A Handbook of Statistical Analyses Using R, Second Edition
Chapman & Hall/CRC (Verlag)
978-1-4200-7933-3 (ISBN)
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A Proven Guide for Easily Using R to Effectively Analyze Data
Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.
New to the Second Edition
New chapters on graphical displays, generalized additive models, and simultaneous inference
A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution
New examples and additional exercises in several chapters
A new version of the HSAUR package (HSAUR2), which is available from CRAN
This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.
Brian S. Everitt is Professor Emeritus at King’s College, University of London. Torsten Hothorn is Professor of Biostatistics in the Institut für Statistik at Ludwig-Maximilians-Universität München.
An Introduction to R
What Is R?
Installing R
Help and Documentation
Data Objects in R
Data Import and Export
Basic Data Manipulation
Computing with Data
Organizing an Analysis
Data Analysis Using Graphical Displays
Introduction
Initial Data Analysis
Analysis Using R
Simple Inference
Introduction
Statistical Tests
Analysis Using R
Conditional Inference
Introduction
Conditional Test Procedures
Analysis Using R
Analysis of Variance
Introduction
Analysis of Variance
Analysis Using R
Simple and Multiple Linear Regression
Introduction
Simple Linear Regression
Multiple Linear Regression
Analysis Using R
Logistic Regression and Generalized Linear Models
Introduction
Logistic Regression and Generalized Linear Models
Analysis Using R
Density Estimation
Introduction
Density Estimation
Analysis Using R
Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Scatterplot Smoothers and Generalized Additive Models
Introduction
Scatterplot Smoothers and Generalized Additive Models
Analysis Using R
Survival Analysis
Introduction
Survival Analysis
Analysis Using R
Analyzing Longitudinal Data I
Introduction
Analyzing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Analyzing Longitudinal Data II
Introduction
Methods for Nonnormal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects
Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R
Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R
Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R
Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R
Bibliography
Index
A Summary appears at the end of each chapter.
| Erscheint lt. Verlag | 22.7.2009 |
|---|---|
| Zusatzinfo | 200+; 69 Tables, black and white; 135 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 235 mm |
| Gewicht | 522 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| ISBN-10 | 1-4200-7933-6 / 1420079336 |
| ISBN-13 | 978-1-4200-7933-3 / 9781420079333 |
| Zustand | Neuware |
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