A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.
Modern Applied Statistics with S (Statistics and Computing)
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Editorial Reviews
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"Modern Applied Statistics With S meets its goal of serving as an introduction to S for new users, as well as a reference and resource for those with more S experience." Journal of the American Statistical Association, December 2005
From the Back Cover
S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R 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, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs,
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48 of 48 people found the following review helpful:
5.0 out of 5 stars
Excellent,
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This review is from: Modern Applied Statistics with S (Hardcover)
A worthy update. The authors are matter-of-fact and straightforward. I appreciate their terse style, the broad coverage, and the many examples. It's also good that they're starting to split programming material off into its own monograph (S Programming), making this book all the more appropriate for learning, although not quite beginning, statisticians. Their considerable contribution in software is also very much appreciated.I do not agree at all with the reviewer who chided them for including R; I say so much the better for it. I very much hope that they will continue to do so.
32 of 32 people found the following review helpful:
5.0 out of 5 stars
R deserves the coverage it gets here,
By Weary Traveller "swampstomper" (Enschede, OV Netherlands) - See all my reviews
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This review is from: Modern Applied Statistics with S (Hardcover)
Another reviewer wrote "I suspect most practicioners use S+". He should have been at the UserR! 2004 conference in Vienna this past March, with 500 or so enthusiastic R users including many from big industry (financial, pharmaceutical). And Ripley is the number-one contributor to the R Help mailing list by a long way. So it is completely appropriate that R is so prominant. Many of us appreciate open source not only for its cost ($0) but also its transparency. The reviewer should take another look at R.
As for the book, it is my data anlysis bible. It gets me started in a correct direction, with very well-explained and worked out examples, which I then adapt to my own datasets. The writing couldn't be clearer, and the references to primary sources as well as non-computational statistics texts I have found to be excellent. This is the one book to own if you are more than a beginner.
25 of 25 people found the following review helpful:
5.0 out of 5 stars
Book Contents,
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This review is from: Modern Applied Statistics with S (Hardcover)
The "search inside this book" feature was not available when this review was posted. Hope it helps.
CONTENTS Introduction Data Manipulation The S Language Graphics Univariate Statistics Linear Statistical Models Generalized Linear Models Non-Linear and Smooth Regression Tree-Based Methods Random and Mixed Effects Exploratory Multivariate Analysis Classification Survival Analysis Time Series Analysis Spatial Statistics Optimization Implementation-Specific Details The S-PLUS GUI Datasets, Software and Libraries References Index
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Inside This Book
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First Sentence:
Statistics is fundamentally concerned with the understanding of structure in data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
fitted model object, dist climb, coef exp, crabs data, conditioning plots, previous duration, contrast matrices, cumulative periodogram, residual deviance, character vector, martingale residuals, model formulae, recycling rule, gaussian family, numeric vector, postscript device, deviance residuals, motif device, galaxies data, generalized linear mixed models, data frame, survivor curves, cpus data, autocorrelation plots, predict method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Std, Knock Hill, Bens of Jura, Old Faithful, Step Df Deviance Resid, Smoothed Periodogram, Time Series Analysis, Estimate Std, Level Normal Basic, Null Deviance, Quantiles of Standard Normal Figure, Tetrahydrocortisone Tetrahydrocortisone Figure, Analytical Methods Committee, Level Percentile, New South Wales, Del Piombo, Del Sarto, Setosa Versicolor Virginica Sepal New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Statistics is fundamentally concerned with the understanding of structure in data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
fitted model object, dist climb, coef exp, crabs data, conditioning plots, previous duration, contrast matrices, cumulative periodogram, residual deviance, character vector, martingale residuals, model formulae, recycling rule, gaussian family, numeric vector, postscript device, deviance residuals, motif device, galaxies data, generalized linear mixed models, data frame, survivor curves, cpus data, autocorrelation plots, predict method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Std, Knock Hill, Bens of Jura, Old Faithful, Step Df Deviance Resid, Smoothed Periodogram, Time Series Analysis, Estimate Std, Level Normal Basic, Null Deviance, Quantiles of Standard Normal Figure, Tetrahydrocortisone Tetrahydrocortisone Figure, Analytical Methods Committee, Level Percentile, New South Wales, Del Piombo, Del Sarto, Setosa Versicolor Virginica Sepal New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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- New Zealand English: Its Origins and Evolution (Studies in English Language) by Elizabeth Gordon in Back Matter
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