9780444892508 - Numerical Ecology (Volume 24) (Developments in Environmental Modelling, Volume 24, Band 20) von Legendre, P.; Legendre, Louis (5 Ergebnisse)

Paperback. Zustand: Very Good. Numerical Ecology: Volume 24 (Developments in Environmental Modelling) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Paperback. Zustand: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1700grams, ISBN:0444892508.

Zustand: Very Good. xv, 853, text figs. . PB. Light creasing to spine. Vg. Second English edition. 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. [9780444892508].

kart. Zustand: Sehr gut. Second impression. 852 pgs. Good copy with small signs of wear, little corner bump. - Contents: Preface xi -- Complex ecological data sets -- Numerical analysis of ecological data -- Autocorrelation and spatial structure -- -Types of spatial structures -- - Tests of statistical significance in the presence of autocorrelation -- - Classical sampling and spatial structure -- Statistical testing by permutation -- -Classical tests of significance -- -Permutation tests -- -Numerical example -- - Remarks on permutation tests -- Computers -- Ecological descriptors -- - Mathematical types of descriptor -- - Intensive -- extensive -- additive -- and non- additive descriptors -- Coding -- - Linear transformation -- -Nonlinear transformations -- - Combining descriptors -- - Ranging and standardization -- - Implicit transformation in association coefficients -- -Normalization -- -Dummy variable (binary) coding -- Missing data -- - Deleting rows or columns -- -Accommodating algorithms to missing data -- - Estimating missing values -- Matrix algebra: a summary -- Matrix algebra -- The ecological data matrix -- Association matrices -- Special matrices -- Vectors and scaling -- Contents -- vi -- Matrix addition and multiplication -- Determinant -- The rank of a matrix -- Matrix inversion -- Eigenvalues and eigenvectors -- -Computation -- - Numerical examples -- Some properties of eigenvalues and eigenvectors -- Singular value decomposition -- Dimensional analysis in ecology -- Dimensional analysis -- Dimensions -- Fundamental principles and the Pi theorem -- The complete set of dimensionless products -- Scale factors and models -- Multidimensional quantitative data -- Multidimensional statistics -- Multidimensional variables and dispersion matrix -- Correlation matrix -- Multinormal distribution -- Principal axes -- Multiple and partial correlations -- -Multiple linear correlation -- -Partial correlation -- - -- statistical significance -- -Interpretation of correlation coefficien -- - Causal modelling using correlations -- Multinormal conditional distribution -- Tests of normality and multinormality -- Multidimensional semiquantitative data -- Nonparametric statistics -- Quantitative -- semiquantitative -- and qualitative multivariates -- One-dimensional nonparametric statistics -- Multidimensional ranking tests -- Multidimensional qualitative data -- General principles -- Information and entropy -- Two-way contingency tables -- Multiway contingency tables -- Contingency tables: correspondence -- Species diversity -- - Diversity -- - Evenness -- equitability -- Ecological resemblance -- The basis for clustering and ordination -- Q and R analyses -- Association coefficients -- Q mode: similarity coefficients -- - Symmetrical binary coefficients -- - Asymmetrical binary coefficients -- -Symmetrical quantitative coefficients -- -Asymmetrical quantitative coefficients -- - Probabilistic coefficients -- Q mode: distance coefficients -- - Metric distances -- -Semimetrics -- R mode: coefficients of dependence -- - Descriptors other than species abundances -- -Species abundances: biological associations -- Choice of a coefficient -- Computer programs and packages -- Cluster analysis -- A search for discontinuities -- Definitions -- The basic model: single linkage clustering -- Cophenetic matrix and ultrametric property -- - Cophenetic matrix -- - Ultrametric property -- The panoply of methods -- -Sequential versus simultaneous algorithms -- -Agglomeration versus division -- -Monothetic versus polythetic methods -- -Hierarchical versus non-hierarchical methods -- -Probabilistic versus non-probabilistic methods -- Hierarchical agglomerative clustering -- -Single linkage agglomerative clustering -- -Complete linkage agglomerative clustering -- -Intermediate linkage clustering -- -Unweighted arithmetic average clustering (UPGMA) -- -Weighted arithmetic average clustering (WPGMA) -- - Unweighted centroid clustering (UPGMC) -- -Weighted centroid clustering (WPGMC) -- -Ward's minimum variance method -- - General agglomerative clustering model -- -Flexible clustering -- - Information analysis -- Reversals -- Hierarchical divisive clustering -- -Monothetic methods -- -Polythetic methods -- ordination space -- -TWINSPAN -- -Division in -- Partitioning by K-means -- Species clustering: biological associations -- - Probabilistic -- -Non-hierarchical complete linkage clustering -- - Indicator species -- clustering -- Seriation -- Clustering statistics -- -Connectedness and isolation -- -Cophenetic correlation and related measures -- Cluster validation -- Cluster representation and choice of a method -- Ordination in reduced space -- Projecting data sets in a few dimensions -- Principal component analysis (PCA) -- -Computing the eigenvectors -- - Computing and representing the principal components -- -Contributions of descriptors -- - Biplots -- -Principal components of a correlation matrix -- -The meaningful components -- -Misuses of principal components -- -Ecological applications -- -Algorithms -- Principal coordinate analysis (PCOA) -- -Computation -- -Numerical example -- -Rationale of the method -- -Negative eigenvalues -- -Ecological applications -- - Algorithms -- Nonmetric multidimensional scaling (MDS) -- Correspondence analysis (CA) -- -Computation -- -Numerical example -- - Interpretation -- -Site x species data tables -- -Arch effect -- -Ecological applications -- -Algorithms -- Factor analysis -- Interpretation of ecological structures -- Ecological structures -- Clustering and ordination -- The mathematics of ecological interpretation -- Regression -- -Simple linear regression: model I -- -Simple linear regression: model II -- - Multiple linear regression -- - Polynomial regression -- -Partial linear regression -- -Nonlinear regression -- -Logistic regression -- - Splines and LOWESS smoothing -- Path analysis -- Matrix comparisons -- -Mantel test -- -More than two matrices -- -ANOSIM test -- - Procrustes analysis -- The -- th-c.