A Handbook of Statistical Analyses Using R, Second Edition Book

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.