Biostatistics with R
- Length: 384 pages
- Edition: 1
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2020-09-10
- ISBN-10: 1108727344
- ISBN-13: 9781108727341
- Sales Rank: #1321712 (See Top 100 Books)
Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.
Cover Half-title Reviews Title page Copyright information Contents Preface Acknowledgements 1 Basic Statistical Terms, Sample Statistics 1.1 Cases, Variables and Data Types 1.2 Population and Random Sample 1.3 Sample Statistics 1.3.1 Characteristics of Position 1.3.1.1 Arithmetic Mean (Average) 1.3.1.2 Median and Other Quantiles 1.3.1.3 Mode 1.3.1.4 Geometric Mean 1.3.2 Characteristics of Variability (Spread) 1.3.2.1 Range 1.3.2.2 Variance 1.3.2.3 Standard Deviation 1.3.2.4 Coefficient of Variation 1.3.2.5 Interquartile Range 1.4 Precision of Mean Estimate, Standard Error of Mean 1.5 Graphical Summary of Individual Variables 1.6 Random Variables, Distribution, Distribution Function, Density Distribution 1.6.1 Probability Distributions and Distribution Functions of Discrete Random Variables 1.6.2 Distribution Functions and Probability Density of Continuous Random Variables 1.7 Example Data 1.8 How to Proceed in R 1.8.1 Graphical Summary of Quantitative Variables 1.9 Reporting Analyses 1.9.1 Methods 1.9.2 Results 1.10 Recommended Reading 2 Testing Hypotheses, Goodness-of-Fit Test 2.1 Principles of Hypothesis Testing 2.2 Possible Errors in Statistical Tests of Hypotheses 2.3 Null Models with Parameters Estimated from the Data: Testing Hardy-Weinberg Equilibrium 2.4 Sample Size 2.5 Critical Values and Significance Level 2.6 Too Good to Be True 2.7 Bayesian Statistics: What is It? 2.8 The Dark Side of Significance Testing 2.8.1 Misinterpretation of p-Values 2.8.2 Too Much Attention on p-Values 2.8.3 Suggested Solutions 2.9 Example Data 2.10 How to Proceed in R 2.11 Reporting Analyses 2.11.1 Methods 2.11.2 Results 2.12 Recommended Reading 3 Contingency Tables 3.1 Two-Way Contingency Tables 3.1.1 Use Case Examples 3.1.2 Analysing Two-Way Contingency Tables 3.1.3 Correction for Continuity 3.1.4 G Test 3.1.5 Two-By-Two Tables 3.2 Measures of Association Strength 3.3 Multidimensional Contingency Tables 3.4 Statistical and Causal Relationship 3.5 Visualising Contingency Tables 3.6 Example Data 3.7 How to Proceed in R 3.8 Reporting Analyses 3.8.1 Methods 3.8.2 Results 3.9 Recommended Reading 4 Normal Distribution 4.1 Main Properties of a Normal Distribution 4.2 Skewness and Kurtosis 4.3 Standardised Normal Distribution 4.4 Verifying the Normality of a Data Distribution 4.5 Example Data 4.6 How to Proceed in R 4.6.1 Finding the Values of a Distribution Function and Quantiles 4.6.2 Testing for an Agreement with a Normal Distribution 4.7 Reporting Analyses 4.7.1 Methods 4.7.2 Results 4.8 Recommended Reading 5 Student's t Distribution 5.1 Use Case Examples 5.2 t Distribution and its Relation to the Normal Distribution 5.3 Single Sample Test and Paired t Test 5.4 One-Sided Tests 5.5 Confidence Interval of the Mean 5.6 Test Assumptions 5.7 Reporting Data Variability and Mean Estimate Precision 5.8 How Large Should a Sample Size Be? 5.9 Example Data 5.10 How to Proceed in R 5.10.1 Single Sample and Paired t Test 5.10.2 Summarising Variability and Describing Mean Precision 5.11 Reporting Analyses 5.11.1 Methods 5.11.2 Results 5.12 Recommended Reading 6 Comparing Two Samples 6.1 Use Case Examples 6.2 Testing for Differences in Variance 6.3 Comparing Means 6.4 Example Data 6.5 How to Proceed in R 6.5.1 F Test of Variance Equality 6.5.2 Two-Sample t Test of the Equality of Means 6.6 Reporting Analyses 6.6.1 Methods 6.6.2 Results 6.7 Recommended Reading 7 Non-parametric Methods for Two Samples 7.1 Mann-Whitney Test 7.2 Wilcoxon Test for Paired Observations 7.3 Using Rank-Based Tests 7.4 Permutation Tests 7.5 Example Data 7.6 How to Proceed in R 7.6.1 Mann-Whitney Test 7.6.2 Wilcoxon Paired Data Test 7.6.3 Permutation Tests 7.7 Reporting Analyses 7.7.1 Methods 7.7.2 Results 7.8 Recommended Reading 8 One-Way Analysis of Variance (ANOVA) and Kruskal-Wallis Test 8.1 Use Case Examples 8.2 ANOVA: A Method for Comparing More Than Two Means 8.3 Test Assumptions 8.4 Sum of Squares Decomposition and the F Statistic 8.5 ANOVA for Two Groups and the Two-Sample t Test 8.6 Fixed and Random Effects 8.7 F Test Power 8.8 Violating ANOVA Assumptions 8.9 Multiple Comparisons 8.9.1 Tukey's Test 8.9.2 Dunnett's Test 8.10 Non-parametric ANOVA: Kruskal-Wallis Test 8.11 Example Data 8.12 How to Proceed in R 8.12.1 One-Way ANOVA 8.12.2 Multiple Comparisons 8.12.3 Power Analysis 8.12.4 Testing a Random Effect 8.12.5 Kruskal-Wallis Test 8.13 Reporting Analyses 8.13.1 Methods 8.13.2 Results 8.14 Recommended Reading 9 Two-Way Analysis of Variance 9.1 Use Case Examples 9.2 Factorial Design 9.3 Sum of Squares Decomposition and Test Statistics 9.4 Two-Way ANOVA with and without Interactions 9.5 Two-Way ANOVA with No Replicates 9.6 Experimental Design 9.6.1 Evaluating Data from Randomised Blocks and Latin Squares 9.7 Multiple Comparisons 9.8 Non-parametric Methods 9.9 Example Data 9.10 How to Proceed in R 9.10.1 Factorial ANOVA with Two Factors 9.10.2 Using a Fixed vs. Random Effect for a Factor 9.10.3 Analysing Randomised Blocks and Latin Squares 9.10.4 Friedman Test 9.11 Reporting Analyses 9.11.1 Methods 9.11.2 Results 9.12 Recommended Reading 10 Data Transformations for Analysis of Variance 10.1 Assumptions of ANOVA and their Possible Violations 10.2 Log-transformation 10.3 Arcsine Transformation 10.4 Square-Root and Box-Cox Transformation 10.5 Concluding Remarks 10.6 Example Data 10.7 How to Proceed in R 10.8 Reporting Analyses 10.8.1 Methods 10.8.2 Results 10.9 Recommended Reading 11 Hierarchical ANOVA, Split-Plot ANOVA, Repeated Measurements 11.1 Hierarchical ANOVA 11.1.1 Use Case Examples 11.1.2 Decomposing Variation in a Hierarchical ANOVA Model 11.2 Split-Plot ANOVA 11.2.1 Use Case Example 11.2.2 Analysis 11.3 ANOVA for Repeated Measurements 11.3.1 Use Case Examples 11.3.2 Analysis 11.4 Example Data 11.5 How to Proceed in R 11.5.1 Hierarchical ANOVA 11.5.2 Variance Components 11.5.3 Split-Plot ANOVA 11.5.4 ANOVA for Repeated Measurements 11.6 Reporting Analyses 11.6.1 Methods 11.6.2 Results 11.7 Recommended Reading 12 Simple Linear Regression: Dependency Between Two Quantitative Variables 12.1 Use Case Examples 12.2 Regression and Correlation 12.3 Simple Linear Regression 12.4 Testing Hypotheses 12.4.1 Introduction 12.4.2 Test Based on Sum of Squares Decomposition 12.4.3 Tests of Regression Coefficients 12.4.4 Test Power 12.5 Confidence and Prediction Intervals 12.6 Regression Diagnostics and Transforming Data in Regression 12.7 Regression Through the Origin 12.8 Predictor with Random Variation 12.9 Linear Calibration 12.10 Example Data 12.11 How to Proceed in R 12.11.1 Simple Linear Regression 12.11.2 Model II Regression 12.12 Reporting Analyses 12.12.1 Methods 12.12.2 Results 12.13 Recommended Reading 13 Correlation: Relationship Between Two Quantitative Variables 13.1 Use Case Examples 13.2 Correlation as a Dependency Statistic for Two Variables on an Equal Footing 13.3 Test Power 13.4 Non-parametric Methods 13.5 Interpreting Correlations 13.6 Statistical Dependency and Causality 13.7 Example Data 13.8 How to Proceed in R 13.8.1 Estimating Correlation and its Significance 13.8.2 Test Power Analysis 13.9 Reporting Analyses 13.9.1 Methods 13.9.2 Results 13.10 Recommended Reading 14 Multiple Regression and General Linear Models 14.1 Use Case Examples 14.2 Dependency of a Response Variable on Multiple Predictors 14.3 Partial Correlation 14.4 General Linear Models and Analysis of Covariance 14.5 Example Data 14.6 How to Proceed in R 14.6.1 Multiple Regression 14.6.2 Visualising Models of Multiple Regression 14.6.3 Stepwise Selection of Predictors 14.6.4 Partial Correlation 14.6.5 Analysis of Covariance 14.7 Reporting Analyses 14.7.1 Methods 14.7.2 Results 14.8 Recommended Reading 15 Generalised Linear Models 15.1 Use Case Examples 15.2 Properties of Generalised Linear Models 15.3 Analysis of Deviance 15.4 Overdispersion 15.5 Log-linear Models 15.6 Predictor Selection 15.7 Example Data 15.8 How to Proceed in R 15.8.1 Simple Logistic Regression 15.8.2 Analysing Contingency Tables with Log-linear Models 15.9 Reporting Analyses 15.9.1 Methods 15.9.2 Results 15.10 Recommended Reading 16 Regression Models for Non-linear Relationships 16.1 Use Case Examples 16.2 Introduction 16.3 Polynomial Regression 16.4 Non-linear Regression 16.5 Example Data 16.6 How to Proceed in R 16.6.1 Polynomial Regression 16.6.2 Non-linear Regression 16.7 Reporting Analyses 16.7.1 Methods 16.7.2 Results 16.8 Recommended Reading 17 Structural Equation Models 17.1 Use Case Examples 17.2 SEMs and Path Analysis 17.3 Example Data 17.4 How to Proceed in R 17.5 Reporting Analyses 17.5.1 Methods 17.5.2 Results 17.6 Recommended Reading 18 Discrete Distributions and Spatial Point Patterns 18.1 Use Case Examples 18.2 Poisson Distribution 18.3 Comparing the Variance with the Mean to Measure Spatial Distribution 18.4 Spatial Pattern Analyses Based on the K-function 18.5 Binomial Distribution 18.6 Example Data 18.7 How to Proceed in R 18.8 Reporting Analyses 18.8.1 Methods 18.8.2 Results 18.9 Recommended Reading Poisson and binomial distributions Spatial pattern analysis 19 Survival Analysis 19.1 Use Case Examples 19.2 Survival Function and Hazard Rate 19.3 Differences in Survival Among Groups 19.4 Cox Proportional Hazard Model 19.5 Example Data 19.6 How to Proceed in R 19.7 Reporting Analyses 19.7.1 Methods 19.7.2 Results 19.8 Recommended Reading 20 Classification and Regression Trees 20.1 Use Case Examples 20.2 Introducing CART 20.3 Pruning the Tree and Crossvalidation 20.4 Competing and Surrogate Predictors 20.5 Example Data 20.6 How to Proceed in R 20.6.1 Regression Trees 20.6.2 Classification Trees 20.7 Reporting Analyses 20.7.1 Methods 20.7.2 Results 20.8 Recommended Reading 21 Classification 21.1 Use Case Examples 21.2 Aims and Properties of Classification 21.3 Input Data 21.4 Similarity and Distance 21.5 Clustering Algorithms 21.6 Displaying Results 21.7 Divisive Methods 21.8 Example Data 21.9 How to Proceed in R 21.10 Other Software 21.11 Reporting Analyses 21.11.1 Methods 21.11.2 Results 21.12 Recommended Reading 22 Ordination 22.1 Use Case Examples 22.2 Unconstrained Ordination Methods 22.3 Constrained Ordination Methods 22.4 Discriminant Analysis 22.5 Example Data 22.6 How to Proceed in R 22.6.1 Unconstrained Ordination 22.6.2 Constrained Ordination 22.6.3 Discriminant Analysis 22.7 Alternative Software 22.8 Reporting Analyses 22.8.1 Methods 22.8.2 Results 22.9 Recommended Reading Appendix A: First Steps with R Software A.1 Starting and Ending R, Command Line, Organising Data A.2 Managing Your Data A.3 Data Types in R A.4 Importing Data into R A.5 Simple Graphics A.6 Frameworks for R A.7 Other Introductions to Work with R Index
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