journal.bib

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@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{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{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{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{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{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{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{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{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{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{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{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{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{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{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{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}
}
@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{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{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{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}
}

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