Modern Applied Statistics with S-PLUSS-PLUS is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas which have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S-PLUS to perform statistical analyses and provides both an introduction to the use of S-PLUS and a course in modern statistical methods. S-PLUS is available for both Windows and UNIX workstations, and both versions are covered in depth. The aim of the book is to show how to use S-PLUS as a powerful and graphical system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS, and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state-of-the-art approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification. This second edition is intended for users of S-PLUS 3.3, 4.0, or later. It covers the recent developments in graphics and new statistical functionality, including bootstraping, mixed effects, linear and non-linear models, factor analysis, and regression with autocorrelated errors. The material on S-PLUS programming has been re-written to explain the full story behind the object-oriented programming features. The authors have written several software libraries which enhance S-PLUS; these and all the datasets used are available on the Internet in versions for Windows and UNIX. There are also on-line complements covering advanced material, further exercises and new features of S-PLUS as they are introduced. Dr. Venables is Head of Department and Senior Lecturer at the Department of |
Contents
4 | |
8 | |
13 | |
18 | |
Graphical Output | 70 |
Distributions and Data Summaries | 164 |
Linear Statistical Models | 191 |
Generalized Linear Models | 224 |
Survival Analysis | 343 |
73 | 345 |
Time Series | 442 |
Spatial Statistics | 472 |
Classification | 493 |
B Common SPLUS Functions | 499 |
Using SPLUS Libraries | 517 |
533 | |
Nonlinear Models | 274 |
Random and Mixed Effects | 297 |
69 | 302 |
Modern Regression | 324 |
538 | |
539 | |
545 | |
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Common terms and phrases
algorithm allows analysis anova argument array binomial boxplot calculations character string character vector cluster coef coefficients columns command components compute consider covariance covariance matrix data frame dataset default degrees of freedom density deviance dist distribution estimate evaluated example factor fitted model function function(x give graphics device graphics parameters histogram integer Intercept iterative labels layout length levels likelihood linear model linear regression lines M-estimators mean median method missing values model formula mydata names non-linear non-linear regression normal Note NULL object operations options output p-value plot region points predict Pregnanetriol Q-Q plot QR decomposition quantile regression residuals result returns S-PLUS sample scale scatterplot search path Section selected smooth specified splines squares standard error statistical subset Tetrahydrocortisone Trellis ttest Unix Value Std variables variance vector weight Windows zero