A Handbook of Statistical Analyses Using R, Second Edition / Edition 2

A Handbook of Statistical Analyses Using R, Second Edition / Edition 2

by Torsten Hothorn, Brian S. Everitt
     
 

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

See more details below

Overview

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.

Read More

Product Details

ISBN-13:
9781420079333
Publisher:
Taylor & Francis
Publication date:
07/20/2009
Edition description:
Older Edition
Pages:
376
Sales rank:
1,079,733
Product dimensions:
6.00(w) x 9.20(h) x 0.90(d)

Table of Contents

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.

Read More

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >