R in a Nutshell: A Desktop Quick Reference Review

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(More customer reviews)'R in a Nutshell' is the essential introductory book on R. Do not try to learn R without it.
I made two attempts to learn R before purchasing this book. In both previous attempts, I had to abort and use another tool to solve my problem because it was taking me too long to accomplish very simple things in R.
The reason R is hard to learn is that its documentation is organized for statisticians that already know R, but have forgotten a detail or two. There are a few other books on learning R, but they are setup like a college course - complete the entire book and THEN you can actually accomplish something.
R in a Nutshell allows you to get working immediately. Simply lookup what you need to do. The firsts thing I did was load a file and make a histogram. I found that stuff in the section on "Loading Data" and the section on charts. In no time I was making stacked area charts for cohorts. Now R is an essential tool for me - and I haven't even taken the time to learn it well! With this book, I don't have to. I can learn as I go. So I actually use R.
Do not R without it.

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What people are saying about R in a Nutshell

"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University

"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks." --David M. Smith, Editor of the "Revolutions" blog at REvolution Computing

Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.

Understand the basics of the language, including the nature of R objects
Learn how to write R functions and build your own packages
Work with data through visualization, statistical analysis, and other methods
Explore the wealth of packages contributed by the R community
Become familiar with the lattice graphics package for high-level data visualization
Learn about bioinformatics packages provided by Bioconductor

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R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R) Review

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(More customer reviews)Skip this paragraph if you know about R and Excel.... R is a powerful programming language that is useful for generating scientific graphics (both simple and extremely complex) and for statistical work. Unfortunately it has a steep learning curve and many say the help files are not particularly helpful for novices. Excel has a user friendly system for entering data and doing basic graphics but has relatively very limited tools for statistics or complex scientific graphics. Combining the strengths of the two is the goal of this book and the free software that goes with it.
With the tools described in this book the user can point and click their way to common analyses and graphics inside of Excel without having to learn to write code in the R programming language. Both the software and book are good but not great because they do not add much to the existing tools for R.
Years ago, John Fox wrote a point-and-click code-generating add-on package for R called R Commander that revolutionized the usability of R. Inside of the R programming environment you can download the Rcmdr package and type library(Rcmdr) and get practically all the same functionality as the tools provided here. What the authors of this book do is bring the functionality of Rcmdr into a Excel as an add-on to the Excel 2003 menus or 2007 ribbon. The implementation is surprisingly smooth (including adding nice right-click menu items) and bug free.
The book itself is mostly nicely rendered color pictures. Think of it as a very well annotated PowerPoint presentation. You will be able to quickly page through it and it is well indexed. The less than 10 page appendix which explains how to install the R packages and required services (or how to install from scratch) is probably the most useful part of the book. The authors do not focus much on the "behind the scenes" strengths of their work which allow you to recalculate and pass information into and out of R "on the fly." However, they do include a few worksheets that demonstrate the ability to pass information into R and return graphics effortlessly. Think of this as the ability to add sliders and push buttons to Excel and have instantly updated high quality graphics. Unfortunately, they only give one example where they use there new RApply function to return a calculated value from R into Excel. There clearly is a lot of functionality but the book does not explain it.
If you are just starting with R this book will probably be a HUGE help to you because it saves you from memorizing a lot of code and it will help you learn how to write code by showing you the commands that were used to generate analyses and graphics. However, if you have experience with R and Rcmdr you probably want to save your money.

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In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.

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Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Review

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(More customer reviews)A survey of remarkable breadth, listing hundreds of sources: 50-100 items per chapter, majority of them from conference proceedings. Evidently, this kind of volume does not let the author discuss any particular paper or topic in detail; at best, a reference is covered with a short paragraph. This does create a problem: the book's value is exhausted once the reader looks up the topic of interest, and moves on to the suggested references. "Temporal data mining" could do with a little more editing, but I am quite impressed with it the way it is, an authoritative and wide-ranging introduction to an interesting topic.

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Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining.Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/

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New Introduction to Multiple Time Series Analysis Review

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(More customer reviews)If you are looking for a book on VARs and cointegration, this is it.
Very clearly written, and with numerical applications of every new concept (so that you can check the accuracy of your codes ...)
Its a significantly improved version of the last edition.
Highly recommended.


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This is the new and totally revised edition of Lütkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

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Statistical Modeling: A Fresh Approach Review

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(More customer reviews)I have a couple of shelves full of introductory statistics books, but for various reasons none have ever seemed suitable for my undergraduate biology/ecology students. Daniel Kaplan has now given me a book I can confidently recommend.
This book, as the title suggest, focusses on a modeling/regression approach but still includes an appropriate discussion on null hypothesis testing and anova. This discussion comes near the end of the book once the modeling approach is clearly understood. It could easily be used as a first statistics book (which is how I would use it), but it could also follow a more traditional null hypothesis based first course.
The writing style is clear and easily followed, with just enough explanation to understand why something is being done, but without the mathematical notation that scares so many students away. A geometrical approach is taken to explain some of the more theoretical aspects. It's also just long enough to be useful, without intimidating students with an enormous number of pages. Chapters are generally short and there is a nice logical flow as you work through the book. The practical examples use R, are well thought out, and great care is taken to explain the output.
Probably not the book for an undergraduate statistician, but for other disciplines that need a good understanding of not only statistical practice, but also statistical thinking, I have yet to come across anything better. Indeed I am so impressed with this book that I felt compelled to write my first ever Amazon review.

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"Statistical Modeling: A Fresh Approach" introduces and illuminates the statistical reasoning used in modern research throughout the natural and social sciences, medicine, government, and commerce.It emphasizes the use of models to untangle and quantify variation in observed data.By a deft and concise use of computing coupled with an innovative geometrical presentation of the relationship among variables,"A Fresh Approach" reveals the logic of statistical inference and empowers the reader to use and understand techniques such as analysis of covariance that are widely used in published research but hardly ever found in introductory texts.Recognizing the essential role the computer plays in modern statistics,{\em A Fresh Approach}provides a complete and self-contained introduction to statistical computing using the powerful (and free) statistics package R.Exercises, software and datasets for the book are available at www.macalester.edu/~kaplan/ISM.

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Mixed Effects Models and Extensions in Ecology with R Review

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(More customer reviews)Many applications in ecology clearly are not amenable to use of the general linear model due to violations of its assumptions. In fact, in most projects I work on, things like correlation among the errors, nonconstant error variance, etc., are the rule, rather than the exception. If you are looking for an applied text dealing with these types of situations with lots of examples, and demonstrations on analysis in R, then you should get this book. It does not delve into theory; there are plenty of other textbooks where you can fill in those details if you are interested. Rather, this book would be ideally suited for quantitative ecologists, biometricians, and statistical consultants who work in life sciences. Another nice thing is that the book does not assume you are an "R expert". Well done.

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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

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Modeling Financial Time Series with S-PLUS® Review

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(More customer reviews)This is an excellent book on financial econometrics, very practical yet rigorous. I wish all econometrics/statistics textbook could like this. Basic theory followed by practical examples - real life examples, not simplified ones like in other books. The authors gave detailed instructions on how to implement various econometric models, i.e. multi-factor models, GARCH, MGARCH, long memory models, state-space, etc. Most econometrics textbooks are at two extremes, they are either too theoretical (you still don't know how to put those models in real life), or too simple (lack of mathematical rigor and without advanced applications). This book is a combination of both worlds, computer codes/math models, and real life examples (some really good ones). A lot of cutting-edge techniques and advanced topics are also covered.

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This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance.Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.

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Cognitive Modeling Review

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(More customer reviews)This book is quite easy to follow and describes the technical and practical details how to actually do cognitive modeling!
Highly recommended for people who are interested in cognitive modeling but don't know where to start.

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Cognitive Modeling is the first book to provide students with an easy-to understand introduction to the basic methods used to build and test cognitive models. Authors Jerome R. Busemeyer and Adele Diederich answer many of the questions that researchers face when beginning work on cognitive models, such as the following: What makes a cognitive model different from conceptual or statistical models? How do you develop such a model? How can you derive qualitatively different predictions between two cognitive models? Focusing on a few key representations, the authors introduce a basic problem in each chapter, illustrate the concept with three examples, and end with a summary of general principles, making this book by far the most accessible cognitive modeling book on the market. Key Features

Emphasizes modeling by presenting the tools needed to build a cognitive model, rather than simply reviewing existing models of cognition
Provides tutorial presentations of psychological, mathematical, statistical, and computational methods used in all areas of cognitive modeling
Includes detailed examples applied to real cognitive models published in the literature in a variety of areas, including recognition, categorization, decision making, and learning
Stresses the importance of designing the right conditions for evaluating models
Addresses the issues of individual differences in cognitive modeling head-on

Cognitive Modeling is ideal for students and researchers across the various domains of cognitive sciences, including perception, learning, decision making, and inference. It is intended for use in upper-level undergraduate and graduate courses such as Cognition/Cognitive Modeling, Cognitive Science, Cognitive Psychology, Quantitative Methods, and Mathematical Modeling in Psychology.

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Introduction to Time Series Modeling (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)Here is the table of contents from the CRC site.
Introduction and Preparatory Analysis
Time Series Data
Classification of Time Series
Objectives of Time Series Analysis
Preprocessing of Time Series
Organization of This Book
The Covariance Function
The Distribution of Time Series and Stationarity
The Autocovariance Function of Stationary Time Series
Estimation of the Autocovariance Function
Multivariate Time Series and Scatterplots
Cross-Covariance Function and Cross-Correlation Function
The Power Spectrum and the Periodogram
The Power Spectrum
The Periodogram
Averaging and Smoothing of the Periodogram
Computational Method of Periodogram
Computation of the Periodogram by Fast Fourier Transform
Statistical Modeling
Probability Distributions and Statistical Models
K-L Information and the Entropy Maximization Principle
Estimation of the K-L Information and Log-Likelihood
Estimation of Parameters by the Maximum Likelihood Method
Akaike Information Criterion (AIC)
Transformation of Data
The Least Squares Method
Regression Models and the Least Squares Method
Householder Transformation Method
Selection of Order by AIC
Addition of Data and Successive Householder Reduction
Variable Selection by AIC
Analysis of Time Series Using ARMA Models
ARMA Model
The Impulse Response Function
The Autocovariance Function
The Relation between AR Coefficients and the PARCOR
The Power Spectrum of the ARMA Process
The Characteristic Equation
The Multivariate AR Model
Estimation of an AR Model
Fitting an AR Model
Yule-Walker Method and Levinson's Algorithm
Estimation of an AR Model by the Least Squares Method
Estimation of an AR Model by the PARCOR Method
Large Sample Distribution of the Estimates
Yule-Walker Method for MAR Model
Least Squares Method for MAR Model
The Locally Stationary AR Model
Locally Stationary AR Model
Automatic Partitioning of the Time Interval
Precise Estimation of a Change Point
Analysis of Time Series with a State-Space Model
The State-Space Model
State Estimation via the Kalman Filter
Smoothing Algorithms
Increasing Horizon Prediction of the State
Prediction of Time Series
Likelihood Computation and Parameter Estimation for a Time Series Model
Interpolation of Missing Observations
Estimation of the ARMA Model
State-Space Representation of the ARMA Model
Initial State of an ARMA Model
Maximum Likelihood Estimate of an ARMA Model
Initial Estimates of Parameters
Estimation of Trends
The Polynomial Trend Model
Trend Component Model--Model for Probabilistic Structural Changes
Trend Model
The Seasonal Adjustment Model
Seasonal Component Model
Standard Seasonal Adjustment Model
Decomposition Including an AR Component
Decomposition Including a Trading-Day Effect
Time-Varying Coefficient AR Model
Time-Varying Variance Model
Time-Varying Coefficient AR Model
Estimation of the Time-Varying Spectrum
The Assumption on System Noise for the Time-Varying Coefficient AR Model
Abrupt Changes of Coefficients
Non-Gaussian State-Space Model
Necessity of Non-Gaussian Models
Non-Gaussian State-Space Models and State Estimation
Numerical Computation of the State Estimation Formula
Non-Gaussian Trend Model
A Time-Varying Variance Model
Applications of Non-Gaussian State-Space Model
The Sequential Monte Carlo Filter
The Nonlinear Non-Gaussian State-Space Model and Approximations of Distributions
Monte Carlo Filter
Monte Carlo Smoothing Method
Nonlinear Smoothing
Simulation
Generation of Uniform Random Numbers
Generation of Gaussian White Noise
Simulation Using a State-Space Model
Simulation with Non-Gaussian Model
Appendix A: Algorithms for Nonlinear Optimization
Appendix B: Derivation of Levinson's Algorithm
Appendix C: Derivation of the Kalman Filter and Smoother Algorithms
Appendix D: Algorithm for the Monte Carlo Filter
Bibliography

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In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the various tools for handling them.The book employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods, including the Kalman filter, the non-Gaussian filter, and the sequential Monte Carlo filter, for the state-space models. Taking a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike, the author derives various methods of parameter estimation, such as the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by the Akaike information criterion (AIC). Along with simulation methods, he also covers standard stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model.With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It encourages readers to build models for their own real-life problems.

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Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities Review

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(More customer reviews)Royle & Dorazio (2008): A truly synthetic overview
This book not only illustrates, and presents R and WinBUGS code for, plenty of methods for inference about distribution and abundance in animal and plant populations and communities; it does much more. It presents a truly synthetic overview of these methods and makes the reader understand how they relate to each other. At the same time, the authors succeed extremely well in teaching a modern, "organic way" of statistical modeling -- where one first thinks hard about how the observed data might have arisen via a combination of stochastic processes (the book is about hierarchical models, remember) and then builds a custom statistical model for exactly those processes. This combination of presenting a unifying synthesis of a vast array of methods and showing how to model a study system organically in my view is unique among the currently available statistical ecology books.

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A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)* Development of classical, likelihood-based procedures for inference, as well asBayesian methods of analysis* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS* Computing support in technical appendices in an online companion web site

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Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics) Review

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(More customer reviews)Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.
The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.
Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.
A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).


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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

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