Spatial Data Analysis in Ecology and Agriculture using R [2nd ed.]

Description

Contents......Page 3 Introduction......Page 12 Analysis of Spatial Data......Page 16 The Data Sets Analyzed in This Book......Page 20 Further Reading......Page 27 Introduction......Page 29 R Basics......Page 31 Programming Concepts......Page 36 Handling Data in R......Page 40 Writing Functions in R......Page 53 Graphics in R......Page 56 Continuing on from Here with R......Page 71 Exercises......Page 72 Introduction......Page 76 Components of a Spatial Random Process......Page 77 Monte Carlo Simulation......Page 86 A Review of Hypothesis and Significance Testing......Page 88 Modeling Spatial Autocorrelation......Page 93 Application to Field Data......Page 107 Exercises......Page 112 Preliminary Considerations......Page 114 Join–Count Statistics......Page 119 Moran’s I and Geary’s c......Page 123 Measures of Autocorrelation Structure......Page 126 Measuring Autocorrelation of Spatially Continuous Data......Page 136 Exercises......Page 142 Introduction......Page 144 Preliminary Considerations......Page 147 Developing the Sampling Patterns......Page 151 Methods for Variogram Estimation......Page 163 Estimating the Sample Size......Page 166 Sampling for Thematic Mapping......Page 167 Design-Based and Model-Based Sampling......Page 168 Exercises......Page 173 Introduction......Page 175 Quality of Attribute Data......Page 176 Spatial Interpolation Procedures......Page 183 Spatial Rectification and Alignment of Data......Page 196 Further Reading......Page 204 Exercises......Page 205 Introduction......Page 207 Data Set 1......Page 209 Data Set 2......Page 222 Data Set 3......Page 237 Data Set 4......Page 248 Exercises......Page 259 Multiple Linear Regression......Page 262 Building a Multiple Regression Model for Field 4.1......Page 277 Generalized Linear Models......Page 288 Further Reading......Page 310 Exercises......Page 311 The Generalized Additive Model......Page 313 Classification and Regression Trees (a.k.a. Recursive Partitioning)......Page 323 Random Forest......Page 345 Exercises......Page 351 Introduction......Page 353 Bootstrap Estimation of the Standard Error......Page 357 Bootstrapping Time Series Data......Page 361 Bootstrapping Spatial Data......Page 368 Application to the EM38 Data......Page 374 Further Reading......Page 377 Exercises......Page 378 Introduction......Page 379 Estimating and Testing the Correlation Coefficient......Page 382 Contingency Tables......Page 392 The Mantel and Partial Mantel Statistics......Page 404 The Modifiable Areal Unit Problem and the Ecological Fallacy......Page 410 Exercises......Page 416 Introduction......Page 418 Basic Properties of the Mixed Model......Page 422 Application to Data Set 3......Page 424 Incorporating Spatial Autocorrelation......Page 427 Generalized Least Squares......Page 434 Spatial Logistic Regression......Page 436 Further Reading......Page 448 Exercises......Page 449 Introduction......Page 450 Detecting Spatial Autocorrelation in a Regression Model......Page 455 Models for Spatial Processes......Page 457 Determining the Appropriate Regression Model......Page 460 Fitting the Spatial Lag and Spatial Error Models......Page 463 The Conditional Autoregressive Model......Page 465 Application of Simultaneous Autoregressive and Conditional Autoregressive Models to Field Data......Page 467 Exercises......Page 471 Introduction......Page 472 Markov Chain Monte Carlo Methods......Page 476 Introduction to WinBUGS......Page 483 Hierarchical Models......Page 497 Incorporation of Spatial Effects......Page 503 Comparison of the Methods......Page 514 Further Reading......Page 515 Exercises......Page 516 Spatiotemporal Data Interpolation......Page 517 Spatiotemporal Process Models......Page 529 Finite State and Time Models......Page 533 Bayesian Spatiotemporal Analysis......Page 547 Further Reading......Page 554 Exercises......Page 555 Introduction......Page 556 Classical Analysis of Variance......Page 557 The Comparison of Methods......Page 562 Pseudoreplicated Data and the Effective Sample Size......Page 569 Exercises......Page 575 Data Set 1......Page 576 Data Set 2......Page 581 Data Set 3......Page 586 Data Set 4......Page 589 Conclusions......Page 593 Matrix Theory & Linear Algebra......Page 595 Linear Regression......Page 602 Method of Lagrange Multipliers......Page 611 Maximum Likelihood Method......Page 612 Change of Variables of Probability Density......Page 618 Data Set 1......Page 620 Data Set 2......Page 624 Data Set 3......Page 625 Data Set 4......Page 626 R Thesaurus......Page 628 Refs......Page 636 Index......Page 658