Book description
The highlevel language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cuttingedge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis.
Building on the success of the author's bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.
Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities.
Introduces all the statistical models covered by R, beginning with simple classical tests such as chisquare and ttest.
Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more.
The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.
Table of contents
 Cover Page
 Title Page
 Copyright
 Contents
 Preface

1: Getting Started
 Installing R
 Running R
 Getting Help in R
 Online Help
 Worked Examples of Functions
 Demonstrations of R Functions
 Libraries in R
 Contents of Libraries
 Installing Packages and Libraries
 Command Line versus Scripts
 Data Editor
 Changing the Look of the R Screen
 Significance Stars
 Disappearing Graphics
 Good Housekeeping
 Linking to Other Computer Languages
 Tidying Up

2: Essentials of the R Language
 Screen prompt
 Builtin Functions
 Numbers with Exponents
 Modulo and Integer Quotients
 Rounding
 Infinity and Things that Are Not a Number (NaN)
 Missing values NA
 Assignment
 Operators
 Creating a Vector
 Named Elements within Vectors
 Vector Functions
 Summary Information from Vectors by Groups
 Using with rather than attach
 Using attach in This Book
 Parallel Minima and Maxima: pmin and pmax
 Subscripts and Indices
 Working with Vectors and Logical Subscripts
 Addresses within Vectors
 Finding Closest Values
 Trimming Vectors Using Negative Subscripts
 Logical Arithmetic
 Evaluation of combinations of TRUE and FALSE
 Repeats
 Generate Factor Levels
 Generating Regular Sequences of Numbers
 Variable Names
 Sorting, Ranking and Ordering
 The sample Function
 Matrices
 Arrays
 Character Strings
 The match Function
 Writing functions in R
 Variance
 Degrees of freedom
 Variance Ratio Test
 Using Variance
 Error Bars
 Loops and Repeats
 The switch Function
 The Evaluation Environment of a Function
 Scope
 Optional Arguments
 Variable Numbers of Arguments (...)
 Returning Values from a Function
 Anonymous Functions
 Flexible Handling of Arguments to Functions
 Evaluating Functions with apply , sapply and lapply
 Looking for runs of numbers within vectors
 Saving Data Produced within R to Disc
 Pasting into an Excel Spreadsheet
 Writing an Excel Readable File from R
 Testing for Equality
 Sets: union , intersect and setdiff
 Pattern Matching
 Testing and Coercing in R
 Dates and Times in R

3: Data Input
 The scan Function
 Data Input from Files
 Saving the File from Excel
 Common Errors when Using read.table
 Browsing to Find Files
 Separators and Decimal Points
 Input and Output Formats
 Setting the Working Directory
 Checking Files from the Command Line
 Reading Dates and Times from Files
 Builtin Data Files
 Reading Data from Files with Nonstandard Formats Using scan
 Reading Files with Different Numbers of Values per Line
 The readLnes Function

4: Dataframes
 Subscripts and Indices
 Selecting Rows from the Dataframe at Random
 Sorting Dataframes
 Using Logical Conditions to Select Rows from the Dataframe
 Omitting Rows Containing Missing Values, NA
 Using order and unique to Eliminate Pseudoreplication
 Complex Ordering with Mixed Directions
 A Dataframe with Row Names instead of Row Numbers
 Creating a Dataframe from Another Kind of Object
 Eliminating Duplicate Rows from a Dataframe
 Dates in Dataframes
 Selecting Variables on the Basis of their Attributes
 Using the match Function in Dataframes
 Merging Two Dataframes
 Adding Margins to a Dataframe
 Summarizing the Contents of Dataframes
 5: Graphics
 6: Tables
 7: Mathematics
 8: Classical Tests

9: Statistical Modelling
 Maximum Likelihood
 The Principle of Parsimony (Occam's Razor)
 Types of Statistical Model
 Steps Involved in Model Simplification
 Model Formulae in R
 Box–Cox Transformations
 Model Criticism
 Model checking
 Summary of Statistical Models in R
 Optional arguments in modelfitting functions
 Dataframes containing the same variable names
 Akaike's Information Criterion
 Misspecified Model
 Model checking in R

10: Regression
 Linear Regression
 Polynomial Approximations to Elementary Functions
 Polynomial Regression
 Fitting a Mechanistic Model to Data
 Linear Regression after Transformation
 Prediction following Regression
 Testing for Lack of Fit in a Regression with Replicated Data at Each Level of x
 Bootstrap with Regression
 Jackknife with regression
 Jackknife after Bootstrap
 Serial correlation in the residuals
 Piecewise Regression
 Robust Fitting of Linear Models
 Model Simplification
 The Multiple Regression Model
 11: Analysis of Variance
 12: Analysis of Covariance

13: Generalized Linear Models
 Error Structure
 Linear Predictor
 Link Function
 Canonical Link Functions
 Proportion Data and Binomial Errors
 Count Data and Poisson Errors
 Deviance: Measuring the Goodness of Fit of a GLM
 Quasilikelihood
 Generalized Additive Models
 Offsets
 Residuals
 Misspecified Error Structure
 Misspecified Link Function
 Overdispersion
 Bootstrapping a GLM
 14: Count Data

15: Count Data in Tables
 A TwoClass Table of Counts
 Sample Size for Count Data
 A FourClass Table of Counts
 TwobyTwo Contingency Tables
 Using Loglinear Models for Simple Contingency Tables
 The Danger of Contingency Tables
 QuasiPoisson and Negative Binomial Models Compared
 A Contingency Table of Intermediate Complexity
 Schoener's Lizards: A Complex Contingency Table
 Plot Methods for Contingency Tables
 16: Proportion Data
 17: Binary Response Variables
 18: Generalized Additive Models

19: MixedEffects Models
 Replication and Pseudoreplication
 The lme and lmer Functions
 Best Linear Unbiased Predictors
 A Designed Experiment with Different Spatial Scales: Split Plots
 Hierarchical Sampling and Variance Components Analysis
 Model Simplification in Hierarchical Sampling
 MixedEffects Models with Temporal Pseudoreplication
 Time Series Analysis in MixedEffects Models
 Random Effects in Designed Experiments
 Regression in MixedEffects Models
 Generalized Linear Mixed Models
 Fixed Effects in Hierarchical Sampling
 Error Plots from a Hierarchical Analysis
 20: Nonlinear Regression
 21: Tree Models
 22: Time Series Analysis
 23: Multivariate Statistics
 24: Spatial Statistics
 25: Survival Analysis
 26: Simulation Models

27: Changing the Look of Graphics
 Graphs for Publication
 Shading
 Logarithmic Axes
 Axis Labels Containing Subscripts and Superscripts
 Different font families for text
 Mathematical Symbols on Plots
 Phase Planes
 Fat Arrows
 Trellis Plots
 ThreeDimensional Plots
 Complex 3D plots with wireframe
 An Alphabetical Tour of the Graphics Parameters
 References and Further Reading
 Index
Product information
 Title: The R Book
 Author(s):
 Release date: June 2007
 Publisher(s): Wiley
 ISBN: 9780470510247
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