Book description
The high-level 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 cutting-edge 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 chi-square and t-test.
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
- Built-in 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
- Built-in Data Files
- Reading Data from Files with Non-standard 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 model-fitting 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
- Quasi-likelihood
- Generalized Additive Models
- Offsets
- Residuals
- Misspecified Error Structure
- Misspecified Link Function
- Overdispersion
- Bootstrapping a GLM
- 14: Count Data
-
15: Count Data in Tables
- A Two-Class Table of Counts
- Sample Size for Count Data
- A Four-Class Table of Counts
- Two-by-Two Contingency Tables
- Using Log-linear Models for Simple Contingency Tables
- The Danger of Contingency Tables
- Quasi-Poisson 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: Mixed-Effects 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
- Mixed-Effects Models with Temporal Pseudoreplication
- Time Series Analysis in Mixed-Effects Models
- Random Effects in Designed Experiments
- Regression in Mixed-Effects Models
- Generalized Linear Mixed Models
- Fixed Effects in Hierarchical Sampling
- Error Plots from a Hierarchical Analysis
- 20: Non-linear 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
- Three-Dimensional 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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