Spatial Data Analysis in Ecology and Agriculture Using R

 Content: Working with Spatial Data Analysis of Spatial Data Data Sets Analyzed in This Book  R Programming Environment R Basics Programming Concepts Handling Data in R Writing Functions in R Graphics in R Other Software Packages  Statistical Properties of Spatially Autocorrelated Data Components of a Spatial Random Process Monte Carlo Simulation Review of Hypothesis and Significance Testing Modeling Spatial Autocorrelation Application to Field Data  Measures of Spatial Autocorrelation Preliminary Considerations Join-Count Statistics Moran\'s I and Geary\'s c Measures of Autocorrelation Structure Measuring Autocorrelation of Spatially Continuous Data  Sampling and Data Collection Preliminary Considerations Developing the Sampling Patterns Methods for Variogram Estimation Estimating the Sample Size Sampling for Thematic Mapping Design-Based and Model-Based Sampling  Preparing Spatial Data for Analysis Quality of Attribute Data Spatial Interpolation Procedures Spatial Rectification and Alignment of Data  Preliminary Exploration of Spatial Data Data Set 1 Data Set 2 Data Set 3 Data Set 4  Multivariate Methods for Spatial Data Exploration Principal Components Analysis Classification and Regression Trees (aka Recursive Partitioning) Random Forest  Spatial Data Exploration via Multiple Regression Multiple Linear Regression Building a Multiple Regression Model for Field 4.1 Generalized Linear Models  Variance Estimation, the Effective Sample Size, and the Bootstrap Bootstrap Estimation of the Standard Error Bootstrapping Time Series Data Bootstrapping Spatial Data Application to the EM38 Data  Measures of Bivariate Association between Two Spatial Variables Estimating and Testing the Correlation Coefficient Contingency Tables Mantel and Partial Mantel Statistics Modifiable Areal Unit Problem and Ecological Fallacy  Mixed Model Basic Properties of the Mixed Model Application to Data Set 3 Incorporating Spatial Autocorrelation Generalized Least Squares Spatial Logistic Regression  Regression Models for Spatially Autocorrelated Data Detecting Spatial Autocorrelation in a Regression Model Models for Spatial Processes Determining the Appropriate Regression Model Fitting the Spatial Lag and Spatial Error Models Conditional Autoregressive Model Application of SAR and CAR Models to Field Data Autologistic Model for Binary Data  Bayesian Analysis of Spatially Autocorrelated Data Markov Chain Monte Carlo Methods Introduction to WinBUGS Hierarchical Models Incorporation of Spatial Effects  Analysis of Spatiotemporal Data Spatiotemporal Cluster Analysis Factors Underlying Spatiotemporal Yield Clusters Bayesian Spatiotemporal Analysis Other Approaches to Spatiotemporal Modeling  Analysis of Data from Controlled Experiments Classical Analysis of Variance Comparison of Methods Pseudoreplicated Data and the Effective Sample Size  Assembling Conclusions Data Set 1 Data Set 2 Data Set 3 Data Set 4 Conclusions  Appendices Review of Mathematical Concepts The Data Sets An R Thesaurus References  Index