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  • 1
    UID:
    b3kat_BV046814426
    Format: xvi, 365 Seiten , Illustrationen, Diagramme
    ISBN: 9781108480383 , 9781108727341
    Content: "Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets and thoroughly explained, step-by-step R code demonstrating the analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience of interested readers, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses"--
    Note: Includes index
    Language: English
    Subjects: Computer Science , Biology
    RVK:
    RVK:
    RVK:
    Keywords: R ; Biologie ; Biostatistik ; R
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Cambridge [u.a.] : Cambridge University Press
    UID:
    b3kat_BV014740839
    Format: XI, 269 S. , Ill., graph. Darst.
    Edition: 1. publ.
    ISBN: 052181409X , 0521891086
    Note: Includes bibliographical references (p. 262-266) and index
    Language: English
    Subjects: Geography , Biology
    RVK:
    RVK:
    Keywords: Multivariate Analyse ; Ökologie
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Book
    Book
    Cambridge [u.a.] : Cambridge Univ. Press
    UID:
    kobvindex_GFZ122604
    Format: XII, 362 S. : Ill., graph. Darst.
    Edition: 2. ed.
    ISBN: 9781107694408 , 1-107-69440-X
    Content: This revised and updated edition focuses on constrained ordination (RDA, CCA), variation partitioning and the use of permutation tests of statistical hypotheses about multivariate data. Both classification and modern regression methods (GLM, GAM, loess) are reviewes and species functional traits and spatial structures are analysed. Nine case studies of varying difficulty help to illustrate the suggestes analytical methods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative approaches, and are supported by data sets and project files available from the book website: http://regent.prf.jcu.cz/maed2/. Written primarily for community ecologists needing to analyse data resulting from field observations and experiments, this book is a valuable resource for students and researchers dealing with both simple and complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulation.
    Note: MAB0014.001: AWI S2-14-0042 , MAB0014.002: M 15.0198 , Contents: Preface. - 1 Introduction and datatypes. - 1.1 Why ordination?. - 1.2 Datatypes. - 1.3 Data transformation and standardisation. - 1.4 Missing values. - 1.5 Types of analyses. - 2 Using Canoco 5. - 2.1 Philosophy of Canoco 5. - 2.2 Data import and editing. - 2.3 Defining analyses. - 2.4 Visualising results. - 2.5 Beware, CANOCO 4.x users!. - 3 Experimental design. - 3.1 Completely randomised design. - 3.2 Randomised complete blocks. - 3.3 Latin square design. - 3.4 Pseudo replicates. - 3.5 Combining more than one factor. - 3.6 Following the development of objects in time: repeated observations. - 3.7 Experimental and observational data. - 4 Basics of gradient analysis. - 4.1 Techniques of gradient analysis. - 4.2 Models of response to gradients. - 4.3 Estimating species optima by weighted averaging. - 4.4 Calibration. - 4.5 Unconstrained ordination. - 4.6 Constrained ordination. - 4.7 Basic ordination techniques. - 4.8 Ordination axes as optimal predictors. - 4.9 Ordination diagrams. - 4.10 Two approaches. - 4.11 Testing significance of the relation with explanatory variables. - 4.12 Monte Carlo permutation tests for the significance of regression. - 4.13 Relating two biotic communities. - 4.14 Community composition as a cause: using reverse analysis. - 5.1 Permutation tests: the philosophy. - 5.2 Pseudo-F statistics and significance. - 5.3 Testing individual constrained axes. - 5.4 Tests with spatial or temporal constraints. - 5.5 Tests with hierarchical constraints. - 5.6 Simple versus conditional effects and stepwises election. - 5.7 Variation partitioning. - 5.8 Significance adjustment for multiple tests. - 6 Similarity measures and distance-based methods. - 6.1 Similarity measures for presence-absence data. - 6.2 Similarity measures for quantitative data. - 6.3 Similarity of cases versus similarity of communities. - 6.4 Similarity between species in trait values. - 6.5 Principal coordinates analysis. - 6.6 Constrained principal coordinates analysis (db-RDA). - 6.7 Non-metric multidimensional scaling. - 6.8 Mantel test. - 7.1 Example data set properties. - 7.2 Non-hierarchical classification (K-means clustering). - 7.3 Hierarchical classification. - 7.4 TWINSPAN. - 8 Regression methods. - 8.1 Regression models in general. - 8.2 General linear model: terms. - 8.3 Generalized linear models (GLM). - 8.4 Loess smoother. - 8.5 Generalized additive models (GAM). - 8.6 Mixed-effect models (LMM, GLMM and GAMM). - 8.7 Classification and regression trees (CART). - 8.8 Modelling species response curves with Canoco. - 9 Interpreting community composition with functional traits. - 9.1 Required data. - 9.2 Two approaches in traits - environment studies. - 9.3 Community-based approach. - 9.4 Species-based approach. - 10 Advanced use of ordination. - 10.1 Principal response curves (PRC). - 10.2 Separating spatial variation. - 10.3 Linear discriminant analysis. - 10.4 Hierarchical analysis of community variation. - 10.5 Partitioning diversity indices into alpha and beta components. - 10.6 Predicting community composition. - 11 Visualising multivariate data. - 11.1 Reading ordination diagrams of linear methods. - 11.2 Reading ordination diagrams of unimodal methods. - 11.3 Attribute plots. - 11.4 Visualising classification, groups, and sequences. - 11.5 T-value biplot. - 12 Case study 1: Variation in forest bird assemblages. - 12.1 Unconstrained ordination: portraying variation in bird community. - 12.2 Simple constrained ordination: the effect of altitude on bird community. - 12.3 Partial constrained ordination: additional effect of other habitat characteristics. - 12.4 Separating and testing alpha and beta diversity. - 13 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows. - 13.1 Unconstrained ordination. - 13.2 Constrained ordination. - 13.3 Classification. - 13.4 Suggestions for additional analyses. - 13.5 Comparing two communities. - 14 Case study 3: Separating the effects of explanatory variables. - 14.1 Introduction. - 14.2 Data. - 14.3 Changes in species richness and composition. - 14.4 Changes in species traits. - 15 Case study 4: Evaluation of experiments in randomised complete blocks. - 15.1 Introduction. - 15.2 Data. - 15.3 Analysis. - 15.4 Calculating ANOVA using constrained ordination. - 16 Case study 5: Analysis of repeated observations of species composition from a factorial experiment. - 16.1 Introduction. - 16.2 Experimental design. - 16.3 Data coding and use. - 16.4 Univariate analyses. - 16.5 Constrained ordinations. - 16.6 Principal response curves. - 16.7 Temporal changes across treatments. - 16.8 Changes in composition of functional traits. - 17 Case study 6: Hierarchical analysis of crayfish community variation. - 17.1 Data and design. - 17.2 Differences among sampling locations. - 17.3 Hierarchical decomposition of community variation. - 18 Case study 7: Analysis of taxonomic data with discriminant analysis and distance-based ordination. - 18.1 Data. - 18.2 Summarising morphological data with PCA. - 18.3 Linear discriminant analysis of morphological data. - 18.4 Principal coordinates analysis of AFLP data. - 18.5 Testing taxon differences in AFLP data using db-RDA. - 18.6 Taking populations into account. - 19 Case study 8: Separating effects of space and environment on oribatid community with PCNM. - 19.1 Ignoring the space. - 19.2 Detecting spatial trends. - 19.3 All-scale spatial variation of community and environment. - 19.4 Variation partitioning with spatial predictors. - 19.5 Visualising spatial variation. - 20 Case study 9: Performing linear regression with redundancy analysis. - 20.1 Data. - 20.2 Linear regression using program R. - 20.3 Linear regression with redundancy analysis. - 20.4 Fitting generalized linear models in Canoco. - Appendix A Glossary. - Appendix B Sample data sets and projects. - Appendix C Access to Canoco and overview of other software. - Appendix D Working with R. - References. - Index to useful tasks in Canoco 5. - Subject index.
    Language: English
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  • 4
    Book
    Book
    Cambridge [u.a.] : Cambridge Univ. Press
    UID:
    kobvindex_GFZ354136712
    Format: XI, 269 S. , graph. Darst. , 26 cm
    Edition: Transferred to digital printing 2009
    ISBN: 052181409X (hb) , 0521891086 (pb.)
    Note: Contents: Preface. - 1. Introduction and data manipulation. - 1.1. Why ordination?. - 1.2. Terminology. - 1.3. Types of analyses. - 1.4. Response variables. - 1.5. Explanatory variables. - 1.6. Handling missing values in data. - 1.7. Importing data from spreadsheets - WCanoImp program. - 1.8. Transformation of species data. - 1.9. Transformation of explanatory variables. - 2. Experimental design. - 2.1. Completely randomized design. - 2.2. Randomized complete blocks. - 2.3. Latin square design. - 2.4. Most frequent errors - pseudoreplications. - 2.5. Combining more than one factor. - 2.6. Following the development of objects in time - repeated observations. - 2.7. Experimental and observational data. - 3. Basics of gradient analysis. - 3.1. Techniques of gradient analysis. - 3.2. Models of species response to environmental gradients. - 3.3. Estimating species optima by the weighted averaging method. - 3.4. Calibration. - 3.5. Ordination. - 3.6. Constrained ordination. - 3.7. Basic ordination techniques. - 3.8. Ordination diagrams. - 3.9. Two approaches. - 3.10. Testing significance of the relation with environmental variables. - 3.11. Monte Carlo permutation tests for the significance of regression. - 4. Using the Canoco for Windows 4.5 package. - 4.1. Overview of the package. - 4.2. Typical flow-chart of data analysis with Canoco for Windows. - 4.3. Deciding on the ordination method: unimodal or linear?. - 4.4. PCA or RDA ordination: centring and standardizing. - 4.5. DCA ordination: detrending. - 4.6. Scaling of ordination scores. - 4.7. Running CanoDraw for Windows 4.0. - 4.8. New analyses providing new views of our data sets. - 5. Constrained ordination and permutation tests. - 5.1. Linear multiple regression model. - 5.2. Constrained ordination model. - 5.3. RDA: constrained PCA. - 5.4. Monte Carlo permutation test: an introduction. - 5.5. Null hypothesis model. - 5.6. Test statistics. - 5.7. Spatial and temporal constraints. - 5.8. Split-plot constraints. - 5.9. Stepwise selection of the model. - 5.10. Variance partitioning procedure. - 6. Similarity measures. - 6.1. Similarity measures for presence-absence data. - 6.2. Similarity measures for quantitative data. - 6.3. Similarity of samples versus similarity of communities. - 6.4. Principal coordinates analysis. - 6.5. Non-metric multidimensional scaling. - 6.6. Constrained principal coordinates analysis (db-RDA). - 6.7. Mantel test. - 7. Classification methods. - 7.1. Sample data set. - 7.2. Non-hierarchical classification (K-means clustering). - 7.3. Hierarchical classifications. - 7.4. TWINSPAN. - 8. Regression methods . - 8.1. Regression models in general. - 8.2. General linear model: terms. - 8.3. Generalized linear models (GLM). - 8.4. Loess smoother. - 8.5. Generalized additive models (GAM). - 8.6. Classification and regression trees. - 8.7. Modelling species response curves with CanoDraw. - 9. Advanced use of ordination. - 9.1. Testing the significance of individual constrained ordination axes. - 9.2. Hierarchical analysis of community variation. - 9.3. Principal response curves (PRC) method. - 9.4. Linear discriminant analysis. - 10. Visualizing multivariate data. - 10.1. What we can infer from ordination diagrams: linear methods. - 10.2. What we can infer from ordination diagrams: unimodal methods. - 10.3. Visualizing ordination results with statistical models. - 10.4. Ordination diagnostics. - 10.5. t-value biplot interpretation. - 11. Case study 1: Variation in forest bird assemblages. - 11.1. Data manipulation. - 11.2. Deciding between linear and unimodal ordination. - 11.3. Indirect analysis: portraying variation in bird community. - 11.4. Direct gradient analysis: effect of altitude. - 11.5.Direct gradient analysis: additional effect of other habitat characteristics. - 12. Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows. - 12.1. The unconstrained ordination. - 12.2. Constrained ordinations. - 12.3. Classification. - 12.4. Suggestions for additional analyses. - 13. Case study 3: Separating the effects of explanatory variables. - 13.1. Introduction. - 13.2. Data. - 13.3. Data analysis. - 14. Case study 4: Evaluation of experiments in randomized complete blocks. - 14.1. Introduction. - 14.2. Data. - 14.3. Data analysis. - 15. Case study 5: Analysis of repeated observations of species composition from a factorial experiment. - 15.1. Introduction. - 15.2. Experimental design. - 15.3. Sampling. - 15.4. Data analysis. - 15.5. Univariate analyses. - 15.6. Constrained ordinations. - 15.7. Further use of ordination results. - 15.8. Principal response curves. - 16. Case study 6: Hierarchical analysis of crayfish community variation. - 16.1. Data and design. - 16.2. Differences among sampling locations. - 16.3. Hierarchical decomposition'of community variation. - 17. Case study 7: Differentiating two species and their hybrids with discriminant analysis. - 17.1. Data. - 17.2. Stepwise selection of discriminating variables. - 17.3. Adjusting the discriminating variables. - 17.4. Displaying results. - Appendix A: Sample datasets and projects. - Appendix B: Vocabulary. - Appendix C: Overview of available software. - References. - Index.
    Language: English
    Subjects: Geography , Biology
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  • 5
    Book
    Book
    Cambridge [u.a.] : Cambridge Univ. Press
    UID:
    b3kat_BV023097056
    Format: XI, 269 S. , Ill., graph. Darst.
    Edition: 1. publ., 3. print.
    ISBN: 9780521814096 , 9780521891080
    Language: English
    Subjects: Geography , Biology
    RVK:
    RVK:
    Keywords: Multivariate Analyse ; Ökologie
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  • 6
    UID:
    kobvindex_IGB000023904
    In: Molecular Ecology. - 28(2019)4, S. 785-802
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  • 7
    Book
    Book
    Cambridge [u.a.] : Cambridge University Press
    UID:
    b3kat_BV025524775
    Format: XI, 269 S. , graph. Darst.
    ISBN: 052181409X , 9780521891080 , 0521891086
    Note: Includes bibliographical references (p. 262-266) and index
    Language: English
    Subjects: Geography , Biology , Mathematics
    RVK:
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Multivariate Analyse ; Ökologie
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  • 8
    UID:
    gbv_1859630057
    Format: 1 online resource (xvi, 364 pages) , digital, PDF file(s).
    ISBN: 9781108616041 , 9781108480383 , 9781108727341
    Content: Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.
    Note: Title from publisher's bibliographic system (viewed on 21 Sep 2020)
    Additional Edition: ISBN 9781108480383
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781108480383
    Language: English
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  • 9
    Online Resource
    Online Resource
    Cambridge : Cambridge University Press
    UID:
    gbv_859312380
    Format: 1 online resource (373 pages)
    Edition: 2nd ed
    ISBN: 9781107694408 , 9781107694408 , 9781139627061
    Content: This revised and updated edition focuses on constrained ordination (RDA, CCA), variation partitioning and the use of permutation tests of statistical hypotheses about multivariate data. Both classification and modern regression methods (GLM, GAM, loess) are reviewed and species functional traits and spatial structures analysed. Nine case studies of varying difficulty help to illustrate the suggested analytical methods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative approaches, and are supported by data sets and project files available from the book website: http://regent.prf.jcu.cz/maed2/. Written primarily for community ecologists needing to analyse data resulting from field observations and experiments, this book is a valuable resource to students and researchers dealing with both simple and complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulation
    Note: Title from publisher's bibliographic system (viewed on 12 Feb 2016)
    Additional Edition: ISBN 9781107694408
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781107694408
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 10
    Online Resource
    Online Resource
    Cambridge : Cambridge University Press
    UID:
    gbv_883404052
    Format: 1 Online-Ressource (xi, 269 pages) , digital, PDF file(s)
    ISBN: 9780511615146
    Content: This book is primarily written for ecologists needing to analyse data resulting from field observations and experiments. It will be particularly useful for students and researchers dealing with complex ecological problems, such as the variation of biotic communities with environmental conditions or the response of biotic communities to experimental manipulation. Following a simple introduction to ordination methods, the text focuses on constrained ordination methods (RDA, CCA) and the use of permutation tests on statistical hypotheses of multivariate data. An overview of classification methods, or modern regression methods (GLM, GAM, loess), is provided and guidance on the correct interpretation of ordination diagrams is given. Seven case studies of varying difficulty help to illustrate the suggested analytical methods, using the Canoco for Windows software. The case studies utilise both the descriptive and manipulative approaches, and they are supported by data sets and project files available from the book website
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015)
    Additional Edition: ISBN 9780521814096
    Additional Edition: ISBN 9780521891080
    Additional Edition: Print version ISBN 9780521814096
    Language: English
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