A beginner's guide to data exploration and visualisation with R / Elena N. Ieno, Alain F. Zuur

Incluye bibliografía: páginas 155-158 e índice: páginas 159-160

Preface.. Acknowledgements.. Datasets Used in This Book.. 1 Introduction.. 1.1 Speaking the Same Language.. 1.2. General Points.. 1.3 Outline of This Book.. 2 Outliers.. 2.1 What Is an Outlier?.. 2.2 Boxplot To Identify Outliers In One Dimension.. 2.2.1 Simple boxplot.. 2.2.2 Conditional boxplot.. 2.2.3 Multi-panel boxplots from the lattice package.. 2.3 Cleveland Dotplot To Identify Outliers.. 2.3.1 Simple Cleveland dotplots.. 2.3.2 Conditional Cleveland dotplots.. 2.3.3 Multi-panel Cleveland dotplots from the lattice package.. 2.4 Boxplots or Cleveland Dotplots?.. 2.5 Can We Apply a Test For Outliers?.. 2.5.1 Z-score.. 2.5.2 Grubbs' test.. 2.6 Outliers In the Two-Dimensional Space.. 2.7 Influential Observations In Regression Models.. 2.8 What to do if You Detect Potential Outliers.. 2.9 Outliers and Multivariate Data.. 2.10 The Pros and Cons of Transformations.. 3 Normality and Homogeneity.. 3.1 What Is Normality?.. 3.2 Histograms And Conditional Histograms.. 3.2.1 Multipanel histograms from the lattice package.. 3.2.2 When is normality of the raw data considered?.. 3.3 Kernel Density Plots.. 3.4 Quantile-Quantile Plots.. 3.4.1 Quantile-quantile plots from the lattice package.. 3.5 Using Tests to Check For Normality.. 3.6 Homogeneity of Variance.. 3.6.1 Conditional boxplots.. 3.6.2 Scatterplots for continuous explanatory variables.. 3.7 Using Tests to Check For Homogeneity.. 3.7.1 The Bartlett test.. 3.7.2 The F-ratio test.. 3.7.3 Levene's test.. 3.7.4 So which test would you choose?.. 3.7.5 R code.. 3.7.6 Using graphs?.. 4 Relationships.. 4.1 Simple Scatterplots.. 4.1.1 Example: Clam data.. 4.1.2 Example: Rabbit data.. 4.1.3 Example: Blow fly data.. 4.2 Multipanel Scatterplots.. 4.2.1 Example: Polychaeta data.. 4.2.2 Example: Bioluminescence data.. 4.3 Pairplots.. 4.3.1 Bioluminescence data.. 4.3.2 Cephalopod data.. 4.3.3 Zoobenthos data.. 4.4 Can We Include Interactions?.. 4.4.1 Irish pH data

4.4.2 Godwit data.. 4.4.3 Irish pH data revisited.. 4.4.4 Parasite data.. 4.5 Design and Interaction Plots.. 5 Collinearity and Confounding.. 5.1 What is Collinearity?.. 5.2 The Sample Correlation Coefficient.. 5.3 Correlation and Outliers.. 5.4 Correlation Matrices.. 5.5 Correlation and Pairplots.. 5.6 Collinearity Due To Interactions.. 5.7 Visualising Collinearity With Conditional Boxplots.. 5.8 Quantifying Collinearity Using Vifs.. 5.8.1 Variance inflation factors.. 5.8.2 Geometric presentation of collinearity.. 5.8.3 Tolerance.. 5.8.4 What constitutes a high VIF value?.. 5.8.5 VIFs in action.. 5.9 Generalised Vif Values.. 5.10 Visualising Collinearity Using Pca Biplot.. 5.11 Causes of Collinearity And Solutions.. 5.12 Be Stubborn and Keep Collinear Covariates?.. 5.13 Confounding Variables.. 5.13.1 Visualising confounding variables.. 5.13.2 Confounding factors in time series analysis.. 6 Case Study: Methane Fluxes.. 6.1 Introduction.. 6.2 Data Exploration.. 6.2.1 Where in the world are the sites?.. 6.2.2 Working with ggplot2.. 6.2.3 Outliers.. 6.2.4 Collinearity.. 6.2.5 Relationships.. 6.2.6 Interactions 6.2.7 Where in the world are the sites (continued?.. 6.3 Statistical Analysis Using Linear Regression.. 6.3.1 Model formulation.. 6.3.2 Fitting a linear regression model.. 6.3.3 Model validation of the linear regression model.. 6.3.4 Interpretation of the linear regression model.. 6.4 Statistical Analysis Using a Mixed Effects Model.. 6.4.1 Model formulation.. 6.4.2 Fitting a mixed effects model.. 6.4.3 Model validation of the mixed effects model.. 6.4.4 Interpretation of the linear mixed effects model.. 6.5 Conclusions.. 6.6 What To Present In a Paper.. 7 Case Study: Oystercatcher Shell Length.. 7.1 Importing the Data.. 7.2 Data Exploration.. 7.3 Applying A Linear Regression Model.. 7.4 Understanding The Results.. 7.5 Trouble.. 7.6 Conclusions

8 Case Study: Hawaiian Bird Time Series.. 8.1 Importing the Data.. 8.2 Coding the Data.. 8.3 Multi-Panel Graph Using Xyplot From Lattice.. 8.3.1 Attempt 1 using xyplot.. 8.3.2 Attempt 2 using xyplot.. 8.3.3 Attempt 3 using xyplot.. 8.4 Multi-Panel Graph Using Ggplot2.. 8.5 Conclusions.. References.. Index.. Books by Highland Statistics

This book uses ecological datasets to discuss data exploration and visualisation tools. The authors also explain how to visualise the results of statistical models, an important aspect for publishing scientific papers. The book includes the R code needed to construct, visualise, and explore the main features of the data step by step. eng