Handbook of statistical analyses using R

Alpha 1040/02 Jan 15
Rs.4023/-

Table of Contents

Introduction
Density Estimation
Analysis Using R
Summary of Findings
Final Comments

Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Summary of Findings
Final Comments 　　　

Scatterplot Smoothers and Additive Models
Introduction 　　　
Scatterplot Smoothers and Generalised Additive Models 　　　
Analysis Using R 　　　
Summary of Findings
Final Comments

Survival Analysis
Introduction 　　　
Survival Analysis 　　　
Analysis Using R 　　　
Summary of Findings
Final Comments 　　　

Quantile Regression
Introduction 　　　
Quantile Regression 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments 　　　

Analysing Longitudinal Data I
Introduction 　　　
Analysing Longitudinal Data 　　　
Linear Mixed Effects Models 　　　
Analysis Using R 　　　
Prediction of Random Effects 　　　
The Problem of Dropouts 　　　
Summary of Findings 　　　
Final Comments

Analysing Longitudinal Data II
Introduction 　　　
Methods for Non-Normal Distributions 　　　
Analysis Using R: GEE 　　　
Analysis Using R: Random Effects
Summary of Findings 　　　
Final Comments 　　　

Simultaneous Inference and Multiple Comparisons
Introduction 　　　
Simultaneous Inference and Multiple Comparisons 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments

Missing Values
Introduction 　　　
The Problems of Missing Data 　　　
Dealing with Missing Values 　　　
Imputing Missing Values 　　　
Analyzing Multiply Imputed Data 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments

Meta-Analysis
Introduction 　　　
Systematic Reviews and Meta-Analysis 　　　
Statistics of Meta-Analysis 　　　
Analysis Using R 　　　
Meta-Regression 　　　
Publication Bias 　　　
Summary of Findings 　　　
Final Comments 　　　

Bayesian Inference
Introduction 　　　
Bayesian Inference 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments 　　　

Principal Component Analysis
Introduction 　　　
Principal Component Analysis 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments

Multidimensional Scaling
Introduction 　　　
Multidimensional Scaling 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments 　　　

Cluster Analysis
Introduction 　　　
Cluster Analysis 　　　
Analysis Using R 　　　
Summary of Findings 　　　
Final Comments

Bibliography

Index

Features

Presents straightforward descriptions of how to use R and interpret the results
Gives an introduction to R for novices, covering basic concepts and common data manipulation techniques
Shows how to obtain informative graphical output by applying the appropriate R functions
Includes more examples and exercises that encourage hands-on practice with R
Offers the data, R code, and lecture slides in the HSAUR3 package available from CRAN
Summary

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.

New to the Third Edition

Three new chapters on quantile regression, missing values, and Bayesian inference
Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
Additional exercises
More detailed explanations of R code
New section in each chapter summarizing the results of the analyses
Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.