Advanced R statistical programming and data models : analysis, machine learning, and visualization /: analysis, machine learning, and visualization. (2019)

• Record Type: Book
• Further Information: Note: Matt Wiley, Joshua F. Wiley.
• Authors: Wiley, Matt; Wiley, Joshua F
• Contents: 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 Background3.2 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 RegressionOrdered 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; 6.5 Summary Chapter 7: ML: Unsupervised7.1 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 GeneralApproaches 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
• Publisher Details: Berkeley, CA : Apress
• Publication Date: 2019
• Extent: 1 online resource (xx, 638 pages), illustrations (some color)
• Subjects: 519.5028/51; R (Computer program language); Mathematical statistics -- Data processing; Electronic books; Electronic books
• Languages: English
• ISBNs: 9781484228722; 1484228723
• Related ISBNs: 9781484228715
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed February 28, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.