Riassunto:
R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields.
Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R's strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive.
A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.
Contenuti:
AN INTRODUCTION TO R
What Is R?
Installing R
Help and Documentation
Data Objects in R
Data Import and Export
Basic Data Manipulation
Simple Summary Statistics
Organising an Analysis
Summary
SIMPLE INFERENCE
Introduction
Statistical Tests
Analysis Using R
Summary
CONDITIONAL INFERENCE
Introduction
Conditional Test Procedures
Analysis Using R
Summary
ANALYSIS OF VARIANCE
Introduction
Analysis of Variance
Analysis Using R
Summary
MULTIPLE LINEAR REGRESSION
Introduction
Multiple Linear Regression
Analysis Using R
Summary
LOGISTIC REGRESSION AND GENERALISED LINEAR MODELS
Introduction
Logistic Regression and Generalised Linear Models
Analysis Using R
Summary
DENSITY ESTIMATION
Introduction
Density Estimation
Analysis Using R
Summary
RECURSIVE PARTITIONING
Introduction
Recursive Partitioning
Analysis Using R
Summary
SURVIVAL ANALYSIS
Introduction
Survival Analysis
Analysis Using R
Summary
ANALYSING LONGITUDINAL DATA I
Introduction
Analysing Longitudinal Data
Linear Mixed Effects models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Summary
ANALYSING LONGITUDINAL DATA II
Introduction
Generalised Estimating Equations
Analysis Using R
Summary
META-ANALYSIS
Introduction
Systematic Reviews and Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Summary
PRINCIPAL COMPONENT ANALYSIS
Introduction
Principal Component Analysis
Analysis Using R
Summary
MULTIDIMENSIONAL SCALING
Introduction
Multidimensional Scaling
Analysis Using R
Summary
CLUSTER ANALYSIS
Introduction
Cluster Analysis
Analysis Using R
Summary
BIBLIOGRAPHY
INDEX
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