Kooperativer Bibliotheksverbund

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Format: 189 S.

ISBN: 978-3-85132-590-4

Series Statement: Schriften / Pierre Legendre 1

Language: German

Subjects: Philosophy

Keywords: Aufsatzsammlung ; Aufsatzsammlung

URL: Inhaltsverzeichnis

URL: http://d-nb.info/999404989/04

Author information: Legendre, Pierre 1930-2023

UID:

Format: 1 online resource (1007 p.)

Edition: 3rd English ed.

ISBN: 1-283-73471-0 , 0-444-53869-0

Series Statement: Developments in environmental modelling ; 24

Uniform Title: Ecologie numérique.

Content: The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others.An updated, 3rd English edition of the most widely cited book on quantitative analysis of multivariate ecological dataRelates ecological questions to methods of statistical analysis, with a clear descripti

Note: Description based upon print version of record. , Front Cover; Numerical Ecology; Copyright; Contents; Preface; Chapter 1: Complex ecological data sets; 1.0 Numerical analysis of ecological data; 1.1 Spatial structure, spatial dependence, spatial correlation; 1.2 Statistical testing by permutation; 1.3 Computer programs and packages; 1.4 Ecological descriptors; 1.5 Coding; 1.6 Missing data; 1.7 Software; Chapter 2: Matrix algebra: a summary; 2.0 Matrix algebra; 2.1 The ecological data matrix; 2.2 Association matrices; 2.3 Special matrices; 2.4 Vectors and scaling; 2.5 Matrix addition and multiplication; 2.6 Determinant; 2.7 Rank of a matrix , 2.8 Matrix inversion2.9 Eigenvalues and eigenvectors; 2.10 Some properties of eigenvalues and eigenvectors; 2.11 Singular value decomposition; 2.12 Software; Chapter 3: Dimensional analysis in ecology; 3.0 Dimensional analysis; 3.1 Dimensions; 3.2 Fundamental principles and the Pi theorem; 3.3 The complete set of dimensionless products; 3.4 Scale factors and models; Chapter 4: Multidimensional quantitative data; 4.0 Multidimensional statistics; 4.1 Multidimensional variables and dispersion matrix; 4.2 Correlation matrix; 4.3 Multinormal distribution; 4.4 Principal axes , 4.5 Multiple and partial correlations4.6 Tests of normality and multinormality; 4.7 Software; Chapter 5: Multidimensional semiquantitative data; 5.0 Nonparametric statistics; 5.1 Quantitative, semiquantitative, and qualitative multivariates; 5.2 One-dimensional nonparametric statistics; 5.3 Rank correlations; 5.4 Coefficient of concordance; 5.5 Software; Chapter 6: Multidimensional qualitative data; 6.0 General principles; 6.1 Information and entropy; 6.2 Two-way contingency tables; 6.3 Multiway contingency tables; 6.4 Contingency tables: correspondence; 6.5 Species diversity; 6.6 Software , Chapter 7: Ecological resemblance7.0 The basis for clustering and ordination; 7.1 Q and R analyses; 7.2 Association coefficients; 7.3 Q mode: similarity coefficients; 7.4 Q mode: distance coefficients; 7.5 R mode: coefficients of dependence; 7.6 Choice of a coefficient; 7.7 Transformations for community composition data; 7.8 Software; Chapter 8: Cluster analysis; 8.0 A search for discontinuities; 8.1 Definitions; 8.2 The basic model: single linkage clustering; 8.3 Cophenetic matrix and ultrametric property; 8.4 The panoply of methods; 8.5 Hierarchical agglomerative clustering; 8.6 Reversals , 8.7 Hierarchical divisive clustering8.8 Partitioning by K-means; 8.9 Species clustering: biological associations; 8.10 Seriation; 8.11 Multivariate regression trees (MRT); 8.12 Clustering statistics; 8.13 Cluster validation; 8.14 Cluster representation and choice of a method; 8.15 Software; Chapter 9: Ordination in reduced space; 9.0 Projecting data sets in a few dimensions; 9.1 Principal component analysis (PCA); 9.2 Correspondence analysis (CA); 9.3 Principal coordinate analysis (PCoA); 9.4 Nonmetric multidimensional scaling (nMDS); 9.5 Software , Chapter 10: Interpretation of ecological structures , English

Additional Edition: ISBN 0-444-53868-2

Language: English

UID:

Format: XI, 585 S. , Ill., graph. Darst.

ISBN: 3540160868 , 0387160868

Series Statement: NATO ASI series : Series G 14

Language: Undetermined

Subjects: Biology

Keywords: Ökologie ; Mathematische Methode ; Ökologie ; Statistik ; Methode ; Ökologie ; Quantitative Methode ; Ökologie ; Statistik ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift

URL: Inhaltstext

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Format: 1 Online-Ressource (xv, 435 Seiten) : , Illustrationen, Diagramme.

Edition: Second edtion

ISBN: 978-3-319-71404-2

Series Statement: Use R!

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-319-71403-5

Language: English

Subjects: Computer Science , Biology

Keywords: Ökologie ; Statistik ; Numerisches Modell ; R

DOI: 10.1007/978-3-319-71404-2

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: IX, 137 S. : Ill.

Series Statement: Ius commune / Sonderhefte 2

Language: Latin

Subjects: Law

URL: Inhaltsverzeichnis

Author information: Legendre, Pierre 1930-2023

UID:

Format: 1 online resource (870 p.)

Edition: 2nd ed.

ISBN: 9786611036430 , 1-281-03643-9 , 0-08-053787-1 , 1-281-02159-8 , 9786611021597 , 0-08-052317-X

Series Statement: Developments in environmental modelling ; 20

Content: The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others. Compared to the first edition of Numerical Ecology, this second edition includes three new chapters, dealing with the analysis of semiquantitative data, canonical analysis and spatial analysis. New sections have been added to al

Note: Description based upon print version of record. , Cover; Contents; Preface; Chapter 1. Complex ecological data sets; 1.0 Numerical analysis of ecological data; 1.1 Autocorrelation and spatial structure; 1.2 Statistical testing by permutation; 1.3 Computers; 1.4 Ecological descriptors; 1.5 Coding; 1.6 Missing data; Chapter 2. Matrix algebra: a summary; 2.0 Matrix algebra; 2.1 The ecological data matrix; 2.2 Association matrices; 2.3 Special matrices; 2.4 Vectors and scaling; 2.5 Matrix addition and multiplication; 2.6 Determinant; 2.7 The rank of a matrix; 2.8 Matrix inversion; 2.9 Eigenvalues and eigenvectors , 2.10 Some properties of eigenvalues and eigenvectors2.11 Singular value decomposition; Chapter 3. Dimensional analysis in ecology; 3.0 Dimensional analysis; 3.1 Dimensions; 3.2 Fundamental principles and the Pi theorem; 3.3 The complete set of dimensionless products; 3.4 Scale factors and models; Chapter 4. Multidimensional quantitative data; 4.0 Multidimensional statistics; 4.1 Multidimensional variables and dispersion matrix; 4.2 Correlation matrix; 4.3 Multinormal distribution; 4.4 Principal axes; 4.5 Multiple and partial correlations; 4.6 Multinormal conditional distribution , 4.7 Tests of normality and multinormalityChapter 5. Multidimensional semiquantitative data; 5.0 Nonparametric statistics; 5.1 Quantitative, semiquantitative, and qualitative multivariates; 5.2 One-dimensional nonparametric statistics; 5.3 Multidimensional ranking tests; Chapter 6. Multidimensional qualitative data; 6.0 General principles; 6.1 Information and entropy; 6.2 Two-way contingency tables; 6.3 Multiway contingency tables; 6.4 Contingency tables: correspondence; 6.5 Species diversity 235; Chapter 7. Ecological resemblance; 7.0 The basis for clustering and ordination , 7.1 Q and R analyses7.2 Association coefficients; 7.3 Q mode: similarity coefficients; 7.4 Q mode: distance coeffficients; 7.5 R mode: coeffcients of dependence; 7.6 Choice of a coef.cient; 7.7 Computer programs and packages; Chapter 8. Cluster analysis; 8.0 A search for discontinuities; 8.1 Definitions; 8.2 The basic model: single linkage clustering; 8.3 Cophenetic matrix and ultrametric property; 8.4 The panoply of methods; 8.5 Hierarchical agglomerative clustering; 8.6 Reversals; 8.7 Hierarchical divisive clustering; 8.8 Partitioning by K-means , 8.9 Species clustering: biological associations8.10 Seriation; 8.11 Clustering statistics; 8.12 Cluster validation; 8.13 Cluster representation and choice of a method; Chapter 9. Ordination in reduced space; 9.0 Projecting data sets in a few dimensions; 9.1 Principal component analysis (PCA); 9.2 Principal coordinate analysis (PCoA); 9.3 Nonmetric multidimensional scaling (MDS); 9.4 Correspondence analysis (CA); 9.5 Factor analysis; Chapter 10. Interpretation of ecological structures; 10.0 Ecological structures; 10.1 Clustering and ordination; 10.2 The mathematics of ecological interpretation , 10.3 Regression , English

Additional Edition: ISBN 0-444-89249-4

Additional Edition: ISBN 0-444-89250-8

Language: English

UID:

Format: 1 online resource (288 pages).

ISBN: 0-12-815692-9 , 0-12-815043-2

Series Statement: Spatial econometrics and spatial statistics

Content: "Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both theoretical foundations and guidance on practical implementation for the eigenvector spatial filtering (ESF) technique. ESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in georeferenced data analyses. With its flexible structure, ESF can be easily applied to generalized linear regression models. The book discusses ESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, and spatial interaction models. In addition, it provides a tutorial for ESF model specification and interfaces, including author developed, user-friendly software"--

Note: Front Cover -- Spatial Regression Analysis Using Eigenvector Spatial Filtering -- Copyright -- Dedication -- Contents -- Foreword -- Moran eigenvector spatial filtering: Multiple origins and convergence -- A word about the theoretical background for MESF in ecology -- Extensions and the future of MESF analysis -- References -- Preface -- Data description -- A preview of the book's content -- References -- Chapter 1: Spatial autocorrelation -- 1.1. Defining SA -- 1.1.1. A mathematical formularization of the first law of geography -- 1.1.2. Quantifying spatial relationships: The spatial weights matrix -- 1.1.3. Different measurements for different data types: Quantifying SA -- 1.1.4. The MC: Distributional theory -- 1.2. Impacts of SA on attribute statistical distributions -- 1.2.1. Effects of spatial dependence: Deviating from independent observations -- 1.2.2. SA and the Moran scatterplot -- 1.2.3. SA and histograms -- 1.3. Summary -- Appendix 1.A. The mean and variance of the MC for linear regression residuals -- References -- Chapter 2: An introduction to spectral analysis -- 2.1. Representing SA in the spectral domain -- 2.1.1. SA: From a spatial frequency to a spatial spectral domain -- 2.1.2. Eigenvalues and eigenvectors -- 2.1.3. Principal components analysis: A reconnaissance -- 2.1.4. The spectral decomposition of a modified SWM -- 2.1.5. Representing the MC with eigenfunctions -- 2.1.6. Visualizing map patterns with eigenvectors -- 2.2. The spectral analysis of one-dimensional data -- 2.3. The spectral analysis of two-dimensional data -- 2.4. The spectral analysis of three-dimensional data -- 2.5. Summary -- Appendix 2.A. The spectral decomposition of a SWM -- References -- Chapter 3: MESF and linear regression -- 3.1. A theoretical foundation for ESFs -- 3.1.1. The fundamental theorem of MESF. , 3.1.2. Map pattern and SA: Heterogeneity in map-wide trends -- 3.2. Estimating an ESF as an OLS problem: An illustrative linear regression example -- 3.2.1. The selection of eigenvectors to construct an ESF -- 3.2.2. Selected criteria for assessing regression models: The PRESS statistic, residual diagnostics, and multicollinearity -- 3.2.3. Interpreting an ESF and its parameter estimates -- 3.2.4. Comparisons between ESF and SAR model specification results -- 3.3. Simulation experiments based upon ESFs -- 3.4. ESF prediction with linear regression -- 3.5. Summary -- References -- Chapter 4: Software implementation for constructing an ESF, with special reference to linear regression -- 4.1. Software implementation -- 4.2. Geographic scale and resolution issues for ESFs -- 4.3. Determining the candidate set of eigenvectors -- 4.4. Extensions to large georeferenced datasets: Implications for big spatial data -- 4.4.1. A validation demonstration for approximate ESFs -- 4.4.2. An exploration of a massively large remotely sensed image -- 4.4.3. Correct SWM eigenvectors for a regular square tessellation -- 4.5. Summary -- Appendix 4.A. Frequency distributions of the 10,000 NDVI values for the validation analysis, with superimposed theoretica ... -- References -- Chapter 5: MESF and generalized linear regression -- 5.1. The logistic regression model specification -- 5.2. The binomial regression model specification -- 5.3. The Poisson regression model specification -- 5.3.1. Population density -- 5.3.2. Counts of wildfires -- 5.4. The negative binomial regression model specification -- 5.4.1. Population density -- 5.4.2. Counts of wildfires -- 5.5. The selection of eigenvectors to construct an ESF for GLMs -- 5.6. ESF prediction with generalized linear regression -- 5.7. Summary -- References -- Chapter 6: Modeling spatial heterogeneity with MESF. , 6.1. Spatially varying coefficients -- 6.2. An ESF expansion of regression coefficients -- 6.3. Multicollinearity in spatially varying coefficients -- 6.4. Local SA ESFs -- 6.4.1. Local versus global SA -- 6.4.2. Local MCs for ESFs -- 6.4.3. Local GRs for ESFs -- 6.4.4. Local Getis-Ord statistics for ESFs -- 6.5. Summary -- Appendix 6.A. Bonferroni adjustment simulation experiment results -- References -- Chapter : Spatial interaction modeling -- 7.1. Initial spatial interaction descriptions of internal Texas migration -- 7.2. Spatially autocorrelated origin and destination variables -- 7.3. Network autocorrelation in migration flows -- 7.4. Spatial and network autocorrelation in journey-to-work flows: A reconnaissance -- 7.5. A toy example: Exemplifying the necessary data structures -- 7.6. Summary -- Appendix 7.A. A Corpus Christi toy spatial interaction dataset R code -- Appendix 7.B. The functions.R code -- References -- Chapter 8: Space-time modeling -- 8.1. Estimating a SURE term -- 8.1.1. A RE term estimation sensitivity analysis -- 8.1.2. Prediction based on an estimated RE term -- 8.2. Space-time data structures: Eigenvector space-time filters -- 8.2.1. The space-time lagged spatial structure specification: Results for Texas population density -- 8.2.2. The space-time contemporaneous spatial structure specification: Results for Texas population density -- 8.2.3. ESTF prediction -- 8.3. A toy example: Exemplifying the necessary data structures -- 8.4. Summary -- Appendix 8.A. A Corpus Christi toy space-time dataset R code -- References -- Chapter 9: MESF and multivariate statistical analysis -- 9.1. PCA, FA, and MESF -- 9.1.1. Selected mathematical features of PCA -- 9.1.2. Multicollinearity -- 9.1.3. Moving from PCA to FA: Seeking parsimony -- 9.2. MANOVA and MESF -- 9.3. DFA and MESF -- 9.3.1. The DFA eigenfunction problem. , 9.3.2. DFA as a regression problem: Two-regions DFA -- 9.4. CCA and MESF -- 9.4.1. The CCA eigenfunction problem -- 9.4.2. ESFs spanning sets of attribute variables -- 9.5. CA and MESF -- 9.6. Summary -- Appendix 9.A. A dendogram from Ward's algorithm for original attribute data -- Appendix 9.B. Multivariate statistical analysis R code -- References -- Chapter 10: Concluding comments: Toy dataset implementation demonstrations -- 10.1. The toy example: A Dallas-Fort Worth metroplex county geographic resolution dataset -- 10.2. The setup -- 10.3. Moran scatterplots -- 10.4. Normal approximation regression: The spatial linear regression specification -- 10.5. Poisson regression: The MESF specification -- 10.6. Binomial regression: The MESF specification -- 10.7. Spatially varying coefficients: The MESF specification -- 10.8. Summary -- References -- Epilogue -- References -- Index -- Back Cover.

Language: English

UID:

Format: 536 S. , Ill.

ISBN: 9783851325942

Series Statement: Schriften / Pierre Legendre Bd. 6

Uniform Title: Le désir politique de dieu 〈dt.〉

Language: German

Keywords: Staat ; Repräsentation ; Politische Theorie ; Bezugssystem ; Anthropologie

URL: Inhaltstext

URL: Inhaltsverzeichnis

Author information: Legendre, Pierre 1930-2023

UID:

Format: X, 853 S. , Ill., graph. Darst.

Edition: 2. ed.

ISBN: 0444892494 , 0444892508

Series Statement: Developments in environmental modelling 20

Uniform Title: Écologie numérique 〈engl.〉

Note: Translated from the French. - Includes bibliographical references and index. - Previous English ed.: 1983

Language: English

Subjects: Biology

Keywords: Ökologie ; Mathematische Methode ; Ökologie ; Statistik ; Ökologie ; Biometrie ; Ökologie ; Mathematische Methode

URL: Inhaltstext

URL: http://www.loc.gov/catdir/enhancements/fy0602/98045198-d.html

URL: http://www.loc.gov/catdir/enhancements/fy0602/98045198-t.html

UID:

Format: 190 S. , 240 mm x 160 mm

ISBN: 9783851325928

Series Statement: Schriften / Pierre Legendre. Hrsg. von Georg Mein und Clemens Pornschlegel 3

Uniform Title: Le crime du corporal Lortie, Traité sur le Père 〈dt.〉

Language: German

Subjects: Law , Philosophy

Keywords: Lortie, Denis 1960- ; Attentat ; Vatermord ; Lortie, Denis 1960- ; Attentat ; Vatermord

URL: Inhaltstext

URL: Inhaltsverzeichnis

Author information: Legendre, Pierre 1930-2023

Author information: Pornschlegel, Clemens 1958-