assembly motif using jackknife method
remove_components

Remove components from dataset
wgsd

Weighted geometric standard deviation
validate_gmean_bymot_jack_xpr

Predicting the performances by assembly motif using jackknife method over several experiments