Advanced R Statistical Programming and Data Models - Analysis, Machine Learning, and Visualization

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples us...

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Bibliographic Details
Main Authors Wiley, Matt, Wiley, Joshua F.
Format eBook Book
LanguageEnglish
Published Berkeley, CA Apress, an imprint of Springer Nature 2019
Apress
Apress L. P
Edition1
Subjects
Computer programs
Computer Science
Decision making
General Engineering & Project Administration
General References
Industrial management
Mathematical models
Mathematical statistics > Data processing
Probability and Statistics in Computer Science
Professional and Applied Computing
Programming Languages, Compilers, Interpreters
Programming Techniques
R (Computer program language)
Statistical methods
Online AccessGet full text
ISBN9781484228715
1484228715
9781484228722
1484228723
DOI10.1007/978-1-4842-2872-2

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Table of Contents:
  • Title Page Introduction Table of Contents 1. Univariate Data Visualization 2. Multivariate Data Visualization 3. GLM 1 4. GLM 2 5. GAMs 6. ML: Introduction 7. ML: Unsupervised 8. ML: Supervised 9. Missing Data 10. GLMMs: Introduction 11. GLMMs: Linear 12. GLMMs: Advanced 13. Modelling IIV Bibliography Index
  • 12.1 Conceptual Background -- 12.2 Logistic GLMM -- Random Intercept -- Random Intercept and Slope -- 12.3 Poisson and Negative Binomial GLMMs -- Random Intercept -- Random Intercept and Slope -- 12.4 Summary -- Chapter 13: Modelling IIV -- 13.1 Conceptual Background -- Bayesian Inference -- What Is IIV? -- Methods for Quantifying and Modelling Variability -- Intra-individual Variability as a Predictor -- Bayesian Variability Model -- Software Implementation: VARIAN -- 13.2 R Examples -- IIV Predicting a Continuous Outcome -- 13.3 Summary -- Bibliography -- Index
  • 6.5 Summary -- Chapter 7: ML: Unsupervised -- 7.1 Data Background and Exploratory Analysis -- 7.2 kmeans -- 7.3 Hierarchical Clusters -- 7.4 Principal Component Analysis -- 7.5 Non-linear Cluster Analysis -- 7.6 Summary -- Chapter 8: ML: Supervised -- 8.1 Data Preparation -- One Hot Encoding -- Scale and Center -- Transformations -- Train vs. Validation Data -- Principal Component Analysis -- 8.2 Supervised Learning Models -- Support Vector Machines -- Classification and Regression Trees -- Random Forests -- Stochastic Gradient Boosting -- Multilayer Perceptron -- 8.3 Summary -- Chapter 9: Missing Data -- 9.1 Conceptual Background -- Multiple Imputation -- General -- Approaches to Multiple Imputation -- Non-linear Effects and Non-normal Outcomes -- GLMs for Imputation -- GAMs for Imputation -- RFs for Imputation -- Other Cases -- 9.2 R Examples -- Multiple Imputation with Regression -- Multiple Imputation with Parallel Processing -- Multiple Imputation Using Random Forests -- 9.3 Case Study: Multiple Imputation with RFs -- 9.4 Summary -- Chapter 10: GLMMs: Introduction -- 10.1 Multilevel Data -- Reshaping Data -- Daily Dataset -- 10.2 Descriptive Statistics -- Basic Descriptives -- Intraclass Correlation Coefficient (ICC) -- 10.3 Exploration and Assumptions -- Distribution and Outliers -- Time Trends -- Autocorrelation -- Assumptions -- 10.4 Summary -- Chapter 11: GLMMs: Linear -- 11.1 Theory -- Generalized Linear Mixed Models -- Mixed Effects vs. Multilevel Model Terminology -- Statistical Inference -- Effect Sizes -- Random Intercept Model -- Visualizing Random Effects -- Interpreting Random Intercept Models -- Random Intercept and Slope Model -- Intercepts and Slopes as Outcomes -- 11.2 R Examples -- Linear Mixed Model with Random Intercept -- Linear Mixed Model with Random Intercept and Slope -- 11.3 Summary -- Chapter 12: GLMMs: Advanced
  • Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Univariate Data Visualization -- 1.1 Distribution -- Visualizing the Observed Distribution -- Stacked Dot Plots and Histograms -- Density Plots -- Comparing the Observed Distribution with Expected Distributions -- Q-Q Plots -- Density Plots -- Fitting More Distributions -- 1.2 Anomalous Values -- 1.3 Summary -- Chapter 2: Multivariate Data Visualization -- 2.1 Distribution -- 2.2 Anomalous Values -- 2.3 Relations Between Variables -- Assessing Homogeneity of Variance -- 2.4 Summary -- Chapter 3: GLM 1 -- 3.1 Conceptual Background -- 3.2 Categorical Predictors and Dummy Coding -- Two-Level Categorical Predictors -- Three- or More Level Categorical Predictors -- 3.3 Interactions and Moderated Effects -- 3.4 Formula Interface -- 3.5 Analysis of Variance -- Conceptual Background -- ANOVA in R -- 3.6 Linear Regression -- Conceptual Background -- Linear Regression in R -- High-Performance Linear Regression -- 3.7 Controlling for Confounds -- 3.8 Case Study: Multiple Linear Regression with Interactions -- 3.9 Summary -- Chapter 4: GLM 2 -- 4.1 Conceptual Background -- Logistic Regression -- Count Regression -- 4.2 R Examples -- Binary Logistic Regression -- Ordered Logistic Regression -- Multinomial Logistic Regression -- Poisson and Negative Binomial Regression -- 4.3 Case Study: Multinomial Logistic Regression -- 4.4 Summary -- Chapter 5: GAMs -- 5.1 Conceptual Overview -- Smoothing Splines -- 5.2 GAMs in R -- Gaussian Outcomes -- Basic GAMs -- GAMs with Interactions -- Binary Outcomes -- Unordered Outcomes -- Count Outcomes -- 5.3 Summary -- Chapter 6: ML: Introduction -- 6.1 Training and Validation Data -- 6.2 Resampling and Cross-Validation -- 6.3 Bootstrapping -- 6.4 Parallel Processing and Random Numbers -- foreach