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by Larry A. Pace, Dr. Joshua F. Wiley
Beginning R: An Introduction to Statistical Programming, Second Edition
Cover
Title
Copyright
Dedication
Contents at a Glance
Contents
About the Author
In Memoriam
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1 : Getting Star?ted
1.1 What is R, Anyway?
1.2 A First R Session
1.3 Your Second R Session
1.3.1 Working with Indexes
1.3.2 Representing Missing Data in R
1.3.3 Vectors and Vectorization in R
1.3.4 A Brief Introduction to Matrices
1.3.5 More on Lists
1.3.6 A Quick Introduction to Data Frames
Chapter 2 : Dealing with Dates, Strings, and Data Frames
2.1 Working with Dates and Times
2.2 Working with Strings
2.3 Working with Data Frames in the Real World
2.3.1 Finding and Subsetting Data
2.4 Manipulating Data Structures
2.5 The Hard Work of Working with Larger Datasets
Chapter 3 : Input and Output
3.1 R Input
3.1.1 The R Editor
3.1.2 The R Data Editor
3.1.3 Other Ways to Get Data Into R
3.1.4 Reading Data from a File
3.1.5 Getting Data from the Web
3.2 R Output
3.2.1 Saving Output to a File
Chapter 4 : Control Structures
4.1 Using Logic
4.2 Flow Control
4.2.1 Explicit Looping
4.2.2 Implicit Looping
4.3 If, If-Else, and ifelse() Statements
Chapter 5 : Functional Programming
5.1 Scoping Rules
5.2 Reserved Names and Syntactically Correct Names
5.3 Functions and Arguments
5.4 Some Example Functions
5.4.1 Guess the Number
5.4.2 A Function with Arguments
5.5 Classes and Methods
5.5.1 S3 Class and Method Example
5.5.2 S3 Methods for Existing Classes
Chapter 6 : Probability Distributions
6.1 Discrete Probability Distributions
6.2 The Binomial Distribution
6.2.1 The Poisson Distribution
6.2.2 Some Other Discrete Distributions
6.3 Continuous Probability Distributions
6.3.1 The Normal Distribution
6.3.2 The t Distribution
6.3.3 The F distribution
6.3.4 The Chi-Square Distribution
References
Chapter 7 : Working with Tables
7.1 Working with One-Way Tables
7.2 Working with Two-Way Tables
Chapter 8 : Descriptive Statistics and Exploratory Data Analysis
8.1 Central Tendency
8.1.1 The Mean
8.1.2 The Median
8.1.3 The Mode
8.2 Variability
8.2.1 The Range
8.2.2 The Variance and Standard Deviation
8.3 Boxplots and Stem-and-Leaf Displays
8.4 Using the fBasics Package for Summary Statistics
References
Chapter 9 : Working with Graphics
9.1 Creating Effective Graphics
9.2 Graphing Nominal and Ordinal Data
9.3 Graphing Scale Data
9.3.1 Boxplots Revisited
9.3.2 Histograms and Dotplots
9.3.3 Frequency Polygons and Smoothed Density Plots
9.3.4 Graphing Bivariate Data
References
Chapter 10 : Traditional Statistical Methods
10.1 Estimation and Confidence Intervals
10.1.1 Confidence Intervals for Means
10.1.2 Confidence Intervals for Proportions
10.1.3 Confidence Intervals for the Variance
10.2 Hypothesis Tests with One Sample
10.3 Hypothesis Tests with Two Samples
References
Chapter 11 : Modern Statistical Methods
11.1 The Need for Modern Statistical Methods
11.2 A Modern Alternative to the Traditional t Test
11.3 Bootstrapping
11.4 Permutation Tests
References
Chapter 12 : Analysis of Variance
12.1 Some Brief Background
12.2 One-Way ANOVA
12.3 Two-Way ANOVA
12.3.1 Repeated-Measures ANOVA
> results <- aov ( fitness ~ time + Error (id / time ), data = repeated)
12.3.2 Mixed-Model ANOVA
References
Chapter 13 : Correlation and Regression
13.1 Covariance and Correlation
13.2 Linear Regression: Bivariate Case
13.3 An Extended Regression Example: Stock Screener
13.3.1 Quadratic Model: Stock Screener
13.3.2 A Note on Time Series
13.4 Confidence and Prediction Intervals
References
Chapter 14 : Multiple Regression
14.1 The Conceptual Statistics of Multiple Regression
14.2 GSS Multiple Regression Example
14.2.1 Exploratory Data Analysis
14.2.2 Linear Model (the First)
14.2.3 Adding the Next Predictor
14.2.4 Adding More Predictors
14.2.5 Presenting Results
14.3 Final Thoughts
References
Chapter 15 : Logistic Regression
15.1 The Mathematics of Logistic Regression
15.2 Generalized Linear Models
15.3 An Example of Logistic Regression
15.3.1 What If We Tried a Linear Model on Age?
15.3.2 Seeing If Age Might Be Relevant with Chi Square
15.3.3 Fitting a Logistic Regression Model
15.3.4 The Mathematics of Linear Scaling of Data
15.3.5 Logit Model with Rescaled Predictor
15.3.6 Multivariate Logistic Regression
15.4 Ordered Logistic Regression
15.4.1 Parallel Ordered Logistic Regression
15.4.2 Non-Parallel Ordered Logistic Regression
15.5 Multinomial Regression
References
Chapter 16 : Modern Statistical Methods II
16.1 Philosophy of Parameters
16.2 Nonparametric Tests
16.2.1 Wilcoxon-Signed-Rank Test
16.2.2 Spearman’s Rho
16.2.3 Kruskal-Wallis Test
16.2.4 One-Way Test
16.3 Bootstrapping
16.3.1 Examples from mtcars
16.3.2 Bootstrapping Confidence Intervals
16.3.3 Examples from GSS
16.4 Final Thought
References
Chapter 17 : Data Visualization Cookbook
17.1 Required Packages
17.2 Univariate Plots
17.3 Customizing and Polishing Plots
17.4 Multivariate Plots
17.5 Multiple Plots
17.6 Three-Dimensional Graphs
References
Chapter 18 : High-Performance Computing
18.1 Data
18.2 Parallel Processing
18.2.1 Other Parallel Processing Approaches
References
Chapter 19 : Text Mining
19.1 Installing Needed Packages and Software
19.1.1 Java
19.1.2 PDF Software
19.1.3 R Packages
19.1.4 Some Needed Files
19.2 Text Mining
19.2.1 Word Clouds and Transformations
19.2.2 PDF Text Input
19.2.3 Google News Input
19.2.4 Topic Models
19.3 Final Thoughts
References
Index
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