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@ARTICLE{SD148,
author = {Dolédec, S. and Chessel, D.},
title = {Rythmes saisonniers et composantes stationnelles en milieu aquatique
{I}- {D}escription d'un plan d'observations complet par projection
de variables},
journal = {Acta Oecologica - Oecologia Generalis},
year = {1987},
volume = {8},
pages = {403-426},
number = {3},
endnotereftype = {Journal Article},
file = {SD148.pdf:SD148.pdf:PDF},
keywords = {ACPVI,projection, Inter-Intra},
pdf = {SD148.pdf},
shorttitle = {Rythmes saisonniers et composantes stationnelles en milieu aquatique
I- Description d'un plan d'observations complet par projection de
variables}
}
@ARTICLE{SD149,
author = {Dolédec, S. and Chessel, D.},
title = {Rythmes saisonniers et composantes stationnelles en milieu aquatique
{II}- {P}rise en compte et élimination d'effets dans un tableau faunistique},
journal = {Acta Oecologica - Oecologia Generalis},
year = {1989},
volume = {10},
pages = {207-232},
number = {3},
endnotereftype = {Journal Article},
file = {SD149.pdf:SD149.pdf:PDF},
keywords = {ACPVI, Projection, Inter-Intra},
pdf = {SD149.pdf},
shorttitle = {Rythmes saisonniers et composantes stationnelles en milieu aquatique
II- Prise en compte et élimination d'effets dans un tableau faunistique}
}
@ARTICLE{SD655,
author = {Thioulouse, J. and Chessel, D.},
title = {A method for reciprocal scaling of species tolerance and sample diversity},
journal = {Ecology},
year = {1992},
volume = {73},
pages = {670-680},
endnotereftype = {Journal Article},
file = {SD655.pdf:SD655.pdf:PDF},
keywords = {AFC, Ordination, Courbe de réponse, Représentation graphique, Niche},
pdf = {SD655.pdf},
shorttitle = {A method for reciprocal scaling of species tolerance and sample diversity}
}
@ARTICLE{SD151,
author = {Dolédec, S. and Chessel, D.},
title = {Co-inertia analysis: an alternative method for studying species-environment
relationships},
journal = {Freshwater Biology},
year = {1994},
volume = {31},
pages = {277-294},
endnotereftype = {Journal Article},
file = {SD151.pdf:SD151.pdf:PDF},
keywords = {Co-Inertie},
pdf = {SD151.pdf},
shorttitle = {Co-inertia analysis: an alternative method for studying species-environment
relationships}
}
@ARTICLE{SD656,
author = {Thioulouse, J. and Chessel, D. and Champely, S.},
title = {Multivariate analysis of spatial patterns: a unified approach to
local and global structures},
journal = {Environmental and Ecological Statistics},
year = {1995},
volume = {2},
pages = {1-14},
endnotereftype = {Journal Article},
file = {SD656.pdf:SD656.pdf:PDF},
keywords = {AFC, Spatial, ACP, Graphe de voisinage, Multi-échelle, Geostatistique,
Multivarié},
pdf = {SD656.pdf},
shorttitle = {Multivariate analysis of spatial patterns: a unified approach to local
and global structures}
}
@ARTICLE{SD154,
author = {Dolédec, S. and Chessel, D. and ter Braak, C.J.F. and Champely, S.},
title = {Matching species traits to environmental variables: a new three-table
ordination method},
journal = {Environmental and Ecological Statistics},
year = {1996},
volume = {3},
pages = {143-166},
endnotereftype = {Journal Article},
file = {SD154.pdf:SD154.pdf:PDF},
keywords = {ordination, Traits Biologiques, Schéma de dualité, RLQ},
pdf = {SD154.pdf},
shorttitle = {Matching species traits to environmental variables: a new three-table
ordination method}
}
@ARTICLE{SD657,
author = {Thioulouse, J. and Chessel, D. and Dolédec, S. and Olivier, J.M.},
title = {{ADE-4}: a multivariate analysis and graphical display software},
journal = {Statistics and Computing},
year = {1997},
volume = {7},
pages = {75-83},
endnotereftype = {Journal Article},
file = {SD657.pdf:SD657.pdf:PDF},
keywords = {Ordination, Logiciel},
pdf = {SD657.pdf},
shorttitle = {ADE-4: a multivariate analysis and graphical display software}
}
@ARTICLE{SD152,
author = {Dolédec, S. and Chessel, D. and Gimaret-Carpentier, C.},
title = {Niche separation in community analysis: a new method},
journal = {Ecology},
year = {2000},
volume = {81},
pages = {2914-2927},
number = {10},
endnotereftype = {Journal Article},
file = {SD152.pdf:SD152.pdf:PDF},
keywords = {Niche, ACP, ACC, Courbe de réponse, Schéma de dualité, OMI, Sélection},
pdf = {SD152.pdf},
shorttitle = {Niche separation in community analysis: a new method}
}
@ARTICLE{SD518,
author = {Perrière, G. and Thioulouse, J.},
title = {Use and misuse of correspondence analysis in codon usage studies},
journal = {Nucleic Acids Research},
year = {2002},
volume = {30},
pages = {4548-4555},
endnotereftype = {Journal Article},
file = {SD518.pdf:SD518.pdf:PDF},
keywords = {AFC, ACP%, ACP},
pdf = {SD518.pdf},
shorttitle = {Use and misuse of correspondence analysis in codon usage studies}
}
@ARTICLE{SD161,
author = {Dray, S. and Chessel, D. and Thioulouse, J.},
title = {Procrustean co-inertia analysis for the linking of multivariate data
sets},
journal = {Ecoscience},
year = {2003},
volume = {10},
pages = {110-119},
number = {1},
abstract = {Procrustes analysis is a method for fitting a set of points to another.
These two sets of points are often defined by the measurements of
two sets of variables for the same individuals (e.g., measurements
of species abundances and environmental variables at the same sites).
We present a solution for graphical representation of the results
of procrustes analysis when the number of variables in each of the
two datasets exceeds two. This method is named procrustean co-inertia
analysis because it is based on the joint use of procrustes analysis
and co-inertia analysis, which is a coupling method for finding linear
combinations of two sets of variables of maximal covariance. It provides
better graphical representation of the concordance between the two
datasets than classical co-inertia analysis. Moreover, distance matrices
can be introduced in the analysis to improve its ecological meaning.
Lastly, a randomization test equivalent to PROTEST is proposed as
an alternative to the Mantel test. An ecological example is presented
to illustrate the method.},
endnotereftype = {Journal Article},
file = {SD161.pdf:SD161.pdf:PDF},
keywords = {Procuste,Co-Inertie,distance},
pdf = {SD161.pdf},
shorttitle = {Procrustean co-inertia analysis for the linking of multivariate data
sets}
}
@ARTICLE{SD162,
author = {Dray, S. and Chessel, D. and Thioulouse, J.},
title = {Co-inertia analysis and the linking of ecological data tables},
journal = {Ecology},
year = {2003},
volume = {84},
pages = {3078-3089},
abstract = {Ecological studies often require studying the common structure of
a pair of data tables. Co-inertia analysis is a multivariate method
for coupling two tables. It is often neglected by ecologists who
prefer the widely used methods of redundancy analysis and canonical
correspondence analysis. We present the co-inertia criterion for
measuring the adequacy between two data sets. Co-inertia analysis
is based on this criterion as are canonical correspondence analysis
or canonical correlation analysis, but the latter two have additional
constraints. Co-inertia analysis is very flexible and allows many
possibilities for coupling. Co-inertia analysis is suitable for quantitative
and/or qualitative or fuzzy environmental variables. Moreover, various
weighting of sites and various transformations and/or centering of
species data are available for this method. Hence, more ecological
considerations can be taken into account in the statistical procedures.
Moreover, the principle of this method is very general and can be
easily extended to the case of distance matrices or to the case of
more than two tables. Simulated ecological data are used to compare
the co-inertia approach with other available methods.},
endnotereftype = {Journal Article},
file = {SD162.pdf:SD162.pdf:PDF},
pdf = {SD162.pdf},
shorttitle = {Co-inertia analysis and the linking of ecological data tables}
}
@ARTICLE{SD799,
author = {Chessel, D. and Dufour, A.-.B. and Thioulouse, J.},
title = {The ade4 package - {I}: {O}ne-table methods},
journal = {R News},
year = {2004},
volume = {4},
pages = {5-10},
file = {SD799.pdf:SD799.pdf:PDF},
pdf = {SD799.pdf}
}
@ARTICLE{SD734,
author = {Pavoine, S. and Dufour, A.-.B. and Chessel, D.},
title = {From dissimilarities among species to dissimilarities among communities:
a double principal coordinate analysis},
journal = {Journal of Theoretical Biology},
year = {2004},
volume = {228},
pages = {523-537},
endnotereftype = {Journal Article},
file = {SD734.pdf:SD734.pdf:PDF},
keywords = {Mesure de biodiversité, PCO},
pdf = {SD734.pdf},
shorttitle = {From dissimilarities among species to dissimilarities among communities:
a double principal coordinate analysis}
}
@ARTICLE{SD658,
author = {Thioulouse, J. and Simier, M. and Chessel, D.},
title = {Simultaneous analysis of a sequence of pairs of ecological tables
with the {STATICO} method},
journal = {Ecology},
year = {2004},
volume = {85},
pages = {272-283},
comment = {Absent},
endnotereftype = {Journal Article},
file = {SD658.pdf:SD658.pdf:PDF},
keywords = {Multi-tableaux, Co-Inertie},
pdf = {SD658.pdf},
shorttitle = {Simultaneous analysis of a sequence of pairs of ecological tables
with the STATICO method}
}
@ARTICLE{SD839,
author = {Dray, S. and Dufour, A.B.},
title = {The ade4 package: implementing the duality diagram for ecologists},
journal = {Journal of Statistical Software},
year = {2007},
volume = {22},
pages = {1-20},
number = {4},
abstract = {Multivariate analyses are well known and widely used to identify and
understand structures of ecological communities. The ade4 package
for the R statistical environment proposes a great number of multivariate
methods. Its implementation follows the tradition of the French school
of "Analyse des Données" and is based on the use of the duality diagram.
We present the theory of the duality diagram and discuss its implementation
in ade4. Classes and main functions are presented. An example is
given to illustrate the ade4 philosophy.},
file = {SD839.pdf:SD839.pdf:PDF},
owner = {stephane},
pdf = {SD839.pdf},
timestamp = {2007.02.08}
}
@ARTICLE{SD836,
author = {Dray, S. and Dufour, A.B. and Chessel, D.},
title = {The ade4 package - {II}: {T}wo-table and \textit{K}-table methods},
journal = {R News},
year = {2007},
volume = {7},
pages = {47-52},
number = {2},
file = {SD836.pdf:SD836.pdf:PDF},
owner = {stephane},
pdf = {SD836.pdf},
timestamp = {2007.01.09}
}
@ARTICLE{SD910,
author = {Pavoine, S. and Blondel, J. and Baguette, M. and Chessel, D.},
title = {A new technique for ordering asymmetrical three-dimensional data
sets in ecology},
journal = {Ecology},
year = {2007},
volume = {88},
pages = {512-523},
file = {SD910.pdf:SD910.pdf:PDF},
keywords = {Multi-tableaux, AFC},
owner = {stephane},
pdf = {SD910.pdf},
timestamp = {2008.04.18}
}
@ARTICLE{SD835,
author = {Thioulouse, J. and Dray, S.},
title = {Interactive multivariate data analysis in {R} with the ade4 and ade4{T}k{GUI}
packages},
journal = {Journal of Statistical Software},
year = {2007},
volume = {22},
pages = {1-14},
number = {5},
abstract = {ade4 is a multivariate data analysis package for the R statistical
environment, and ade4TkGUI is a Tcl/Tk graphical user interface for
the most essential methods of ade4. Both packages are available on
CRAN. An overview of ade4TkGUI is presented, and the pros and cons
of this approach are discussed. We conclude that command line interfaces
(CLI) and graphical user interfaces (GUI) are complementary. ade4TkGUI
can be valuable for biologists and particularly for ecologists who
are often occasional users of R. It can spare them having to acquire
an in-depth knowledge of R, and it can help first time users in a
first approach.},
file = {SD835.pdf:SD835.pdf:PDF},
owner = {stephane},
pdf = {SD835.pdf},
timestamp = {2007.01.09}
}
@ARTICLE{SD805,
author = {Dray, S.},
title = {On the number of principal components: A test of dimensionality based
on measurements of similarity between matrices},
journal = {Computational Statistics and Data Analysis},
year = {2008},
volume = {52},
pages = {2228-2237},
abstract = {An important problem in principal component analysis (PCA) is the
estimation of the correct number of components to retain. PCA is
most often used to reduce a set of observed variables to a new set
of variables of lower dimensionality. The choice of this dimensionality
is a crucial step for the interpretation of results or subsequent
analyses, because it could lead to a loss of information (underestimation)
or the introduction of random noise (overestimation). New techniques
are proposed to evaluate the dimensionality in PCA. They are based
on similarity measurements, singular value decomposition and permutation
procedures. A simulation study is conducted to evaluate the relative
merits of the proposed approaches. Results showed that one method
based on the RV coefficient is very accurate and seems to be more
efficient than other existing approaches.},
file = {SD805.pdf:SD805.pdf:PDF},
owner = {stephane},
pdf = {SD805.pdf},
timestamp = {2006.05.03}
}
@ARTICLE{SD807,
author = {Dray, S. and Saïd, S. and Débias, F.},
title = {Spatial ordination of vegetation data using a generalization of {W}artenberg's
multivariate spatial correlation},
journal = {Journal of Vegetation Science},
year = {2008},
volume = {19},
pages = {45-56},
abstract = {Question: Are there spatial structures in the composition of plant
communities?
Methods: Identification and measurement of spatial structures is a
topic of great interest in plant ecology. Univariate measurements
of spatial autocorrelation such as Moran's I and Geary's c are widely
used, but extensions to the multivariate case (i.e. multi-species)
are rare. Here, we propose a multivariate spatial analysis based
on Moran's I (MULTISPATI) by introducing a row-sum standardized spatial
weight matrix in the statistical triplet notation. This analysis,
which is a generalization of Wartenberg's approach to multivariate
spatial correlation, would imply a compromise between the relations
among many variables (multivariate analysis) and their spatial structure
(autocorrelation). MULTISPATI approach is very flexible and can handle
various kinds of data (quantitative and/or qualitative data, contingency
tables). A study is presented to illustrate the method using a spatial
version of Correspondence Analysis.
Location: Territoire d'Etude et d'Expérimentation de Trois-Fontaines
(eastern France).
Results: Ordination of vegetation plots by this spatial analysis is
quite robust with reference to rare species and highlights spatial
patterns related to soil properties.},
file = {SD807.pdf:SD807.pdf:PDF},
owner = {stephane},
pdf = {SD807.pdf},
timestamp = {2006.05.03}
}
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