Keywords:
Environmental sciences--Statistical methods.
;
Electronic books.
Description / Table of Contents:
Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
Type of Medium:
Online Resource
Pages:
1 online resource (560 pages)
Edition:
2nd ed.
ISBN:
9781498728737
Series Statement:
Chapman and Hall/CRC Applied Environmental Statistics Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=4732231
DDC:
550.2855133
Language:
English
Note:
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- List of Figures -- List of Tables -- I: Basic Concepts -- 1: Introduction -- 1.1 Tool for Inductive Reasoning -- 1.2 The Everglades Example -- 1.2.1 Statistical Issues -- 1.3 Effects of Urbanization on Stream Ecosystems -- 1.3.1 Statistical Issues -- 1.4 PCB in Fish from Lake Michigan -- 1.4.1 Statistical Issues -- 1.5 Measuring Harmful Algal Bloom Toxin -- 1.6 Bibliography Notes -- 1.7 Exercise -- 2: A Crash Course on R -- 2.1 What is R? -- 2.2 Getting Started with R -- 2.2.1 R Commands and Scripts -- 2.2.2 R Packages -- 2.2.3 R Working Directory -- 2.2.4 Data Types -- 2.2.5 R Functions -- 2.3 Getting Data into R -- 2.3.1 Functions for Creating Data -- 2.3.2 A Simulation Example -- 2.4 Data Preparation -- 2.4.1 Data Cleaning -- 2.4.1.1 Missing Values -- 2.4.2 Subsetting and Combining Data -- 2.4.3 Data Transformation -- 2.4.4 Data Aggregation and Reshaping -- 2.4.5 Dates -- 2.5 Exercises -- 3: Statistical Assumptions -- 3.1 The Normality Assumption -- 3.2 The Independence Assumption -- 3.3 The Constant Variance Assumption -- 3.4 Exploratory Data Analysis -- 3.4.1 Graphs for Displaying Distributions -- 3.4.2 Graphs for Comparing Distributions -- 3.4.3 Graphs for Exploring Dependency among Variables -- 3.5 From Graphs to Statistical Thinking -- 3.6 Bibliography Notes -- 3.7 Exercises -- 4: Statistical Inference -- 4.1 Introduction -- 4.2 Estimation of Population Mean and Confidence Interval -- 4.2.1 Bootstrap Method for Estimating Standard Error -- 4.3 Hypothesis Testing -- 4.3.1 t-Test -- 4.3.2 Two-Sided Alternatives -- 4.3.3 Hypothesis Testing Using the Confidence Interval -- 4.4 A General Procedure -- 4.5 Nonparametric Methods for Hypothesis Testing -- 4.5.1 Rank Transformation -- 4.5.2 Wilcoxon Signed Rank Test -- 4.5.3 Wilcoxon Rank Sum Test.
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4.5.4 A Comment on Distribution-Free Methods -- 4.6 Significance Level α, Power 1 - β, and p-Value -- 4.7 One-Way Analysis of Variance -- 4.7.1 Analysis of Variance -- 4.7.2 Statistical Inference -- 4.7.3 Multiple Comparisons -- 4.8 Examples -- 4.8.1 The Everglades Example -- 4.8.2 Kemp's Ridley Turtles -- 4.8.3 Assessing Water Quality Standard Compliance -- 4.8.4 Interaction between Red Mangrove and Sponges -- 4.9 Bibliography Notes -- 4.10 Exercises -- II: Statistical Modeling -- 5: Linear Models -- 5.1 Introduction -- 5.2 From t-test to Linear Models -- 5.3 Simple and Multiple Linear Regression Models -- 5.3.1 The Least Squares -- 5.3.2 Regression with One Predictor -- 5.3.3 Multiple Regression -- 5.3.4 Interaction -- 5.3.5 Residuals and Model Assessment -- 5.3.6 Categorical Predictors -- 5.3.7 Collinearity and the Finnish Lakes Example -- 5.4 General Considerations in Building a Predictive Model -- 5.5 Uncertainty in Model Predictions -- 5.5.1 Example: Uncertainty in Water Quality Measurements -- 5.6 Two-Way ANOVA -- 5.6.1 ANOVA as a Linear Model -- 5.6.2 More Than One Categorical Predictor -- 5.6.3 Interaction -- 5.7 Bibliography Notes -- 5.8 Exercises -- 6: Nonlinear Models -- 6.1 Nonlinear Regression -- 6.1.1 Piecewise Linear Models -- 6.1.2 Example: U.S. Lilac First Bloom Dates -- 6.1.3 Selecting Starting Values -- 6.2 Smoothing -- 6.2.1 Scatter Plot Smoothing -- 6.2.2 Fitting a Local Regression Model -- 6.3 Smoothing and Additive Models -- 6.3.1 Additive Models -- 6.3.2 Fitting an Additive Model -- 6.3.3 Example: The North American Wetlands Database -- 6.3.4 Discussion: The Role of Nonparametric Regression Models in Science -- 6.3.5 Seasonal Decomposition of Time Series -- 6.3.5.1 The Neuse River Example -- 6.4 Bibliographic Notes -- 6.5 Exercises -- 7: Classification and Regression Tree -- 7.1 The Willamette River Example.
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7.2 Statistical Methods -- 7.2.1 Growing and Pruning a Regression Tree -- 7.2.2 Growing and Pruning a Classification Tree -- 7.2.3 Plotting Options -- 7.3 Comments -- 7.3.1 CART as a Model Building Tool -- 7.3.2 Deviance and Probabilistic Assumptions -- 7.3.3 CART and Ecological Threshold -- 7.4 Bibliography Notes -- 7.5 Exercises -- 8: Generalized Linear Model -- 8.1 Logistic Regression -- 8.1.1 Example: Evaluating the Effectiveness of UV as a Drinking Water Disinfectant -- 8.1.2 Statistical Issues -- 8.1.3 Fitting the Model in R -- 8.2 Model Interpretation -- 8.2.1 Logit Transformation -- 8.2.2 Intercept -- 8.2.3 Slope -- 8.2.4 Additional Predictors -- 8.2.5 Interaction -- 8.2.6 Comments on the Crypto Example -- 8.3 Diagnostics -- 8.3.1 Binned Residuals Plot -- 8.3.2 Overdispersion -- 8.3.3 Seed Predation by Rodents: A Second Example of Logistic Regression -- 8.4 Poisson Regression Model -- 8.4.1 Arsenic Data from Southwestern Taiwan -- 8.4.2 Poisson Regression -- 8.4.3 Exposure and Offset -- 8.4.4 Overdispersion -- 8.4.5 Interactions -- 8.4.6 Negative Binomial -- 8.5 Multinomial Regression -- 8.5.1 Fitting a Multinomial Regression Model in R -- 8.5.2 Model Evaluation -- 8.6 The Poisson-Multinomial Connection -- 8.7 Generalized Additive Models -- 8.7.1 Example: Whales in the Western Antarctic Peninsula -- 8.7.1.1 The Data -- 8.7.1.2 Variable Selection Using CART -- 8.7.1.3 Fitting GAM -- 8.7.1.4 Summary -- 8.8 Bibliography Notes -- 8.9 Exercises -- III: Advanced Statistical Modeling -- 9: Simulation for Model Checking and Statistical Inference -- 9.1 Simulation -- 9.2 Summarizing Regression Models Using Simulation -- 9.2.1 An Introductory Example -- 9.2.2 Summarizing a Linear Regression Model -- 9.2.2.1 Re-transformation Bias -- 9.2.3 Simulation for Model Evaluation -- 9.2.4 Predictive Uncertainty -- 9.3 Simulation Based on Re-sampling.
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9.3.1 Bootstrap Aggregation -- 9.3.2 Example: Confidence Interval of the CART-Based Threshold -- 9.4 Bibliography Notes -- 9.5 Exercises -- 10: Multilevel Regression -- 10.1 From Stein's Paradox to Multilevel Models -- 10.2 Multilevel Structure and Exchangeability -- 10.3 Multilevel ANOVA -- 10.3.1 Intertidal Seaweed Grazers -- 10.3.2 Background N2O Emission from Agriculture Fields -- 10.3.3 When to Use the Multilevel Model? -- 10.4 Multilevel Linear Regression -- 10.4.1 Nonnested Groups -- 10.4.2 Multiple Regression Problems -- 10.4.3 The ELISA Example-An Unintended Multilevel Modeling Problem -- 10.5 Nonlinear Multilevel Models -- 10.6 Generalized Multilevel Models -- 10.6.1 Exploited Plant Monitoring-Galax -- 10.6.1.1 A Multilevel Poisson Model -- 10.6.1.2 A Multilevel Logistic Regression Model -- 10.6.2 Cryptosporidium in U.S. Drinking Water-A Poisson Regression Example -- 10.6.3 Model Checking Using Simulation -- 10.7 Concluding Remarks -- 10.8 Bibliography Notes -- 10.9 Exercises -- 11: Evaluating Models Based on Statistical Signicance Testing -- 11.1 Introduction -- 11.2 Evaluating TITAN -- 11.2.1 A Brief Description of TITAN -- 11.2.2 Hypothesis Testing in TITAN -- 11.2.3 Type I Error Probability -- 11.2.4 Statistical Power -- 11.2.5 Bootstrapping -- 11.2.6 Community Threshold -- 11.2.7 Conclusions -- 11.3 Exercises -- Bibliography -- Index.
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