ALBERT

Note: Contents: About the author. - About the technical reviewer. - Acknowledgments. - Introduction. - Chapter 1: Getting R and getting started. - Chapter 2: Programming in R. - Chapter 3: Writing reusable functions. - Chapter 4: Summary statistics. - Chapter 5: Creating Tables and graphs. - Chapter 6: Discrete probability distributions. - Chapter 7: Computing normal probabilities. - Chapter 8: Creating confidence intervals. - Chapter 9: Performing t tests. - Chapter 10: One-way analysis of variance. - Chapter 11: Advanced analysis of variance. - Chapter 12: Correlation and regression. - Chapter 13: Multiple regression. - Chapter 14: Logistic regression. - Chapter 15: Chi-square tests. - Chapter 16: Nonparametric tests. - Chapter 17: Using R for simulation. - Chapter 18: The 'new' statistics: resampling and bootstrapping. - Chapter 19: Making an R package. - Chapter 20: The R commander package. - Index

Note: Zugl.: Stockholm, Univ., Diss., 2013

Note: 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.

Note: Contents: Series foreword. - Preface. - Select bibliography. - The authors. - 1 Observed flow in the Earth's midlalitudes. - 1.1 Vertical structure. - 1.2 Horizontal structure. - 1.3 Transient activity. - 1.4 Scales of motion. - 1.5 The Norwegian frontal model of cyclones. - Theme 1 Fluid dynamics of the midlatitude atmosphere. - 2 Fluid dynamics in an inertial frame of reference. - 2.1 Definition of fluid. - 2.2 Flow variables and the continuum hypothesis. - 2.3 Kinematics: characterizing fluid flow. - 2.4 Governing physical principles. - 2.5 Lagrangian and Eulerian perspectives. - 2.6 Mass conservation equation. - 2.7 First Law of Thermodynamics. - 2.8 Newton's Second Law of Motion. - 2.9 Bernoulli's Theorem. - 2.10 Heating and water vapour. - 3 Rotating frames of reference. - 3.1 Vectors in a rotating frame of reference. - 3.2 Velocity and Acceleration. - 3.3 The momentum equation in a rotating frame. - 3.4 The centrifugal pseudo-force. - 3.5 The Coriolis pseudo-force. - 3.6 The Taylor-Proudman theorem. - 4 The spherical Earth. - 4.1 Spherical polar coordinates. - 4.2 Scalar equations. - 4.3 The momentum equations. - 4.4 Energy and angular momentum.- 4.5 The shallow atmosphere approximation. - 4.6 The beta effect and the spherical Earth. - 5 Scale analysis and its applications. - 5.1 Principles of scaling methods. - 5.2 The use of a reference atmosphere. - 5.3 The horizontal momentum equations. - 5.4 Natural coordinates, geostrophic and gradient wind balance. - 5.5 Vertical motion. - 5.6 The vertical momentum equation. - 5.7 The mass continuity equation. - 5.8 The thermodynamic energy equation. - 5.9 Scalings for Rossby numbers that are not small. - 6 Alternative vertical coordinates. - 6.1 A general vertical coordinate. - 6.2 Isobaric coordinates. - 6.3 Other pressure-based vertical coordinates. - 6.4 Isentropic coordinates. - 7 Variations of density and the basic equations. - 7.1 Boussinesq approximation. - 7.2 Anelastic approximation. - 7.3 Stratification and gravity waves. - 7.4 Balance, gravity waves and Richardson number. - 7.5 Summary of the basic equation sets. - 7.6 The energy of atmospheric motions. - Theme 2 Rotation in the atmosphere. - 8 Rotation in the atmosphere. - 8.1 The concept of vorticity. - 8.2 The vorticity equation. - 8.3 The vorticity equation for approximate sets of equations. - 8.4 The solenoidal term. - 8.5 The expansion/contraction term. - 8.6 The stretching and tilting terms. - 8.7 Friction and vorticity. - 8.8 The vorticity equation in alternative vertical coordinates. - 8.9 Circulation. - 9 Vorticity and the barotropic vorticity equation. - 9.1 The barotropic vorticity equation. - 9.2 Poisson's equation and vortex interactions. - 9.3 Flow over a shallow hill. - 9.4 Ekman pumping. - 9.5 Rossby waves and the beta plane. - 9.6 Rossby group velocity. - 9.7 Rossby ray tracing. - 9.8 Inflexion point instability. - 10 Potential vorticity. - 10.1 Potential vorticity. - 10.2 Alternative derivations of Ertel's theorem. - 10.3 The principle of invertibility. - 10.4 Shallow water equation potential vorticity. - 11 Turbulence and atmospheric flow. - 11.1 The Reynolds number . - 11.2 Three-dimensional flow at large Reynolds number. - 11.3 Two-dimensional flow at large Reynolds number. - 11.4 Vertical mixing in a stratified fluid. - 11.5 Reynolds stresses. - Theme 3 Balance in atmospheric flow. - 12 Quasi-geostrophic flows. - 12.1 Wind and temperature in balanced flows. - 12.2 The quasi-geostrophic approximation. - 12.3 Quasi-geostrophic potential vorticity. - 12.4 Ertel and quasi-geostrophic potential vorticities. - 13 The omega equation. - 13.1 Vorticity and thermal advection form. - 13.2 Sutcliffe Form. - 13.3 Q-vector form. - 13.4 Ageostrophic flow and the maintenance of balance. - 13.5 Balance and initialization. - 14 Linear theories of baroclinic instability. - 14.1 Qualitative discussion. - 14.2 Stability analysis of a zonal flow. - 14.3 Rossby wave interpretation of the stability conditions. - 14.4 The Eady model. - 14.5 The Charney and other quasi-geostrophic models. - 14.6 More realistic basic states. - 14.7 Initial value problem. - 15 Frontogenesis. - 15.1 Frontal scales. - 15.2 Ageostrophic circulation. - 15.3 Description of frontal collapse. - 15.4 The semi-geostrophic Eady model. - 15.5 The confluence model. - 15.6 Upper-level frontogenesis. - 16 The nonlinear development of baroclinic waves. - 16.1 The nonlinear domain. - 16.2 Semi-geostrophic baroclinic waves. - 16.3 Nonlinear baroclinic waves on realistic jetson the sphere. - 16.4 Eddy transports and zonal mean flow changes. - 16.5 Energetics of baroclinic waves. - 17 The potential vorticity perspective. - 17.1 Setting the scene. - 17.2 Potential vorticity and vertical velocity. - 17.3 Life cycles of some baroclinic waves. - 17.4 Alternative perspectives. - 17.5 Midlatitude blocking. - 17.6 Frictional and heating effects. - 18 Rossby wave propagation and potential vorticity mixing. - 18.1 Rossby wave propagation. - 18.2 Propagation of Rossby waves into the stratosphere. - 18.3 Propagation through a slowly varying medium. - 18.4 The Eliassen-Palm flux and group velocity. - 18.5 Baroclinic life cycles and Rossby waves. - 18.6 Variations of amplitude. - 18.7 Rossby waves and potential vorticity steps. - 18.8 Potential vorticity steps and the Rhines scale. - Appendices. - Appendix A: Notation. - Appendix B: Revision of vectors and vector calculus. - B.1 Vectors and their algebra. - B.2 Products of vectors. - B.3 Scalar fields and the grad operator. - B.4 The divergence and curl operators. - B.5 Gauss' and Stokes' theorems. - B.6 Some useful vector identities. - Index.

Note: Part I: Introduction to Data Mining Chapter 1 - What's It All About? Chapter 2 - Input: Concepts, Instances, and Attributes Chapter 3 - Output: Knowledge Representation Chapter 4 - Algorithms: The Basic Methods Chapter 5 - Credibility: Evaluating What's Been Learned Part II: Advanced Data Mining Chapter 6 - Implementations: Real Machine Learning Schemes Chapter 7 - Data Transformations Chapter 8 - Ensemble Learning Chapter 9 - Moving on: Applications and Beyond Part III: The Weka Data Mining Workbench Chapter 10 - Introduction to Weka Chapter 11 - The Explorer Chapter 12 - The Knowledge Flow Interface Chapter 13 - The Experimenter Chapter 14 - The Command-Line Interface Chapter 15 - Embedded Machine Learning Chapter 16 - Writing New Learning Schemes Chapter 17 - Tutorial Exercises for the Weka Explorer

Note: Foreword; Acknowledgements; List of participating members of the Expert Group; CONTENTS; List of tables; List of figures; Executive summary; Part I: Methodology and Data on Levelised Costs for Generating Electricity; Chapter 1 Introduction and context; Chapter 2 Methodology, conventions and key assumptions; Chapter 3 Technology overview; Chapter 4 Country-by-country data on electricity generating costs for different technologies; Part 2: Sensitivity analyses and boundary issues; Chapter 5 Median case; Chapter 6 Sensitivity analyses. , Chapter 7 System integration aspects of variable renewable power generationChapter 8 Financing issues; Chapter 9 Levelised costs and the working of actual power markets; Chapter 10 Carbon capture and storage; Chapter 11 Synthesis report on other studies of the levelised cost of electricity; ANNEXES; Annex 1 Issues concerning data from non-OECD countries and assumptions forthe electricity generating cost calculations; Annex 2 List of abbreviations;.