Biostatistics with R: An Introductory Guide for Field Biologists

Cover\nHalf-title\nReviews\nTitle page\nCopyright information\nContents\nPreface\nAcknowledgements\n1 Basic Statistical Terms, Sample Statistics\n	1.1 Cases, Variables and Data Types\n	1.2 Population and Random Sample\n	1.3 Sample Statistics\n		1.3.1 Characteristics of Position\n			1.3.1.1 Arithmetic Mean (Average)\n			1.3.1.2 Median and Other Quantiles\n			1.3.1.3 Mode\n			1.3.1.4 Geometric Mean\n		1.3.2 Characteristics of Variability (Spread)\n			1.3.2.1 Range\n			1.3.2.2 Variance\n			1.3.2.3 Standard Deviation\n			1.3.2.4 Coefficient of Variation\n			1.3.2.5 Interquartile Range\n	1.4 Precision of Mean Estimate, Standard Error of Mean\n	1.5 Graphical Summary of Individual Variables\n	1.6 Random Variables, Distribution, Distribution Function, Density Distribution\n		1.6.1 Probability Distributions and Distribution Functions of Discrete Random Variables\n		1.6.2 Distribution Functions and Probability Density of Continuous Random Variables\n	1.7 Example Data\n	1.8 How to Proceed in R\n		1.8.1 Graphical Summary of Quantitative Variables\n	1.9 Reporting Analyses\n		1.9.1 Methods\n		1.9.2 Results\n	1.10 Recommended Reading\n2 Testing Hypotheses, Goodness-of-Fit Test\n	2.1 Principles of Hypothesis Testing\n	2.2 Possible Errors in Statistical Tests of Hypotheses\n	2.3 Null Models with Parameters Estimated from the Data: Testing Hardy-Weinberg Equilibrium\n	2.4 Sample Size\n	2.5 Critical Values and Significance Level\n	2.6 Too Good to Be True\n	2.7 Bayesian Statistics: What is It?\n	2.8 The Dark Side of Significance Testing\n		2.8.1 Misinterpretation of p-Values\n		2.8.2 Too Much Attention on p-Values\n		2.8.3 Suggested Solutions\n	2.9 Example Data\n	2.10 How to Proceed in R\n	2.11 Reporting Analyses\n		2.11.1 Methods\n		2.11.2 Results\n	2.12 Recommended Reading\n3 Contingency Tables\n	3.1 Two-Way Contingency Tables\n		3.1.1 Use Case Examples\n		3.1.2 Analysing Two-Way Contingency Tables\n		3.1.3 Correction for Continuity\n		3.1.4 G Test\n		3.1.5 Two-By-Two Tables\n	3.2 Measures of Association Strength\n	3.3 Multidimensional Contingency Tables\n	3.4 Statistical and Causal Relationship\n	3.5 Visualising Contingency Tables\n	3.6 Example Data\n	3.7 How to Proceed in R\n	3.8 Reporting Analyses\n		3.8.1 Methods\n		3.8.2 Results\n	3.9 Recommended Reading\n4 Normal Distribution\n	4.1 Main Properties of a Normal Distribution\n	4.2 Skewness and Kurtosis\n	4.3 Standardised Normal Distribution\n	4.4 Verifying the Normality of a Data Distribution\n	4.5 Example Data\n	4.6 How to Proceed in R\n		4.6.1 Finding the Values of a Distribution Function and Quantiles\n		4.6.2 Testing for an Agreement with a Normal Distribution\n	4.7 Reporting Analyses\n		4.7.1 Methods\n		4.7.2 Results\n	4.8 Recommended Reading\n5 Student\'s t Distribution\n	5.1 Use Case Examples\n	5.2 t Distribution and its Relation to the Normal Distribution\n	5.3 Single Sample Test and Paired t Test\n	5.4 One-Sided Tests\n	5.5 Confidence Interval of the Mean\n	5.6 Test Assumptions\n	5.7 Reporting Data Variability and Mean Estimate Precision\n	5.8 How Large Should a Sample Size Be?\n	5.9 Example Data\n	5.10 How to Proceed in R\n		5.10.1 Single Sample and Paired t Test\n		5.10.2 Summarising Variability and Describing Mean Precision\n	5.11 Reporting Analyses\n		5.11.1 Methods\n		5.11.2 Results\n	5.12 Recommended Reading\n6 Comparing Two Samples\n	6.1 Use Case Examples\n	6.2 Testing for Differences in Variance\n	6.3 Comparing Means\n	6.4 Example Data\n	6.5 How to Proceed in R\n		6.5.1 F Test of Variance Equality\n		6.5.2 Two-Sample t Test of the Equality of Means\n	6.6 Reporting Analyses\n		6.6.1 Methods\n		6.6.2 Results\n	6.7 Recommended Reading\n7 Non-parametric Methods for Two Samples\n	7.1 Mann-Whitney Test\n	7.2 Wilcoxon Test for Paired Observations\n	7.3 Using Rank-Based Tests\n	7.4 Permutation Tests\n	7.5 Example Data\n	7.6 How to Proceed in R\n		7.6.1 Mann-Whitney Test\n		7.6.2 Wilcoxon Paired Data Test\n		7.6.3 Permutation Tests\n	7.7 Reporting Analyses\n		7.7.1 Methods\n		7.7.2 Results\n	7.8 Recommended Reading\n8 One-Way Analysis of Variance (ANOVA) and Kruskal-Wallis Test\n	8.1 Use Case Examples\n	8.2 ANOVA: A Method for Comparing More Than Two Means\n	8.3 Test Assumptions\n	8.4 Sum of Squares Decomposition and the F Statistic\n	8.5 ANOVA for Two Groups and the Two-Sample t Test\n	8.6 Fixed and Random Effects\n	8.7 F Test Power\n	8.8 Violating ANOVA Assumptions\n	8.9 Multiple Comparisons\n		8.9.1 Tukey\'s Test\n		8.9.2 Dunnett\'s Test\n	8.10 Non-parametric ANOVA: Kruskal-Wallis Test\n	8.11 Example Data\n	8.12 How to Proceed in R\n		8.12.1 One-Way ANOVA\n		8.12.2 Multiple Comparisons\n		8.12.3 Power Analysis\n		8.12.4 Testing a Random Effect\n		8.12.5 Kruskal-Wallis Test\n	8.13 Reporting Analyses\n		8.13.1 Methods\n		8.13.2 Results\n	8.14 Recommended Reading\n9 Two-Way Analysis of Variance\n	9.1 Use Case Examples\n	9.2 Factorial Design\n	9.3 Sum of Squares Decomposition and Test Statistics\n	9.4 Two-Way ANOVA with and without Interactions\n	9.5 Two-Way ANOVA with No Replicates\n	9.6 Experimental Design\n		9.6.1 Evaluating Data from Randomised Blocks and Latin Squares\n	9.7 Multiple Comparisons\n	9.8 Non-parametric Methods\n	9.9 Example Data\n	9.10 How to Proceed in R\n		9.10.1 Factorial ANOVA with Two Factors\n		9.10.2 Using a Fixed vs. Random Effect for a Factor\n		9.10.3 Analysing Randomised Blocks and Latin Squares\n		9.10.4 Friedman Test\n	9.11 Reporting Analyses\n		9.11.1 Methods\n		9.11.2 Results\n	9.12 Recommended Reading\n10 Data Transformations for Analysis of Variance\n	10.1 Assumptions of ANOVA and their Possible Violations\n	10.2 Log-transformation\n	10.3 Arcsine Transformation\n	10.4 Square-Root and Box-Cox Transformation\n	10.5 Concluding Remarks\n	10.6 Example Data\n	10.7 How to Proceed in R\n	10.8 Reporting Analyses\n		10.8.1 Methods\n		10.8.2 Results\n	10.9 Recommended Reading\n11 Hierarchical ANOVA, Split-Plot ANOVA, Repeated Measurements\n	11.1 Hierarchical ANOVA\n		11.1.1 Use Case Examples\n		11.1.2 Decomposing Variation in a Hierarchical ANOVA Model\n	11.2 Split-Plot ANOVA\n		11.2.1 Use Case Example\n		11.2.2 Analysis\n	11.3 ANOVA for Repeated Measurements\n		11.3.1 Use Case Examples\n		11.3.2 Analysis\n	11.4 Example Data\n	11.5 How to Proceed in R\n		11.5.1 Hierarchical ANOVA\n		11.5.2 Variance Components\n		11.5.3 Split-Plot ANOVA\n		11.5.4 ANOVA for Repeated Measurements\n	11.6 Reporting Analyses\n		11.6.1 Methods\n		11.6.2 Results\n	11.7 Recommended Reading\n12 Simple Linear Regression: Dependency Between Two Quantitative Variables\n	12.1 Use Case Examples\n	12.2 Regression and Correlation\n	12.3 Simple Linear Regression\n	12.4 Testing Hypotheses\n		12.4.1 Introduction\n		12.4.2 Test Based on Sum of Squares Decomposition\n		12.4.3 Tests of Regression Coefficients\n		12.4.4 Test Power\n	12.5 Confidence and Prediction Intervals\n	12.6 Regression Diagnostics and Transforming Data in Regression\n	12.7 Regression Through the Origin\n	12.8 Predictor with Random Variation\n	12.9 Linear Calibration\n	12.10 Example Data\n	12.11 How to Proceed in R\n		12.11.1 Simple Linear Regression\n		12.11.2 Model II Regression\n	12.12 Reporting Analyses\n		12.12.1 Methods\n		12.12.2 Results\n	12.13 Recommended Reading\n13 Correlation: Relationship Between Two Quantitative Variables\n	13.1 Use Case Examples\n	13.2 Correlation as a Dependency Statistic for Two Variables on an Equal Footing\n	13.3 Test Power\n	13.4 Non-parametric Methods\n	13.5 Interpreting Correlations\n	13.6 Statistical Dependency and Causality\n	13.7 Example Data\n	13.8 How to Proceed in R\n		13.8.1 Estimating Correlation and its Significance\n		13.8.2 Test Power Analysis\n	13.9 Reporting Analyses\n		13.9.1 Methods\n		13.9.2 Results\n	13.10 Recommended Reading\n14 Multiple Regression and General Linear Models\n	14.1 Use Case Examples\n	14.2 Dependency of a Response Variable on Multiple Predictors\n	14.3 Partial Correlation\n	14.4 General Linear Models and Analysis of Covariance\n	14.5 Example Data\n	14.6 How to Proceed in R\n		14.6.1 Multiple Regression\n		14.6.2 Visualising Models of Multiple Regression\n		14.6.3 Stepwise Selection of Predictors\n		14.6.4 Partial Correlation\n		14.6.5 Analysis of Covariance\n	14.7 Reporting Analyses\n		14.7.1 Methods\n		14.7.2 Results\n	14.8 Recommended Reading\n15 Generalised Linear Models\n	15.1 Use Case Examples\n	15.2 Properties of Generalised Linear Models\n	15.3 Analysis of Deviance\n	15.4 Overdispersion\n	15.5 Log-linear Models\n	15.6 Predictor Selection\n	15.7 Example Data\n	15.8 How to Proceed in R\n		15.8.1 Simple Logistic Regression\n		15.8.2 Analysing Contingency Tables with Log-linear Models\n	15.9 Reporting Analyses\n		15.9.1 Methods\n		15.9.2 Results\n	15.10 Recommended Reading\n16 Regression Models for Non-linear Relationships\n	16.1 Use Case Examples\n	16.2 Introduction\n	16.3 Polynomial Regression\n	16.4 Non-linear Regression\n	16.5 Example Data\n	16.6 How to Proceed in R\n		16.6.1 Polynomial Regression\n		16.6.2 Non-linear Regression\n	16.7 Reporting Analyses\n		16.7.1 Methods\n		16.7.2 Results\n	16.8 Recommended Reading\n17 Structural Equation Models\n	17.1 Use Case Examples\n	17.2 SEMs and Path Analysis\n	17.3 Example Data\n	17.4 How to Proceed in R\n	17.5 Reporting Analyses\n		17.5.1 Methods\n		17.5.2 Results\n	17.6 Recommended Reading\n18 Discrete Distributions and Spatial Point Patterns\n	18.1 Use Case Examples\n	18.2 Poisson Distribution\n	18.3 Comparing the Variance with the Mean to Measure Spatial Distribution\n	18.4 Spatial Pattern Analyses Based on the K-function\n	18.5 Binomial Distribution\n	18.6 Example Data\n	18.7 How to Proceed in R\n	18.8 Reporting Analyses\n		18.8.1 Methods\n		18.8.2 Results\n	18.9 Recommended Reading\n	Poisson and binomial distributions\n	Spatial pattern analysis\n19 Survival Analysis\n	19.1 Use Case Examples\n	19.2 Survival Function and Hazard Rate\n	19.3 Differences in Survival Among Groups\n	19.4 Cox Proportional Hazard Model\n	19.5 Example Data\n	19.6 How to Proceed in R\n	19.7 Reporting Analyses\n		19.7.1 Methods\n		19.7.2 Results\n	19.8 Recommended Reading\n20 Classification and Regression Trees\n	20.1 Use Case Examples\n	20.2 Introducing CART\n	20.3 Pruning the Tree and Crossvalidation\n	20.4 Competing and Surrogate Predictors\n	20.5 Example Data\n	20.6 How to Proceed in R\n		20.6.1 Regression Trees\n		20.6.2 Classification Trees\n	20.7 Reporting Analyses\n		20.7.1 Methods\n		20.7.2 Results\n	20.8 Recommended Reading\n21 Classification\n	21.1 Use Case Examples\n	21.2 Aims and Properties of Classification\n	21.3 Input Data\n	21.4 Similarity and Distance\n	21.5 Clustering Algorithms\n	21.6 Displaying Results\n	21.7 Divisive Methods\n	21.8 Example Data\n	21.9 How to Proceed in R\n	21.10 Other Software\n	21.11 Reporting Analyses\n		21.11.1 Methods\n		21.11.2 Results\n	21.12 Recommended Reading\n22 Ordination\n	22.1 Use Case Examples\n	22.2 Unconstrained Ordination Methods\n	22.3 Constrained Ordination Methods\n	22.4 Discriminant Analysis\n	22.5 Example Data\n	22.6 How to Proceed in R\n		22.6.1 Unconstrained Ordination\n		22.6.2 Constrained Ordination\n		22.6.3 Discriminant Analysis\n	22.7 Alternative Software\n	22.8 Reporting Analyses\n		22.8.1 Methods\n		22.8.2 Results\n	22.9 Recommended Reading\nAppendix A: First Steps with R Software\n	A.1 Starting and Ending R, Command Line, Organising Data\n	A.2 Managing Your Data\n	A.3 Data Types in R\n	A.4 Importing Data into R\n	A.5 Simple Graphics\n	A.6 Frameworks for R\n	A.7 Other Introductions to Work with R\nIndex