R-Ticulate : A Beginner's Guide to Data Analysis for Natural Scientists

R-Ticulate A Beginner's Guide to Data Analysis for Natural Scientists

Detalles Bibliográficos
Otros Autores: Bader, Martin (Professor), author (author), Leuzinger, Sebastian, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc [2024]
Edición:First edition
Materias:
R (Computer program language)
Science > Data processing.
Science > Statistical methods.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009840466506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Foreword
  • Preface
  • About the Companion Website
  • Chapter 1 Hypotheses, Variables, Data
  • 1.1 Occam's Razor
  • 1.2 Scientific Hypotheses
  • 1.3 The Choice of a Software
  • 1.3.1 First Steps in R
  • 1.4 Variables
  • 1.4.1 Variable Names and Values
  • 1.4.2 Types of Variables
  • 1.4.3 Predictor and Response Variables
  • 1.5 Data Processing and Data Formats
  • 1.5.1 The Long vs. the Wide Format
  • 1.5.2 Choice of Variable, Dataset, and File Names
  • 1.5.3 Adding, Removing, and Subsetting Variables and Data Frames
  • 1.5.4 Aggregating Data
  • 1.5.5 Working with Time and Strings
  • Chapter 2 Measuring Variation
  • 2.1 What Is Variation?
  • 2.2 Treatment vs. Control
  • 2.3 Systematic and Unsystematic Variation
  • 2.4 The Signal‐to‐Noise Ratio
  • 2.5 Measuring Variation Graphically
  • 2.6 Measuring Variation Using Metrics
  • 2.7 The Standard Error
  • 2.8 Population vs. Sample
  • Chapter 3 Distributions and Probabilities
  • 3.1 Probability Distributions
  • 3.2 Finding the Best Fitting Distribution for Sample Data
  • 3.2.1 Graphical Tools
  • 3.2.2 Goodness‐of‐Fit Tests
  • 3.3 Quantiles
  • 3.4 Probabilities
  • 3.4.1 Density Functions (dnorm, dbinom,...)
  • 3.4.2 Probability Distribution Functions (pnorm, pbinom,...)
  • 3.4.3 Quantile Functions (qnorm, qbinom,...)
  • 3.4.4 Random Sampling Functions (rnorm, rbinom,...)
  • 3.5 The Normal Distribution
  • 3.6 Central Limit Theorem
  • 3.7 Test Statistics
  • 3.7.1 Null and Alternative Hypotheses
  • 3.7.2 The Alpha Threshold and Significance Levels
  • 3.7.3 Type I and Type II Errors
  • References
  • Chapter 4 Replication and Randomisation
  • 4.1 Replication
  • 4.2 Statistical Independence
  • 4.3 Randomisation
  • 4.4 Randomisation in R
  • 4.5 Spatial Replication and Randomisation in Observational Studies
  • Chapter 5 Two‐Sample and One‐Sample Tests.
  • 5.1 The t‐Statistic
  • 5.2 Two Sample Tests: Comparing Two Groups
  • 5.2.1 Student's t‐Test
  • 5.2.1.1 Testing for Normality
  • 5.2.1.2 What to Write in a Report or Paper and How to Visualise the Results of a t‐Test
  • 5.2.1.3 Two‐Tailed vs. One‐Tailed t‐Tests
  • 5.2.2 Rank‐Based Two‐Sample Tests
  • 5.3 One‐Sample Tests
  • 5.4 Power Analyses and Sample Size Determination
  • Chapter 6 Communicating Quantitative Information Using Visuals
  • 6.1 The Fundamentals of Scientific Plotting
  • 6.2 Scatter Plots
  • 6.3 Line Plots
  • 6.4 Box Plots and Bar Plots
  • 6.5 Multipanel Plots and Plotting Regions
  • 6.6 Adding Text, Formulae, and Colour
  • 6.7 Interaction Plots
  • 6.8 Images, Colour Contour Plots, and 3D Plots
  • 6.8.1 Adding Images to Plots
  • 6.8.2 Colour Contour Plots
  • References
  • Chapter 7 Working with Categorical Data
  • 7.1 Tabling and Visualising Categorical Data
  • 7.2 Contingency Tables
  • 7.3 The Chi‐squared Test
  • 7.4 Decision Trees
  • 7.5 Optimising Decision Trees
  • References
  • Chapter 8 Working with Continuous Data
  • 8.1 Covariance
  • 8.2 Correlation Coefficient
  • 8.3 Transformations
  • 8.4 Plotting Correlations
  • 8.5 Correlation Tests
  • References
  • Chapter 9 Linear Regression
  • 9.1 Basics and Simple Linear Regression
  • 9.1.1 Making Sense of the summary Output for Regression Models Fitted with lm
  • 9.1.2 Model Diagnostics
  • 9.1.3 Model Predictions and Visualisation
  • 9.1.4 What to Write in a Report or Paper?
  • 9.1.4.1 Material and Methods
  • 9.1.4.2 Results
  • 9.1.5 Dealing with Variance Heterogeneity
  • 9.2 Multiple Linear Regression
  • 9.2.1 Multicollinearity in Multiple Regression Models
  • 9.2.2 Testing Interactions Among Predictors
  • 9.2.3 Model Selection and Comparison
  • 9.2.4 Variable Importance
  • 9.2.5 Visualising Multiple Linear Regression Results
  • References.
  • Chapter 10 One or More Categorical Predictors - Analysis of Variance
  • 10.1 Comparing Groups
  • 10.2 Comparing Groups Numerically
  • 10.3 One‐way ANOVA Using R
  • 10.4 Checking for the Model Assumptions
  • 10.5 Post Hoc Comparisons
  • 10.6 Two‐way ANOVA and Interactions
  • 10.7 What If the Model Assumptions Are Violated?
  • Reference
  • Chapter 11 Analysis of Covariance (ANCOVA)
  • 11.1 Interpreting ANCOVA Results
  • 11.2 Post Hoc Test for ANCOVA
  • References
  • Chapter 12 Some of What Lies Ahead
  • 12.1 Generalised Linear Models
  • 12.2 Nonlinear Regression
  • 12.2.1 Initial Parameter Estimates (Starting Values)
  • 12.2.2 Nonlinear Model Fitting and Visualisation
  • 12.3 Generalised Additive Models
  • 12.4 Modern Approaches to Dealing with Heteroscedasticity
  • 12.4.1 Variance Modelling Using Generalised Least‐squares Estimation
  • 12.4.2 Robust, Heteroscedasticity‐Consistent Covariance Matrix Estimation
  • References
  • Index
  • EULA.

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