A Handbook of Statistical Analyses Using R

TL;DR: An Introduction to R What Is R?

Abstract: 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.