Analyzing medical data using S-PLUS应用S-PLUS的医学数据分析
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作者： Brian Everitt，Sophia Rabe-Hesketh著
出 版 社：
出版时间： 2001-9-1字数：版次：页数： 485印刷时间： 2001/09/01开本： 16开印次：纸张： 胶版纸I S B N ： 9780387988627包装： 精装内容简介
This book covers a range of statistical methods useful in the analysis of medical data, from the simple to the sophisticated, and shows how they may be applied using the latest versions of S-PLUS and S-PLUS 6. In each chapter several sets of medical data are explored and analysed using a mixture of graphical and model fitting approaches. At the end of each chapter the S-PLUS script files are listed, enabling readers to reproduce all the analyses and graphics in the chapter. These script files can be downloaded from a web site. The aim of the book is to show how to use S-PLUS as a powerful environment for undertaking a variety of statistical analyses from simple inference to complex model fitting, and for providing informative graphics. All such methods are of increasing importance in handling data from a variety of medical investigations including epidemiological studies and clinical trials. The mix of real data examples and background theory make this book useful for students and researchers alike. For the former, exercises are provided at the end of each chapter to increase their fluency in using the command line language of the S-PLUS software. Professor Brian Everitt is Head of the Department of Biostatistics and Computing at the Institute of Psychiatry in London and Sophia Rabe-Hesketh is a senior lecturer in the same department. Professor Everitt is the author of over 30 books on statistics including two previously co-authored with Dr. Rabe-Hesketh.
目录
1An Introduction to S-PLUS
2 Describing Data
3 Basic Inference
4 Scatterplots,Simple Regression and Smoothing
5 Analysis of ariance and Covariance
6 The Analysis of Longitudinal Data
7 More Graphics
8 Multiple Linear Regression
9 Generalized Linear Models I:Logistic Regression
10 Generalised linear models II:Poisson regression
11 Linear Mixed Models I
12 Linear Mixed Models II
13 Generalized Additive Models
14 Nonlinear models
15 Regression trees
16 Survival Analysis I
17 Survival Analysis II:Cox's Regression
18 Principal Components and Factor Analysis
19 Cluster Analysis
20 Discriminant Function Analysis
A The S-PLUS GUI
B Answers to selected exerciese
References
Index
