Modern Applied Statistics with SSPLUS 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 SPLUS to perform statistical analyses and provides both an introduction to the use of SPLUS and a course in modern statistical methods. SPLUS 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 SPLUS as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book in intended for wouldbe users of SPLUS 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 stateoftheart approaches to topics such as linear, nonlinear, and smooth regression models, treebased methods, multivariate analysis and pattern recognition, survival analysis, time series and spatial statistics. Throughout, modern techniques such as robust methods, nonparametric smoothing, and bootstrapping are used where appropriate. This third edition is intended for users of SPLUS 4.5, 5.0, 2000 or later, although SPLUS 3.3/4 are also considered. The major change from the second edition is coverage of the current versions of SPLUS. The material has been extensively rewritten using new examples and the latest computationally intensive methods. The companion volume on S Programming will provide an indepth guide for those writing software in the S language. The authors have written several software libraries that enhance SPLUS; these and all the datasets used are available on the Internet in versions for Windows and UNIX. There are extensive online complements covering advanced material, usercontributed extensions, further exercises, and new features of SPLUS as they are introduced. Dr. Venables is now Statistician with CSRIO in Queensland, having been at the Department of Statistics, University of Adelaide, for many years previously. He has given many short courses on SPLUS in Australia, Europe, and the USA. Professor Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition, and neural networks. 
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A good overview of what you can do with R and S+.
It won't be sufficient if you don't already know your statistics: but there are plenty of references for further reading.
Review: Modern Applied Statistics with S
User Review  Barry  GoodreadsI think this was the first book I bought on R, and it's certainly the most wellthumbed. The graphing examples are really quite good, but it's a really hard slog if you're new to R. Once you've gotten familiar, though, check it out if you're doing a lot of data visualization work. Read full review
Contents
Introduction  1 
11 A Quick Overview of S  3 
12 Using S  5 
13 An Introductory Session  6 
14 What Next?  12 
Data Manipulation  13 
22 Connections  20 
23 Data Manipulation  27 
88 Additive Models  232 
89 ProjectionPursuit Regression  238 
810 Neural Networks  243 
811 Conclusions  249 
TreeBased Methods  251 
91 Partitioning Methods  253 
92 Implementation in rpart  258 
93 Implementation in tree  266 
24 Tables and CrossClassification  37 
The S Language  41 
32 More on S Objects  44 
33 Arithmetical Expressions  47 
34 Character Vector Operations  51 
35 Formatting and Printing  54 
36 Calling Conventions for Functions  55 
37 Model Formulae  56 
38 Control Structures  58 
39 Array and Matrix Operations  60 
310 Introduction to Classes and Methods  66 
Graphics  69 
41 Graphics Devices  71 
42 Basic Plotting Functions  72 
43 Enhancing Plots  77 
44 Fine Control of Graphics  82 
45 Trellis Graphics  89 
Univariate Statistics  107 
52 Generating Random Data  110 
53 Data Summaries  111 
54 Classical Univariate Statistics  115 
55 Robust Summaries  119 
56 Density Estimation  126 
57 Bootstrap and Permutation Methods  133 
Linear Statistical Models  139 
62 Model Formulae and Model Matrices  144 
63 Regression Diagnostics  151 
64 Safe Prediction  155 
65 Robust and Resistant Regression  156 
66 Bootstrapping Linear Models  163 
67 Factorial Designs and Designed Experiments  165 
68 An Unbalanced FourWay Layout  169 
69 Predicting Computer Performance  177 
610 Multiple Comparisons  178 
Generalized Linear Models  183 
71 Functions for Generalized Linear Modelling  187 
72 Binomial Data  190 
73 Poisson and Multinomial Models  199 
74 A Negative Binomial Family  206 
75 OverDispersion in Binomial and Poisson GLMs  208 
NonLinear and Smooth Regression  211 
82 Fitting NonLinear Regression Models  212 
83 NonLinear Fitted Model Objects and Method Functions  217 
84 Confidence Intervals for Parameters  220 
85 Profiles  226 
86 Constrained NonLinear Regression  227 
87 OneDimensional CurveFitting  228 
Random and Mixed Effects  271 
101 Linear Models  272 
102 Classic Nested Designs  279 
103 NonLinear Mixed Effects Models  286 
104 Generalized Linear Mixed Models  292 
105 GEE Models  299 
Exploratory Multivariate Analysis  301 
111 Visualization Methods  302 
112 Cluster Analysis  315 
113 Factor Analysis  321 
114 Discrete Multivariate Analysis  325 
Classification  331 
122 Classification Theory  338 
123 NonParametric Rules  341 
124 Neural Networks  342 
125 Support Vector Machines  344 
126 Forensic Glass Example  346 
127 Calibration Plots  349 
Survival Analysis  353 
131 Estimators of Survivor Curves  355 
132 Parametric Models  359 
133 Cox Proportional Hazards Model  365 
134 Further Examples  371 
Time Series Analysis  387 
141 SecondOrder Summaries  389 
142 ARIMA Models  397 
143 Seasonality  403 
144 Nottingham Temperature Data  406 
145 Regression with Autocorrelated Errors  411 
146 Models for Financial Series  414 
Spatial Statistics  419 
152 Kriging  425 
153 Point Process Analysis  430 
Optimization  435 
162 SpecialPurpose Optimization Functions  436 
ImplementationSpecific Details  447 
A2 Using SPLUS under Windows  450 
A3 Using R under Unix Linux  453 
A4 Using R under Windows  454 
A5 For Emacs Users  455 
The SPLUS GUI  457 
Datasets Software and Libraries  461 
C2 Using Libraries  462 
References  465 
481  
Common terms and phrases
algorithm analysis approximate argument bandwidth binomial bootstrap calculate Chapter character vector clusters Coefficients column compute confidence intervals consider covariance data frame dataset default degrees of freedom deviance device distribution Error t value example factors frequency function function(x Gaussian give GLMs graphics groups Intercept iterative labels levels library section likelihood linear model linear regression loglinear models Mestimators maximum likelihood mean method missing values model formula model matrix multivariate names nonlinear nonlinear regression normal Note object observations options output pvalue parameters periodogram plot points Poisson postscript predict principal components projection pursuit QQ plot quantile random effects residuals result Ripley rpart SPLUS sample scale scatterplot selected shown in Figure smooth specified spline split squares standard error Statistics Tetrahydrocortisone tion tree Trellis UNIX Value Std variables variance weights window WinF xlab ylab zero
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Page 471  Gill, PE, Murray. W.. and Wright. MH (1981). Practical Optimization.