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000827106 019__ $$a1033643155$$a1034578544
000827106 020__ $$a9783319714042$$q(electronic book)
000827106 020__ $$a331971404X$$q(electronic book)
000827106 020__ $$z9783319714035
000827106 0247_ $$a10.1007/978-3-319-71404-2$$2doi
000827106 035__ $$aSP(OCoLC)on1029352637
000827106 035__ $$aSP(OCoLC)1029352637$$z(OCoLC)1033643155$$z(OCoLC)1034578544
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000827106 049__ $$aISEA
000827106 050_4 $$aQH541.15.M34
000827106 08204 $$a577.015/1$$223
000827106 1001_ $$aBorcard, Daniel,$$eauthor.
000827106 24510 $$aNumerical ecology with R /$$cDaniel Borcard, François Gillet, Pierre Legendre.
000827106 250__ $$aSecond edition.
000827106 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000827106 300__ $$a1 online resource.
000827106 336__ $$atext$$btxt$$2rdacontent
000827106 337__ $$acomputer$$bc$$2rdamedia
000827106 338__ $$aonline resource$$bcr$$2rdacarrier
000827106 347__ $$atext file$$bPDF$$2rda
000827106 4901_ $$aUse R!
000827106 504__ $$aIncludes bibliographical references and index.
000827106 5050_ $$aIntro; Preface; Contents; About the Authors; Supplementary Material; Chapter 1: Introduction; 1.1 Why Numerical Ecology?; 1.2 Why R?; 1.3 Readership and Structure of the Book; 1.4 How to Use This Book; 1.5 The Data Sets; 1.5.1 The Doubs Fish Data; 1.5.2 The Oribatid Mite Data; 1.6 A Quick Reminder About Help Sources; 1.7 Now It Is Time; Chapter 2: Exploratory Data Analysis; 2.1 Objectives; 2.2 Data Exploration; 2.2.1 Data Extraction; 2.2.2 Species Data: First Contact; 2.2.3 Species Data: A Closer Look; 2.2.4 Ecological Data Transformation; 2.2.5 Environmental Data; 2.3 Conclusion
000827106 5058_ $$aChapter 3: Association Measures and Matrices3.1 Objectives; 3.2 The Main Categories of Association Measures (Short Overview); 3.2.1 Q Mode and R Mode; 3.2.2 Symmetrical or Asymmetrical Coefficients in Q Mode: The Double-Zero Problem; 3.2.3 Association Measures for Qualitative or Quantitative Data; 3.2.4 To Summarize; 3.3 Q Mode: Computing Dissimilarity Matrices Among Objects; 3.3.1 Q Mode: Quantitative Species Data; 3.3.2 Q Mode: Binary (Presence-Absence) Species Data; 3.3.3 Q Mode: Quantitative Data (Excluding Species Abundances)
000827106 5058_ $$a3.3.4 Q Mode: Binary Data (Excluding Species Presence-Absence Data)3.3.5 Q Mode: Mixed Types Including Categorical (Qualitative Multiclass) Variables; 3.4 R Mode: Computing Dependence Matrices Among Variables; 3.4.1 R Mode: Species Abundance Data; 3.4.2 R Mode: Species Presence-Absence Data; 3.4.3 R Mode: Quantitative and Ordinal Data (Other than Species Abundances); 3.4.4 R Mode: Binary Data (Other than Species Abundance Data); 3.5 Pre-transformations for Species Data; 3.6 Conclusion; Chapter 4: Cluster Analysis; 4.1 Objectives; 4.2 Clustering Overview
000827106 5058_ $$a4.3 Hierarchical Clustering Based on Links4.3.1 Single Linkage Agglomerative Clustering; 4.3.2 Complete Linkage Agglomerative Clustering; 4.4 Average Agglomerative Clustering; 4.5 Wardś Minimum Variance Clustering; 4.6 Flexible Clustering; 4.7 Interpreting and Comparing Hierarchical Clustering Results; 4.7.1 Introduction; 4.7.2 Cophenetic Correlation; 4.7.3 Looking for Interpretable Clusters; 4.7.3.1 Graph of the Fusion Level Values; 4.7.3.2 Compare Two Dendrograms to Highlight Common Subtrees; 4.7.3.3 Multiscale Bootstrap Resampling; 4.7.3.4 Average Silhouette Widths
000827106 5058_ $$a4.7.3.5 Comparison Between the Dissimilarity Matrix and Binary Matrices Representing Partitions4.7.3.6 Species Fidelity Analysis; 4.7.3.7 Silhouette Plot of the Final Partition; 4.7.3.8 Final Dendrogram with Graphical Options; 4.7.3.9 Spatial Plot of the Clustering Result; 4.7.3.10 Heat Map and Ordered Community Table; 4.8 Non-hierarchical Clustering; 4.8.1 k-means Partitioning; 4.8.1.1 k-means with Random Starts; 4.8.1.2 Use of k-means Partitioning to Optimize an Independently Obtained Classification; 4.8.2 Partitioning Around Medoids (PAM); 4.9 Comparison with Environmental Data
000827106 506__ $$aAccess limited to authorized users.
000827106 520__ $$aThis much anticipated volume maps the connections between numerical ecology and its implementation in the 'R' language. Beginning with concise theoretical overviews, the authors go on to explore the methodology using applied and extensively annotated examples.
000827106 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 28, 2018).
000827106 650_0 $$aEcology$$xMathematics$$xData processing.
000827106 650_0 $$aEcology$$xStatistical methods$$xData processing.
000827106 650_0 $$aR (Computer program language)
000827106 650_0 $$aMultivariate analysis.
000827106 650_0 $$aEcology$$xStatistical methods.
000827106 650_0 $$aEcology$$xData processing.
000827106 7001_ $$aGillet, François,$$eauthor.
000827106 7001_ $$aLegen, Pierre,$$eauthor.
000827106 77608 $$iPrint version: $$z9783319714035
000827106 830_0 $$aUse R.
000827106 852__ $$bebk
000827106 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-71404-2$$zOnline Access$$91397441.1
000827106 909CO $$ooai:library.usi.edu:827106$$pGLOBAL_SET
000827106 980__ $$aEBOOK
000827106 980__ $$aBIB
000827106 982__ $$aEbook
000827106 983__ $$aOnline
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