Mathematical and statistical software Books
Taylor & Francis Ltd Reproducible Research with R and RStudio Chapman HallCRC The R Series
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£58.99
Taylor & Francis Ltd Reproducible Research with R and RStudio Chapman HallCRC The R Series
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£147.25
Taylor & Francis Ltd Introduction to Time Series Modeling with Applications in R Chapman HallCRC Monographs on Statistics and Applied Probability
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£114.00
Taylor & Francis Ltd Computer Intensive Methods in Statistics
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£52.24
Taylor & Francis Ltd Computer Intensive Methods in Statistics
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£142.50
Taylor & Francis Ltd R for Conservation and Development Projects
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£58.99
Taylor & Francis Ltd Statistical Programming in SAS
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£68.39
Taylor & Francis Ltd Statistical Programming in SAS
Book SynopsisStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining dataTrade Review"This book is useful for people who want to learn SAS programing, and assumes the students have knowledge of multiple linear regression and one-way ANOVA models.…The second edition has added a chapter on text processing, and reorganized the chapter order…Some topics that are relevant for the SAS Base and Certifications exams are covered, and a nice feature is the highlighting of programing tips in gray." ~Technometrics"This is a very complete book for programming SAS in statistical analyses. This second edition offers the possibility to debug some programs and provides new examples and applications, which are very useful. This book is a very useful companion tool for students or beginners in SAS, or for more experienced statisticians who already use SAS for statistical analyses."~ISCB NewsTable of ContentsContentsPreface ..............................................................................................................................................ixAcknowledgments ...................................................................................................................... xiiiAuthor .............................................................................................................................................xv1. Structuring, Implementing, and Debugging Programs to Learn about Data ...........11.1 Statistical Programming ................................................................................................11.2 Learning from Constructed, Artificial Data ...............................................................2Processing a Particular Data Set—Extracting Variable Names from aColumn of an Input Data Set.........................................................................................2Learning More about Unfamiliar Statistical Methods—Linear MixedEffects Models .................................................................................................................5Improving Your Intuition about Statistical Theory— Sampling Distributionof Means ...........................................................................................................................81.3 Good Programming Practice ...................................................................................... 11Document Your Programs! .......................................................................................... 11Use Meaningful Variable Names ................................................................................ 13Use a Variety of CaSeS in Program Statements ........................................................ 14Indent Program Statements That Naturally Go Together ....................................... 141.4 SAS Program Structure ................................................................................................ 151.5 What Is a SAS Data Set? ............................................................................................... 211.6 Internally Documenting SAS Programs ....................................................................221.7 Basic Debugging ...........................................................................................................231.8 Getting Help ..................................................................................................................27Using Help in SAS ........................................................................................................27Getting Help from a Web Browser Search .................................................................291.9 Exercises .........................................................................................................................292. Reading, Creating, and Formatting Data Sets ................................................................ 312.1 What Does a SAS DATA Step Do? .............................................................................. 312.2 Reading Data from External Files ..............................................................................33Reading Data Directly as Part of a Program—Anyone for Datalines? .................34Reading Data Sets Saved as Text—INFILE Can Be Your Friend (PROCIMPORT Too!) ................................................................................................................38Sometimes, Variables Are in Particular Columns or in Particular Formats .........402.3 Reading CSV, Excel, and TEXT Files .......................................................................... 412.4 Temporary versus Permanent Status of Data Sets ...................................................432.5 Formatting and Labeling Variables ............................................................................46Using Formats to Read and Display Variable Values ..............................................46Internal Representations and Output Displays ........................................................49Character, Numeric, Time, and Date Formats ..........................................................532.6 User-Defined Formatting .............................................................................................58Saving Formats for Later Use ......................................................................................632.7 Recoding and Transforming Variables in a DATA Step ........................................66Indicator Variables ......................................................................................................682.8 Writing Out a File or Making a Simple Report ......................................................73Simple Report Generation .........................................................................................73Exporting a File ...........................................................................................................772.9 Exercises .......................................................................................................................803. Programming a DATA Step ................................................................................................833.1 Writing Programs by Subdividing Tasks ................................................................83Estimate the Probability That a Randomly Selected 30- to 39-Year-OldMale Is Taller than a Randomly Selected Female of the Same Age .....................83Conditional Execution ...........................................................................................84Looping to Repeat a Task ......................................................................................86Returning to the Height Probability Simulation ............................................... 873.2 Ordering How Tasks Are Done ................................................................................90Missing Data in Functions .........................................................................................923.3 Indexable Lists of Variables (Also Known as Arrays) ...........................................93Defining Values in the Variable List .........................................................................93Inputting Values in the Variable List ........................................................................94Reassign Missing Value Codes for Numeric Variables “.” ...................................95Recoding Missing Values for All Numeric and Character Variables ..................953.4 Functions Associated with Statistical Distributions .............................................963.5 Generating Variables Using Random Number Generators ................................ 1023.6 Remembering Variable Values across Observations ........................................... 105Processing Multiple Observations for a Single Observation .............................. 1063.7 Case Study 1: Is the Two-Sample t-Test Robust to Violations of theHeterogeneous Variance Assumption? ................................................................. 109Case Study 1 (Revisited with DATA Step Programming) .................................. 1183.8 Efficiency Considerations—How Long Does It Take? .........................................1223.9 Case Study 2: Monte Carlo Integration to Estimate an Integral ........................ 1233.10 Case Study 3: Simple Percentile-Based Bootstrap ................................................ 1283.11 Case Study 4: Randomization Test for the Equality of Two Populations ......... 1303.12 Exercises ..................................................................................................................... 1344. Combining, Extracting, and Reshaping Data ............................................................... 1374.1 Adding Observations by SET-ing Data Sets.......................................................... 1374.2 Adding Variables by MERGE-ing Data Sets ......................................................... 1404.3 Working with Tables in PROC SQL ....................................................................... 1484.4 Converting Wide to Long Formats ......................................................................... 1614.5 Converting Long to Wide Formats ......................................................................... 1644.6 Case Study: Reshaping a World Bank Data Set .................................................... 1664.7 Building Training and Validation Data Sets ......................................................... 1754.8 Exercises ..................................................................................................................... 1794.9 Self-study Lab ............................................................................................................ 1805. Macro Programming .......................................................................................................... 1915.1 What Is a Macro and Why Would You Use It? ..................................................... 1915.2 Motivation for Macros: Numerical Integration to DetermineP(0 < Z < 1.645) ......................................................................................................... 1915.3 Processing Macros .................................................................................................... 1955.4 Macro Variables, Parameters, and Functions........................................................ 1955.5 Conditional Execution, Looping, and Macros ...................................................... 198More Complicated Macro Variable Construction ................................................203Changing Locations in a Macro during Execution ..............................................2045.6 Debugging Macro Code and Programs.................................................................206Write Out Values of Macro Variables .....................................................................206Useful SAS Options for Debugging Macros ......................................................... 2075.7 Saving Macros ........................................................................................................... 2115.8 Functions and Routines for Macros ....................................................................... 2115.9 Case Study: Macro for Constructing Training and Test Data Set for ModelComparison ............................................................................................................... 2165.10 Case Study: Processing Multiple Data Sets ...........................................................2235.11 Exercises .....................................................................................................................2276. Customizing Output and Generating Data Visualizations .......................................2296.1 Using the Output Delivery System ........................................................................229Basic Ideas ..................................................................................................................229Destinations—RTF, HTML, PDF, and More! .........................................................230What’s Produced and How to Select It ..................................................................235Another Destination That Stat Programmers Should Visit—OUTPUT ............ 2436.2 Graphics in SAS ......................................................................................................... 2496.3 ODS Statistical Graphics ..........................................................................................2506.4 Modifying Graphics Using the ODS Graphics Editor ......................................... 2576.5 Graphing with Styles and Templates .....................................................................2606.6 Statistical Graphics—Entering the Land of SG Procedures ............................... 266SGPLOT ...................................................................................................................... 266SGPANEL ................................................................................................................... 269SGSCATTER .............................................................................................................. 2716.7 Case Study: Using the SG Procedures ................................................................... 2736.8 Enhancing SG Displays—Options with SG Procedure Statements .................. 2796.9 Using Annotate Data Sets to Enhance SG Displays ............................................2846.10 Using Attribute Maps to Enhance SG Displays ................................................... 2876.11 Exercises .....................................................................................................................2907. Processing Text .................................................................................................................... 2937.1 Cleaning and Processing Text Data ....................................................................... 2937.2 Starting with Character Functions ......................................................................... 2937.3 Processing Text .......................................................................................................... 2987.4 Case Study: Sentiment in State of the Union Addresses .....................................3027.5 Case Study: Reading Text from a Web Page .........................................................3097.6 Regular Expressions ................................................................................................. 3157.7 Case Study (Revisited)—Applying Regular Expressions ................................... 3197.8 Exercises ..................................................................................................................... 3218. Programming with Matrices and Vectors ..................................................................... 3238.1 Defining a Matrix and Subscripting ...................................................................... 3238.2 Using Diagonal Matrices and Stacking Matrices ................................................. 3298.3 Using Elementwise Operations, Repeating, and Multiplying Matrices ........... 3328.4 Importing a Data Set into SAS/IML and Exporting Matrices fromSAS/IML to a Data Set .............................................................................................333Creating Matrices from SAS Data Sets and Vice Versa ........................................3338.5 Case Study 1: Monte Carlo Integration to Estimate π ..........................................3368.6 Case Study 2: Bisection Root Finder ...................................................................... 3378.7 Case Study 3: Randomization Test Using Matrices Imported from PROCPLAN ..........................................................................................................................3408.8 Case Study 4: SAS/IML Module to Implement Monte Carlo Integrationto Estimate π ..............................................................................................................3428.9 Storing and Loading SAS/IML Modules ..............................................................3448.10 SAS/IML and R .........................................................................................................3458.11 Exercises .....................................................................................................................350References ...................................................................................................................................355Index ............................................................................................................................................. 357
£166.25
Taylor & Francis Ltd Sharpening Your Advanced SAS Skills
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£58.89
Taylor & Francis Ltd Introductory Adaptive Trial Designs A Practical Guide with R Chapman HallCrc Biostatics
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£58.89
Taylor & Francis Ltd Foundations of Statistical Algorithms
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Taylor & Francis Ltd Practical Statistical Methods
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£47.99
Taylor & Francis Ltd MATLAB with Applications to Engineering Physics and Finance
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Taylor & Francis Ltd Regression Modeling
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£58.89
Taylor & Francis Ltd Engineering ProductionGrade Shiny Apps
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£128.25
Taylor & Francis Ltd Engineering ProductionGrade Shiny Apps
Book SynopsisFrom the Reviews[This book] contains an excellent blend of both Shiny-specific topics and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters.Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact.David Granjon, Sr. Expert Data Science, NovartisPresented in full color, Engineering Production-Grade ShinTrade Review"ThinkR’s book is a gradual and pleasant invitation to the production-ready shiny apps world. It focuses on the unfortunately too often forgotten general principles necessary to be successful in this quest, before exposing a comprehensive and robust workflow powered by the {golem} package. This books fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. Readers will appreciate the number of exclusive references like {shinypsum}, {gargoyle}, {crrry} and {dockerfiler} that will definitely help them to reach the production-ready graal. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact." – David Granjon, Sr. Expert Data Science, Novartis"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."– Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)Table of Contents1. About Successful Shiny Apps. 2. Planning Ahead. 3. Structuring your Project. 4. Introduction to {golem}. 5. The workflow. 6. UX Matters. 7. Don’t rush into coding. 8. Setting up for success with {golem} (#settingupsuccess). 9. Building an “ipsum-app”. 10. Building the app with {golem} 11. Build yourself a safety net. 12. Version Control. 13. Deploy your application. 14. The Need for Optimization. 15. Common Application Caveats. 16. Optimizing {shiny} Code. 17. Using JavaScript. 18. A Gentle Introduction to CSS. Appendix.
£47.49
Taylor & Francis Ltd Introduction to Time Series Modeling with Applications in R
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£43.69
Taylor & Francis Ltd Handbook of Bayesian Variable Selection
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£147.25
Taylor & Francis Ltd R Markdown Cookbook Chapman HallCRC The R Series
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£75.99
Taylor & Francis Ltd Data Analytics for the Social Sciences
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£80.74
Taylor & Francis Data Analytics for the Social Sciences
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£228.00
Taylor & Francis Ltd Applied MetaAnalysis with R and Stata
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£45.99
Taylor & Francis Ltd Visualizing Surveys in R
Book SynopsisFor researchers who use surveys interested in learning how to seize vast possibilities and flexibility of R in survey analysis/visualizations. Psychologists, marketeers, HR personnel, managers, other professionals who wish to standardize/automate the process for visualizing survey data. Suitable for textbook courses.Table of ContentsI Preparation. 1. Survey data. 2. Process. 3. Variables. 4. Categories. 5. Read data. 6. Parse values. 7. Validate data. 8. Pre-process data. 9. Build a dataset. 10. Basic statistics. 11. Create plots with ggplot2. 12. Save plots to files. 13. R Markdown. II Plotting. 14. Numeric plots. 15. Bar charts. 16. Percentage bars. 17. Diverging percentage bars. 18. Pie charts. 19. Lollipop plots. 20. Dot plots. 21. Heatmaps. 22. Geographic maps. 23. Missing value plots. 24. Validation plots.
£137.75
Taylor & Francis Ltd Numerical Techniques in MATLAB
Book SynopsisIn this book, various numerical methods are discussed in a comprehensive way. It delivers a mixture of theory, examples and MATLAB practicing exercises to help the students in improving their skills. To understand the MATLAB programming in a friendly style, the examples are solved. The MATLAB codes are mentioned in the end of each topic. Throughout the text, a balance between theory, examples and programming is maintained.Key Features Methods are explained with examples and codes System of equations has given full consideration Use of MATLAB is learnt for every method This book is suitable for graduate students in mathematics, computer science and engineering.Table of Contents1. Common Commands Used in Matlab. 2. System of Linear Equations. 3. Polynomial Interpolation. 4. Root Finding Methods. 5. Numerical Integration. 6. Solution of Initial Value Problems. 7. Boundary Value Problems.
£87.39
Taylor & Francis Ltd Statistical Analysis of Questionnaires
Book SynopsisStatistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing.The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphiTrade Review"This book follows a well established approach to the psychometric analysis of questionnaire data as found in educational, survey and medical research. The authors provide an in-depth discussion of the analysis of score reliability and item properties grounded in classical test theory (CTT), and of the probabilistic modeling of individual responses based on latent variable models. … Chapter 5 is a bit different and focus on the estimation of item and person parameters and the diagnostic of IRT models. The first part is rather technical but it does a good job at describing Statistical Analysis of Questionnaires the pros and cons of each technique–joint, conditional and marginal maximum likelihood–and how they could be implemented using custom software. … The authors conclude (…) by highlighting multidimensional IRT models which allow to relax the strong hypothesis of unidimensionality that is attached to all previous models, as well as the main strengths of structural equation models which can be viewed as providing the glue between factor analytic methods and IRT. Overall, the authors succeed at presenting a solid and reliable framework for psychometric analysis of questionnaire data."— Christophe Lalanne, Paris-Diderot University, in the Journal of Statistical Software, November 2017Table of ContentsPreliminaries. Classical Test Theory. Item Response Theory Models for Dichotomous Items. Item Response Theory Models for Polytomous Items. Estimation Methods and Diagnostics. Some Extensions of Traditional Item Response Theory Models.
£43.99
Taylor & Francis Ltd Forecasting and Analytics with the Augmented
Book SynopsisForecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called ADAM (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analTable of Contents1. Introduction 2. Forecasts evaluation 3. Time series components and simple forecasting methods 4. Introduction to ETS 5. Pure additive ADAM ETS 6. Pure multiplicative ADAM ETS 7. General ADAM ETS model 8. Introduction to ARIMA 9. ADAM ARIMA 10. Explanatory variables in ADAM 11. Estimation of ADAM 12. Multiple frequencies in ADAM 13. Intermittent State Space Model 14. Model diagnostics 15. Model selection and combinations in ADAM 16. Handling uncertainty in ADAM 17. Scale model for ADAM 18. Forecasting with ADAM 19. Forecasting functions of the smooth package 20. What’s next?
£87.39
Taylor & Francis Ltd Spatial Statistics for Data Science
£73.14
Taylor & Francis Ltd Compositional Data Analysis in Practice
Book SynopsisCompositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.Trade Review"(…This book) avoids cumbersome theoretical digressions and only presents to the reader the essential basic concepts for the application of CODA, using ratios and logratios that retain most of the original data structure and, subsequently, may lead to proper conclusions. … The simplification of the analysis and the straightforward interpretability of results is, clearly, one of the primary values of the publication. In addition, the emphasis on the general application of weights in the calculus of most of the operations and methodologies used throughout the book deserves a special mention.. … Altogether, the book and the easyCODA R package may represent a promising instrument for introducing CODA in the fat and oils field, where fatty acid compositions have been treated until now exclusively by classical multivariate techniques without considering their compositional structure. Predicting the future is risky, but the book may represent an essential instrument for CODA spreading since it represents just what many practitioners were expecting to initiate their experience in this promising new statistical field of compositional data analysis."—A. Garrido Fernández in Gracas y Aceites – International Journal of Fats and Oils, July-September 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, Department of Earth and Environmental Sciences, University of WaterlooWaterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, University of Waterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019Table of ContentsWhat are compositional data, and why are they special? Geometry and visualization of compositional data. Logratio transformations. Properties and distributions of logratios. Regression models involving compositional data. Dimension reduction using logratio analysis. Clustering of compositional data. The problem of zeros, with some solutions. Simplifying the task: variable selection. Case study: Fatty acids of marine amphipods. Appendix A: Theory of compositional data analysis. Appendix B: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: Epilogue.
£114.00
Taylor & Francis Ltd Omic Association Studies with R and Bioconductor
Book SynopsisAfter the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a websTrade Review"This book is a good tool for self-learning analytical strategies for omics data. It requires previous knowledge of R and focuses on getting things done...I think the book would be a good reference for masters or PhD students that have to perform their analysis and need a starting point. Also, for the practicing statistician working with omics data."- Victor Moreno, ISCB News, July 2020 Table of Contents1 Introduction 2 Case examples 3 Dealing with omic data in Bioconductor 4 Genetic association studies 5 Genomic variant studies 6 Adressing batch effects 7 Transcriptomic studies 8 Epigenomic studies 9 Exposomic analysis 10 Enrichment analysis 11 Multiomic data analysis
£105.00
Taylor & Francis Ltd HandsOn Machine Learning with R
Book SynopsisHands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algoTrade Review"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"...The book describes in detail the various methods for solving classification and clustering problems. Functions from many R libraries are compared, which enables the reader to understand their respective advantages and disadvantages. The authors have developed a clear structure to the book that includes a brief description of each model, examples of using the model for specific real-life examples, and discussion of the advantages and disadvantages of the model. This structure is one of the book’s main advantages."- Igor Malyk, ISCB News, July 2020Table of ContentsI FUNDAMENTALS 1. Introduction to Machine Learning 1.1 Supervised learning 1.1.1 Regression problems 1.1.2 Classification problems 1.2 Unsupervised learning 1.3 Roadmap 1.4 The data sets 2. Modeling Process 2.1 Prerequisites 2.2 Data splitting 2.2.1 Simple random sampling 2.2.2 Stratified sampling 2.2.3 Class imbalances 2.3 Creating models in R 2.3.1 Many formula interfaces 2.3.2 Many engines 2.4 Resampling methods 2.4.1 k-fold cross validation 2.4.2 Bootstrapping 2.4.3 Alternatives 2.5 Bias variance trade-off 2.5.1 Bias 2.5.2 Variance 2.5.3 Hyperparameter tuning 2.6 Model evaluation 2.6.1 Regression models 2.6.2 Classification models 2.7 Putting the processes together 3. Feature & Target Engineering 3.1 Prerequisites 3.2 Target engineering 3.3 Dealing with missingness 3.3.1 Visualizing missing values 3.3.2 Imputation 3.4 Feature filtering 3.5 Numeric feature engineering 3.5.1 Skewness 3.5.2 Standardization 3.6 Categorical feature engineering 3.6.1 Lumping 3.6.2 One-hot & dummy encoding 3.6.3 Label encoding 3.6.4 Alternatives 3.7 Dimension reduction 3.8 Proper implementation 3.8.1 Sequential steps 3.8.2 Data leakage 3.8.3 Putting the process together II SUPERVISED LEARNING 4. Linear Regression 4.1 Prerequisites 4.2 Simple linear regression 4.2.1 Estimation 4.2.2 Inference 4.3 Multiple linear regression 4.4 Assessing model accuracy 4.5 Model concerns 4.6 Principal component regression 4.7 Partial least squares 4.8 Feature interpretation 4.9 Final thoughts 5. Logistic Regression 5.1 Prerequisites 5.2 Why logistic regression 5.3 Simple logistic regression 5.4 Multiple logistic regression 5.5 Assessing model accuracy 5.6 Model concerns 5.7 Feature interpretation 5.8 Final thoughts 6. Regularized Regression 6.1 Prerequisites 6.2 Why regularize? 6.2.1 Ridge penalty 6.2.2 Lasso penalty 6.2.3 Elastic nets 6.3 Implementation 6.4 Tuning 6.5 Feature interpretation 6.6 Attrition data 6.7 Final thoughts 7. Multivariate Adaptive Regression Splines 7.1 Prerequisites 7.2 The basic idea 7.2.1 Multivariate regression splines 7.3 Fitting a basic MARS model 7.4 Tuning 7.5 Feature interpretation 7.6 Attrition data 7.7 Final thoughts 8. K-Nearest Neighbors 8.1 Prerequisites 8.2 Measuring similarity 8.2.1 Distance measures 8.2.2 Pre-processing 8.3 Choosing k 8.4 MNIST example 8.5 Final thoughts 9 Decision Trees 9.1 Prerequisites 9.2 Structure 9.3 Partitioning 9.4 How deep? 9.4.1 Early stopping 9.4.2 Pruning 9.5 Ames housing example 9.6 Feature interpretation 9.7 Final thoughts 10. Bagging 10.1 Prerequisites 10.2 Why and when bagging works 10.3 Implementation 10.4 Easily parallelize 10.5 Feature interpretation 10.6 Final thoughts 11. Random Forests 11.1 Prerequisites 11.2 Extending bagging 11.3 Out-of-the-box performance 11.4 Hyperparameters 11.4.1 Number of trees 11.4.2 mtry 11.4.3 Tree complexity 11.4.4 Sampling scheme 11.4.5 Split rule 11.5 Tuning strategies 11.6 Feature interpretation 11.7 Final thoughts 12. Gradient Boosting 12.1 Prerequisites 12.2 How boosting works 12.2.1 A sequential ensemble approach 12.2.2 Gradient descent 12.3 Basic GBM 12.3.1 Hyperparameters 12.3.2 Implementation 12.3.3 General tuning strategy 12.4 Stochastic GBMs 12.4.1 Stochastic hyperparameters 12.4.2 Implementation 12.5 XGBoost 12.5.1 XGBoost hyperparameters 12.5.2 Tuning strategy 12.6 Feature interpretation 12.7 Final thoughts 13. Deep Learning 13.1 Prerequisites 13.2 Why deep learning 13.3 Feedforward DNNs 13.4 Network architecture 13.4.1 Layers and nodes 13.4.2 Activation 13.5 Backpropagation 13.6 Model training 13.7 Model tuning 13.7.1 Model capacity 13.7.2 Batch normalization 13.7.3 Regularization 13.7.4 Adjust learning rate 13.8 Grid Search 13.9 Final thoughts 14. Support Vector Machines 14.1 Prerequisites 14.2 Optimal separating hyperplanes 14.2.1 The hard margin classifier 14.2.2 The soft margin classifier 14.3 The support vector machine 14.3.1 More than two classes 14.3.2 Support vector regression 14.4 Job attrition example 14.4.1 Class weights 14.4.2 Class probabilities 14.5 Feature interpretation 14.6 Final thoughts 15. Stacked Models 15.1 Prerequisites 15.2 The Idea 15.2.1 Common ensemble methods 15.2.2 Super learner algorithm 15.2.3 Available packages 15.3 Stacking existing models 15.4 Stacking a grid search 15.5 Automated machine learning 15.6 Final thoughts 16. Interpretable Machine Learning 16.1 Prerequisites 16.2 The idea 16.2.1 Global interpretation 16.2.2 Local interpretation 16.2.3 Model-specific vs. model-agnostic 16.3 Permutation-based feature importance 16.3.1 Concept 16.3.2 Implementation 16.4 Partial dependence 16.4.1 Concept 16.4.2 Implementation 16.4.3 Alternative uses 16.5 Individual conditional expectation 16.5.1 Concept 16.5.2 Implementation 16.6 Feature interactions 16.6.1 Concept 16.6.2 Implementation 16.6.3 Alternatives 16.7 Local interpretable model-agnostic explanations 16.7.1 Concept 16.7.2 Implementation 16.7.3 Tuning 16.7.4 Alternative uses 16.8 Shapley values 16.8.1 Concept 16.8.2 Implementation 16.8.3 XGBoost and built-in Shapley values 16.9 Localized step-wise procedure 16.9.1 Concept 16.9.2 Implementation 16.10Final thoughts III DIMENSION REDUCTION 17. Principal Components Analysis 17.1 Prerequisites 17.2 The idea 17.3 Finding principal components 17.4 Performing PCA in R 17.5 Selecting the number of principal components 17.5.1 Eigenvalue criterion 17.5.2 Proportion of variance explained criterion 17.5.3 Scree plot criterion 17.6 Final thoughts 18. Generalized Low Rank Models 18.1 Prerequisites 18.2 The idea 18.3 Finding the lower ranks 18.3.1 Alternating minimization 18.3.2 Loss functions 18.3.3 Regularization 18.3.4 Selecting k 18.4 Fitting GLRMs in R 18.4.1 Basic GLRM model 18.4.2 Tuning to optimize for unseen data 18.5 Final thoughts 19. Autoencoders 19.1 Prerequisites 19.2 Undercomplete autoencoders 19.2.1 Comparing PCA to an autoencoder 19.2.2 Stacked autoencoders 19.2.3 Visualizing the reconstruction 19.3 Sparse autoencoders 19.4 Denoising autoencoders 19.5 Anomaly detection 19.6 Final thoughts IV Clustering 20. K-means Clustering 20.1 Prerequisites 20.2 Distance measures 20.3 Defining clusters 20.4 k-means algorithm 20.5 Clustering digits 20.6 How many clusters? 20.7 Clustering with mixed data 20.8 Alternative partitioning methods 20.9 Final thoughts 21. Hierarchical Clustering 21.1 Prerequisites 21.2 Hierarchical clustering algorithms 21.3 Hierarchical clustering in R 21.3.1 Agglomerative hierarchical clustering 21.3.2 Divisive hierarchical clustering 21.4 Determining optimal clusters 21.5 Working with dendrograms 21.6 Final thoughts 22. Model-based Clustering 22.1 Prerequisites 22.2 Measuring probability and uncertainty 22.3 Covariance types 22.4 Model selection 22.5 My basket example 22.6 Final thoughts Bibliography Index
£78.84
Taylor & Francis Inc Design and Analysis of Experiments with R
Book SynopsisDesign and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to: Make an appropriate design choice based on the objectives of a research project Create a design and perform an experiment Interpret the results of computer data analysis The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is uTrade Review"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"Thank you for writing your phenomenal book "Design and Analysis of Experiments with R". I'm teaching a new course this spring on experimental design and reinforcement learning. The students are graduate bioengineers, so I was having difficulty finding a text that blends theory, practice, and computation. Your book excels at all three. The first chapter I read clarified several topics and improved both my teaching and research. After testing a dozen DOE and RSM books, yours is the clear winner. I understand the enormous time that goes into a well-constructed textbook. I hope this message conveys my deep appreciation for your effort."—Paul Jensen, Ph.D., Assistant Professor , Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign"This is an excellent but demanding text. … This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. … reading this text is likely to influence their course for the better."—MAA Reviews, March 2015"In my opinion, this is a very valuable book. It covers the topics that I judge should be in such a book including what might be called the standard designs and more … it has become my go to text on experimental design."David E. Booth, TechnometricsTable of ContentsIntroduction. Completely Randomized Designs with One Factor. Factorial Designs. Randomized Block Designs. Designs to Study Variances. Fractional Factorial Designs. Incomplete and Confounded Block Designs. Split-Plot Designs. Crossover and Repeated Measures Designs. Response Surface Designs. Mixture Experiments. Robust Parameter Design Experiments. Experimental Strategies for Increasing Knowledge. Bibliography. Index.
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Cambridge University Press Computational Discrete Mathematics
Book SynopsisCombinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics. This definitive reference/user's guide provides examples of all 450 Combinatorica functions in action, along with tutorial text on the mathematical and algorithmic theory.Trade ReviewReview of the hardback: 'This book is the definite reference guide to Combinatorica … it is more than just a reference since it has all the necessary theory to comprehend the concepts … It is a very readable edition full of graphical and stimulating approaches to combinatorics and graph theories … This is a great resource for the acknowledgment of beautiful patterns and important properties of graphs and other combinatorial objects … This book is highly recommended. it is well organized, and readable textbook for beginners and intermediate students.' Leonardo On-lineTable of Contents1. Combinatorica: an explorer's guide; 2. Permutations and combinations; 3. Algebraic combinatorics; 4. Partitions, compositions and Young tableaux; 5. Graph representation; 6. Generating graphs; 7. Properties of graphs; 8. Algorithmic graph theory.
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Cambridge University Press Microcomputers and Mathematics
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Cambridge University Press Mathematica R in the Laboratory
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Cambridge University Press Solving Odes with MATLAB
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Cambridge University Press Mathematica in the Laboratory
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Cambridge University Press Mathematical Explorations with MATLAB
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Cambridge University Press Applied Linear Models with SAS
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Cambridge University Press Solving ODEs with MATLAB
Book SynopsisThis concise text, first published in 2003, is for a one-semester course for upper-level undergraduates and beginning graduate students in engineering, science, and mathematics, and can also serve as a quick reference for professionals. The treatment of each method is brief and technical issues are minimized, but all the issues important in practice and for understanding the code are discussed.Trade Review' … this is a readable, accessible text full of invaluable advice, illustrated using interesting examples and exercises … if you do have some background knowledge of numerical analysis, MATLAB, and are motivated by the application of numerical methods to real problems, you will find this book full of interest … the book acts as a useful introduction to several important, more general, issues in scientific computing.' The Mathematical GazetteTable of Contents1. Getting started; 2. Initial value problems; 3. Boundary value problems; 4. Delay differential equations.
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Cambridge University Press Complex Analysis with MATHEMATICA
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Cambridge University Press Numerical and Statistical Methods for Bioengineering
Book SynopsisThe first MATLAB-based numerical methods textbook specifically for bioengineers, including topics on hypothesis testing, plus numerous examples drawn exclusively from biomedical engineering applications. This is an ideal core text for one-semester undergraduate courses, and is also a valuable reference for anyone interested in the quantitative aspects of biology research.Trade Review'I think this book is a winner … [it] is really easy to read and places frameworks for numerical analysis into realistic bioengineering concepts that students will find familiar and relevant. This is most evident in the excellent boxed examples, but also in many of the homework problems. I also really liked the 'key points to consider' at the end of the chapters - these are useful reminders for the students. Finally, the book presents bioinformatics in a manageable fashion that should help demystify this subject for interested students.' K. Jane Grande-Allen, Rice UniversityTable of Contents1. Types and sources of numerical error; 2. Systems of linear equations; 3. Statistics and probability; 4. Hypothesis testing; 5. Root finding techniques for nonlinear equations; 6. Numerical quadrature; 7. Numerical integration of ordinary differential equations; 8. Nonlinear data regression and optimization; 9. Basic algorithms of bioinformatics; Appendix A. Introduction to MATLAB; Appendix B. Location of nodes for Gauss-Legendre quadrature.
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Cambridge University Press Data Analysis for Physical Scientists
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Cambridge University Press Bayesian Social Science Statistics Volume 2
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Cambridge University Press A Guide to MATLAB For Beginners and Experienced Users
Book SynopsisNow in its third edition, this outstanding textbook explains everything you need to get started using MATLAB . It contains concise explanations of essential MATLAB commands, as well as easily understood instructions for using MATLAB's programming features, graphical capabilities, simulation models, and rich desktop interface. MATLAB 8 and its new user interface is treated extensively in the book. New features in this edition include: a complete treatment of MATLAB's publish feature; new material on MATLAB graphics, enabling the user to master quickly the various symbolic and numerical plotting routines; and a robust presentation of MuPAD and how to use it as a stand-alone platform. The authors have also updated the text throughout, reworking examples and exploring new applications. The book is essential reading for beginners, occasional users and experienced users wishing to brush up their skills. Further resources are available from the authors' website at www-math.umd.edu/schol/a-guTrade ReviewReview of previous edition: 'Major highlights of the book are completely transparent examples of classical yet always intriguing mathematical, statistical, engineering, economics, and physics problems. In addition, the book explains a seamless use with Microsoft Word for integrating MATLAB® outputs with documents, reports, presentations, or other online processes. Advanced topics with examples include: Monte Carlo simulation, population dynamics, and linear programming. … an outstanding textbook, and, likewise, should be an integral part of the technical reference shelf for most IT professionals. It is a great resource for wherever MATLAB® is available!' ACM UbiquityReview of previous edition: 'This is a short, focused introduction to MATLAB®, a comprehensive software system for mathematical and technical computing. For the beginner it explains everything needed to start using MATLAB®, while experienced users ... will find much useful information here.' L'enseignement mathematiqueTable of ContentsPreface; 1. Getting started; 2. MATLAB basics; 3. Interacting with MATLAB; Practice Set A. Algebra and arithmetic; 4. Beyond the basics; 5. MATLAB graphics; 6. MATLAB programming; 7. Publishing and M-books; Practice Set B. Math, graphics, and programming; 8. MuPAD; 9. Simulink; 10. GUIs; 11. Applications; Practice Set C. Developing your MATLAB skills; 12. Troubleshooting; Solutions to the practice sets; Glossary; Index.
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Elsevier Science Publishing Co Inc Handbook of Statistical Analysis and Data Mining
Book SynopsisTrade Review"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. "Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. "What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)Table of ContentsPart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The "Right Model" for the "Right Purpose": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models" 21. Ethics and Data Analytics 22. IBM Watson
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