1  20
Next
2. R for programmers : advanced techniques [2017]
 Zhang, Dan, 1983 author.
 Boca Raton : CRC Press, [2017]
 Description
 Book — 1 online resource
 Summary

 part SECTION I: APPLYING R IN MATHEMATIC CALCULATIONS AND ALGORITHMS
 chapter 1 The Knowledge System and Mathematical Functions of R
 chapter 2 The Algorithm Implementations in R
 part SECTION II: R PROGRAMMING IN DEPTH
 chapter 3 The Kernel Programming in R
 chapter 4 ObjectOriented Programming
 part SECTION III: DEVELOPING R PACKAGE
 chapter 5 Developing R Packages
 chapter 6 Journey on R Game Development
3. R packages [2015]
 Wickham, Hadley.
 First edition.  Sebastopol, CA : O'Reilly Media, 2015.
 Description
 Book — xiv, 182 pages : ill. ; 24 cm
 Summary

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a wellrespected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA76.73 .R3 W53 2015  Unknown 
 Fox, John author.
 Boca Raton : CRC Press, [2017]
 Description
 Book — 1 online resource
 Summary

 Introducing R and the R Commander. Installing R and the R Commander. A Quick Tour of the R Commander. Data Input and Data Management. Summarizing and Graphing Data. Simple Statistical Tests. Fitting Linear and Generalized Linear Models. Probability Distributions and Simulation. Using R Commander Plugin Packages.
 (source: Nielsen Book Data)
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5. R recipes : a problemsolution approach [2014]
 Pace, Larry A.
 [New York] : Apress ; New York : Distributed to the book trade worldwide by Springer, c2014.
 Description
 Book — 242 pages : ill. ; 24 cm
 Summary

 1. Migrating to R
 2. Input and Output
 3. Data Structures
 4. Merging and Reshaping Datasets
 5. Working with Dates and Strings
 6. Working with Tabular Data
 7. Working with Numerical Data
 8. Graphics and Data Visualization
 9. Probability Distributions
 10. Tests of Differences
 11. Tests of Relationships
 12. Modern Robust Statistics
 13. Writing Functions
 14. Working with Financial Data
 15. Dealing with Big Data
 16. Introduction to Text and Data Mining.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276.45 .R3 P33 2014  Unknown 
 De Brouwer, Philippe J. S., author.
 Hoboken, NJ, USA : Wiley, 2020.
 Description
 Book — 1 online resource
 Summary

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big RBook for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for nonexperts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book's content, as well as all the extracted Rcode and is available to everyone on a Wiley Book Companion Site The Big RBook is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.
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 Wickham, Hadley.
 2nd ed  Sebastopol : O'Reilly Media, Incorporated, 2023
 Description
 Book — 1 online resource (382 p.)
 Summary

 Intro
 Copyright
 Table of Contents
 Preface
 Welcome!
 Introduction
 Philosophy
 In This Book
 What's Not Here
 Conventions Used in This Book
 Colophon
 O'Reilly Online Learning
 How to Contact Us
 Acknowledgments
 Part I. Getting Started
 Chapter 1. The Whole Game
 Load devtools and Friends
 Toy Package: regexcite
 Preview the Finished Product
 create_package()
 use_git()
 Write the First Function
 use_r()
 load_all()
 Commit strsplit1()
 check()
 Edit DESCRIPTION
 use_mit_license()
 document()
 NAMESPACE Changes
 check() Again
 Install()
 use_testthat()
 use_package()
 use_github()
 use_readme_rmd()
 The End: check() and install()
 Review
 Chapter 2. System Setup
 devtools, usethis, and You
 Personal Startup Configuration
 R Build Toolchain
 Windows
 macOS
 Linux
 Verify System Prep
 Chapter 3. Package Structure and State
 Package States
 Source Package
 Bundled Package
 .Rbuildignore
 Binary Package
 Installed Package
 InMemory Package
 Package Libraries
 Chapter 4. Fundamental Development Workflows
 Create a Package
 Survey the Existing Landscape
 Name Your Package
 Package Creation
 Where Should You create_package()?
 RStudio Projects
 Benefits of RStudio Projects
 How to Get an RStudio Project
 What Makes an RStudio Project?
 How to Launch an RStudio Project
 RStudio Project Versus Active usethis Project
 Working Directory and Filepath Discipline
 Test Drive with load_all()
 Benefits of load_all()
 Other Ways to Call load_all()
 check() and R CMD check
 Workflow
 Background on R CMD check
 Chapter 5. The Package Within
 Alfa: A Script That Works
 Bravo: A Better Script That Works
 Charlie: A Separate File for Helper Functions
 Delta: A Failed Attempt at Making a Package
 Echo: A Working Package
 Foxtrot: Build Time Versus Run Time
 Golf: Side Effects
 Concluding Thoughts
 Script Versus Package
 Finding the Package Within
 Package Code Is Different
 Part II. Package Components
 Chapter 6. R Code
 Organize Functions Into Files
 Fast Feedback via load_all()
 Code Style
 Understand When Code Is Executed
 Example: A Path Returned by system.file()
 Example: Available Colors
 Example: Aliasing a Function
 Respect the R Landscape
 Manage State with withr
 Restore State with base::on.exit()
 Isolate Side Effects
 When You Do Need Side Effects
 Constant Health Checks
 Chapter 7. Data
 Exported Data
 Preserve the Origin Story of Package Data
 Documenting Datasets
 NonASCII Characters in Data
 Internal Data
 Raw Data File
 Filepaths
 pkg_example() Path Helpers
 Internal State
 Persistent User Data
 Chapter 8. Other Components
 Other Directories
 Installed Files
 Package Citation
 Configuration Tools
 Part III. Package Metadata
 Chapter 9. DESCRIPTION
 The DESCRIPTION File
8. Metaprogramming in R : advanced statistical programming for data science, analysis, and finance [2017]
 Mailund, Thomas, author.
 [United States] : Apress, [2017]
 Description
 Book — 1 online resource.
 Summary

 1. Anatomy of a Functio
 n2. Inside a FunctionCal
 l3. Expressions and Environment
 s4. Manipulating Expressions
 5. Working with Substitutions.
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 Online

 EBSCOhost Access limited to 1 user
 Google Books (Full view)
9. R for SAS and SPSS users [2009]
 Muenchen, Robert A.
 New York : Springer, c2009.
 Description
 Book — xvii, 470 p. : ill. ; 25 cm.
 Summary

 Introduction. The five main parts of SAS and SPSS. Programming conventions. Typographic conventions. Installing & updating R. Running R. Help and documentation. Programming language basics. Data Acquisition. Selecting Variables  Var, Variables=. Selecting observations  where, if select if, filter. Selecting both variables and observations. Converting data structures. Data management. Recoding variables. Value labels or formats (& measurement level). Variable labels. Generating data. How R stores data. Managing your files and workspace. Graphics overview. Traditional graphics. The ggplot2 package. Statistics. Summary. Conclusion. Appendix A. Appendix B. Appendix C. Bibliography.
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Green Library, Science Library (Li and Ma)
Green Library  Status 

Find it Jonsson Social Sciences Reading Room  
QA276.45 .R3 M48 2009  Inlibrary use 
Science Library (Li and Ma)  Status 

Stacks  
QA276.45 .R3 M48 2009  Unknown 
 Rodríguez Revilla, Ramiro, author.
 Primera edición  Bogotá, D.C. : Ediciones Unisalle, [2020]
 Description
 Book — 1 online resource Digital: text file.PDF.EPUB.
 Kronthaler, Franz.
 [Place of publication not identified] : Springer Spektrum, 2021.
 Description
 Book — 1 online resource
 Summary

 Comment. 1 R and RStudio. 2 Data analysis basics with RStudio. 3 Data tourism (simulated). 4 Describing data with RStudio. 5 Testing normal distribution with RStudio. 6 Testing hypotheses with RStudio,  7 Linear regression with RStudio. 8 Further reading. 9 Appendix: 1 Questionnaire, 2 Data "tourism.xlsx" including legend, 3 How to deal with missing data, 4 Solutions for the task.
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 Vinod, Hrishikesh D., 1939
 Singapore ; Hackensack, N. J. : World Scientific, c2011.
 Description
 Book — xvii, 329 p. : ill. ; 23 cm.
 Summary

 R Preliminaries
 Elementary Geometry and Algebra Using R
 Vector Spaces
 Matrix Basics and R Software
 Determinant and Singularity
 The Rank and Trace of a Matrix
 Matrix Inverse and Solution of Linear Equations.
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 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA188 .V56 2011  Unknown 
 Hector, Andy. Author
 Oxford : Oxford University Press, 2015.
 Description
 Book — 199 pages ; 24 cm
 Summary

 1. Introduction
 2. Comparing Groups: Analysis of Variance
 3. Comparing Groups: Student's t test
 4. Linear Regression
 5. Comparisons using Estimates and Intervals
 6. Interactions
 7. Analysis of Covariance: ANCOVA
 8. Maximum Likelihood and Generalized Linear Models
 9. Generalized Linear Models for Data with NonNormal Distributions
 10. Mixed Effects Models
 11. Generalized Linear Mixedeffects Models
 12. Final Thoughts
 Appendix 1: A very short introduction to the R programming language for statistics and graphics.
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(source: Nielsen Book Data)
Marine Biology Library (Miller), Science Library (Li and Ma)
Marine Biology Library (Miller)  Status 

Stacks  Request (opens in new tab) 
QH324.2 .H44 2015  Unknown 
Science Library (Li and Ma)  Status 

Stacks  
QH324.2 .H44 2015  Unknown 
 New York ; London : Springer, 2005.
 Description
 Book — xix, 473 p.
 Summary

 Preprocessing Overview. Preprocessing HighDensity Oligonucleotide Arrays. Quality Assessment of Affymetrix GeneChip Data. Preprocessing Twocolor Spotted Arrays. Cellbased assays. SELDITOF Mass Spectrometry Protein Data. Metadata Resources and Tools in Bioconductor. Querying on line resources. Interactive Outputs. Visualizing Data. Analysis Overview. Distance Measures in DNA Microarray Data Analysis. Cluster Analysis of Genomic Data. Analysis of Differential Gene Expression Studies. Multiple Testing Procedures: R multtest Package and Applications to Genomics. Machine Learning Concepts and Tools for Statistical Genomics. Ensemble Methods of Computational Inference. BrowserBased Affymetrix Analysis and Annotation. Introduction and Motivating Examples. Graphs. Bioconductor Software for Graphs. Case Studies Using Graphs on Biological Data. Limma: Linear Models for Microarray Data. Classification with Gene Expression Data. From Cel Files to Annotated lists of Interesting Genes.
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(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QH507 .B56 2005  Unknown 
 Touchon, Justin C., author.
 Oxford : Oxford University Press, [2021]
 Description
 Book — 1 online resource
 Summary

 Preface
 1: Introduction to R
 2: Before You Begin (aka Thoughts on Proper Data Analysis)
 3: Exploratory Data Analysis and Data Summarization
 4: Introduction to Plotting
 5: Basic Statistical Analyses using R
 6: More Linear Models in R!
 7: Generalized Linear Models
 8: Mixed Effects Models
 9: Advanced Data Wrangling and Plotting
 10: Writing Loops and Functions in R
 11: Final Thoughts.
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16. R programming for bioinformatics [2009]
 Gentleman, Robert, 1959
 Boca Raton, FL : CRC Press, c2009.
 Description
 Book — xii, 314 p. : ill. ; 25 cm.
 Summary

 Introducing R Motivation A note on the text
 R Language Fundamentals Data structures Managing your R session Language basics Subscripting and subsetting Vectorized computations Replacement functions Functional programming Writing functions Flow control Exception handling Evaluation Lexical scope Graphics ObjectOriented Programming in R The basics of OOP S3 OOP S4 OOP Using classes and methods in packages Documentation Debugging Managing S3 and S4 together Navigating the class and method hierarchy Input and Output in R Basic file handling Connections File input and output Source and sink: capturing R output Tools for accessing files on the Internet Working with Character Data Builtin capabilities Regular expressions Prefixes, suffixes and substrings Biological sequences Matching patterns Foreign Language Interfaces Calling C and FORTRAN from R Writing C code to interface with R Using the R API Loading libraries Advanced topics Other languages R Packages Package basics Package management Package authoring Initialization Data Technologies Using R for data manipulation Example Database technologies XML Bioinformatics resources on the WWW Debugging and Profiling The browser function Debugging in R Debugging C and other foreign code Profiling R code Managing memory References An Introduction appears at the beginning of each chapter.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QH324.2 .G46 2009  Unknown 
17. Learning microeconometrics with R [2020]
 Adams, Christopher P., author.
 First edition  Boca Raton, FL : CRC Press, 2020
 Description
 Book — 1 online resource (xxx, 368 pages)
 Summary

 I. Experiments.
 1. Ordinary Least Squares.
 2. Multiple Regression.
 3. Instrumental Variables
 4. Bounds Estimation. II. Structural Estimation.
 5. Estimating Demand.
 6. Estimating Selection Models.
 7. Demand Estimation with IV
 8. Estimating Games.
 9. Estimating Auction Models III. Repeated Measurement.
 10. Panel data.
 11. Synthetic Controls.
 12. Mixture Models. IV. Appendices. A. Measuring Uncertainty. B. Statistical Programming in R Acknowledgements Bibliography.
 (source: Nielsen Book Data)
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 Online

 EBSCOhost Access limited to 3 simultaneous users
 Google Books (Full view)
 Curry, Edward R., author.
 First edition  Boca Raton, FL : CRC Press, Taylor & Francis Group, 2020
 Description
 Book — 1 online resource ( xi, 298 pages) : illustrations (chiefly color)
 Summary

 Introduction
 Introduction to R
 An Introduction to LINUX for Biological Research
 Statistical Methods for Data Analysis
 Analyzing Generic Tabular Numeric Datasets in R
 Functional Enrichment Analysis
 Integrating Multiple Datasets in R
 Analyzing Microarray Data in R
 Analyzing DNA Methylation Microarray Data in R
 DNA Analysis With Microarrays
 Working with Sequencing Data
 Genomic Sequence Profiling
 ChIPseq
 RNAseq
 Bisulphite Sequencing
 Final Notes.
 (source: Nielsen Book Data)
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 Online

 ProQuest Ebook Central Access limited to 1 user
 Google Books (Full view)
 Logan, Murray.
 Chichester, West Sussex, UK ; Hoboken, NJ : WileyBlackwell, 2010.
 Description
 Book — xxviii, 546 p. : ill. ; 26 cm.
 Summary

 Preface. R quick reference card. General key to statistical methods.
 1 Introduction to R. 1.1 Why R? 1.2 Installing R. 1.3 The R environment. 1.4 Object names. 1.5 Expressions, Assignment and Arithmetic. 1.6 R Sessions and workspaces. 1.7 Getting help. 1.8 Functions. 1.9 Precedence. 1.10 Vectors  variables. 1.11 Matrices, lists and data frames. 1.12 Object information and conversion. 1.13 Indexing vectors, matrices and lists. 1.14 Pattern matching and replacement (character search and replace). 1.15 Data manipulation. 1.16 Functions that perform other functions repeatedly. 1.17 Programming in R. 1.18 An introduction to the R graphical environment. 1.19 Packages. 1.20 Working with scripts. 1.21 Citing R in publications. 1.22 Further reading.
 2 Datasets. 2.1 Constructing data frames. 2.2 Reviewing a data frame  fix(). 2.3 Importing (reading) data. 2.4 Exporting (writing) data. 2.5 Saving and loading of R objects. 2.6 Data frame vectors. 2.7 Manipulating data sets. 2.8 Dummy data sets  generating random data.
 3 Introductory statistical principles. 3.1 Distributions. 3.2 Scale transformations. 3.3 Measures of location. 3.4 Measures of dispersion and variability. 3.5 Measures of the precision of estimates  standard errors and confidence intervals. 3.6 Degrees of freedom. 3.7 Methods of estimation. 3.8 Outliers. 3.9 Further reading.
 4 Sampling and experimental design with R. 4.1 Random sampling. 4.2 Experimental design.
 5 Graphical data presentation. 5.1 The plot() function . 5.2 Graphical Parameters. 5.3 Enhancing and customizing plots with lowlevel plotting functions. 5.4 Interactive graphics. 5.5 Exporting graphics. 5.6 Working with multiple graphical devices. 5.7 Highlevel plotting functions for univariate (single variable) data. 5.8 Presenting relationships. 5.9 Presenting grouped data. 5.10 Presenting categorical data. 5.11 Trellis graphics.
 6 Simple hypothesis testing  one and two population tests. 6.1 Hypothesis testing. 6.2 One and twotailed tests. 6.3 t tests. 6.4 Assumptions. 6.5 Statistical decision and power. 6.6 Robust tests. 6.7 Further reading. 6.8 Key for simple hypothesis testing. 6.9 Worked examples of real biological data sets.
 7 Introduction to Linear models. 7.1 Linear models. 7.2 Linear models in R. 7.3 Estimating linear model parameters. 7.4 Comments about the importance of understanding the structure and parameterization of linear models.
 8 Correlation and simple linear regression. 8.1 Correlation. 8.2 Simple linear regression. 8.3 Smoothers and local regression. 8.4 Correlation and regression in R. 8.5 Further reading. 8.6 Key for correlation and regression. 8.7 Worked examples of real biological data sets.
 9 Multiple and curvilinear regression. 9.1 Multiple linear regression. 9.2 Linear models. 9.3 Null hypotheses. 9.4 Assumptions. 9.5 Curvilinear models. 9.6 Robust regression. 9.7 Model selection. 9.8 Regression trees. 9.9 Further reading. 9.10 Key and analysis sequence for multiple and complex regression. 9.11 Worked examples of real biological data sets.
 10 Single factor classification (ANOVA). 10.0.1 Fixed versus random factors. 10.1 Null hypotheses. 10.2 Linear model. 10.3 Analysis of variance. 10.4 Assumptions. 10.5 Robust classification (ANOVA). 10.6 Tests of trends and means comparisons. 10.7 Power and sample size determination. 10.8 ANOVA in R. 10.9 Further reading. 10.10 Key for single factor classification (ANOVA). 10.11 Worked examples of real biological data sets.
 11 Nested ANOVA. 11.1 Linear models. 11.2 Null hypotheses. 11.3 Analysis of variance. 11.4 Variance components. 11.5 Assumptions. 11.6 Pooling denominator terms. 11.7 Unbalanced nested designs. 11.8 Linear mixed effects models. 11.9 Robust alternatives. 11.10 Power and optimisation of resource allocation. 11.11 Nested ANOVA in R. 11.12 Further reading. 11.13 Key for nested ANOVA. 11.14 Worked examples of real biological data sets.
 12 Factorial ANOVA. 12.1 Linear models. 12.2 Null hypotheses. 12.3 Analysis of variance. 12.4 Assumptions. 12.5 Planned and unplanned comparisons. 12.6 Unbalanced designs. 12.7 Robust factorial ANOVA. 12.8 Power and sample sizes. 12.9 Factorial ANOVA in R. 12.10 Further reading. 12.11 Key for factorial ANOVA. 12.12 Worked examples of real biological data sets.
 13 Unreplicated factorial designs  randomized block and simple repeated measures. 13.1 Linear models. 13.2 Null hypotheses. 13.3 Analysis of variance. 13.4 Assumptions. 13.5 Specific comparisons. 13.6 Unbalanced unreplicated factorial designs. 13.7 Robust alternatives. 13.8 Power and blocking efficiency. 13.9 Unreplicated factorial ANOVA in R. 13.10 Further reading. 13.11 Key for randomized block and simple repeated measures ANOVA. 13.12 Worked examples of real biological data sets.
 14 Partly nested designs: split plot and complex repeated measures. 14.1 Null hypotheses. 14.2 Linear models. 14.3 Analysis of variance. 14.4 Assumptions. 14.5 Other issues. 14.6 Further reading. 14.7 Key for partly nested ANOVA. 14.8 Worked examples of real biological data sets.
 15 Analysis of covariance (ANCOVA). 15.1 Null hypotheses. 15.2 Linear models. 15.3 Analysis of variance. 15.4 Assumptions. 15.5 Robust ANCOVA. 15.6 Specific comparisons. 15.7 Further reading. 15.8 Key for ANCOVA. 15.9 Worked examples of real biological data sets.
 16 Simple Frequency Analysis. 16.1 The chisquare statistic. 16.2 Goodness of fit tests. 16.3 Contingency tables. 16.4 Gtests. 16.5 Small sample sizes. 16.6 Alternatives. 16.7 Power analysis. 16.8 Simple frequency analysis in R. 16.9 Further reading. 16.10 Key for Analysing frequencies. 16.11 Worked examples of real biological data sets.
 17 Generalized linear models (GLM). 17.1 Dispersion (over or under). 17.2 Binary data  logistic (logit) regression. 17.3 Count data  Poisson generalized linear models. 17.4 Assumptions. 17.5 Generalized additive models (GAM's)  nonparametric GLM. 17.6 GLM and R. 17.7 Further reading. 17.8 Key for GLM. 17.9 Worked examples of real biological data sets. Bibliography. R index. Statistics index. Companion website for this book: wiley.com/go/logan/r.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QH323.5 .L645 2010  Unknown 
 Lepš, Jan, 1953 author.
 Cambridge, UK ; New York, NY : Cambridge University Press, 2020
 Description
 Book — xvi, 365 pages : illustrations ; 25 cm
 Summary

 Machine generated contents note:
 1. Basic Statistical Terms, Sample Statistics
 1.1. Cases, Variables and Data Types
 1.2. Population and Random Sample
 1.3. Sample Statistics
 1.4. Precision of Mean Estimate, Standard Error of Mean
 1.5. Graphical Summary of Individual Variables
 1.6. Random Variables, Distribution, Distribution Function, Density Distribution
 1.7. Example Data
 1.8. How to Proceed in R
 1.9. Reporting Analyses
 1.10. Recommended Reading
 2. Testing Hypotheses, GoodnessofFit Test
 2.1. Principles of Hypothesis Testing
 2.2. Possible Errors in Statistical Tests of Hypotheses
 2.3. Null Models with Parameters Estimated from the Data: Testing HardyWeinberg Equilibrium
 2.4. Sample Size
 2.5. Critical Values and Significance Level
 2.6. Too Good to Be True
 2.7. Bayesian Statistics: What is It?
 2.8. The Dark Side of Significance Testing
 2.9. Example Data
 2.10. How to Proceed in R
 2.11. Reporting Analyses
 2.12. Recommended Reading
 3. Contingency Tables
 3.1. TwoWay Contingency Tables
 3.2. Measures of Association Strength
 3.3. Multidimensional Contingency Tables
 3.4. Statistical and Causal Relationship
 3.5. Visualising Contingency Tables
 3.6. Example Data
 3.7. How to Proceed in R
 3.8. Reporting Analyses
 3.9. Recommended Reading
 4. Normal Distribution
 4.1. Main Properties of a Normal Distribution
 4.2. Skewness and Kurtosis
 4.3. Standardised Normal Distribution
 4.4. Verifying the Normality of a Data Distribution
 4.5. Example Data
 4.6. How to Proceed in R
 4.7. Reporting Analyses
 4.8. Recommended Reading
 5. Student's t Distribution
 5.1. Use Case Examples
 5.2. T Distribution and its Relation to the Normal Distribution
 5.3. Single Sample Test and Paired t Test
 5.4. OneSided Tests
 5.5. Confidence Interval of the Mean
 5.6. Test Assumptions
 5.7. Reporting Data Variability and Mean Estimate Precision
 5.8. How Large Should a Sample Size Be?
 5.9. Example Data
 5.10. How to Proceed in R
 5.11. Reporting Analyses
 5.12. Recommended Reading
 6. Comparing Two Samples
 6.1. Use Case Examples
 6.2. Testing for Differences in Variance
 6.3. Comparing Means
 6.4. Example Data
 6.5. How to Proceed in R
 6.6. Reporting Analyses
 6.7. Recommended Reading
 7. Nonparametric Methods for Two Samples
 7.1. Mann  Whitney Test
 7.2. Wilcoxon Test for Paired Observations
 7.3. Using RankBased Tests
 7.4. Permutation Tests
 7.5. Example Data
 7.6. How to Proceed in R
 7.7. Reporting Analyses
 7.8. Recommended Reading
 8. OneWay Analysis of Variance (ANOVA) and KruskalWallis Test
 8.1. Use Case Examples
 8.2. ANOVA: A Method for Comparing More Than Two Means
 8.3. Test Assumptions
 8.4. Sum of Squares Decomposition and the F Statistic
 8.5. ANOVA for Two Groups and the TwoSample t Test
 8.6. Fixed and Random Effects
 8.7. F Test Power
 8.8. Violating ANOVA Assumptions
 8.9. Multiple Comparisons
 8.10. Nonparametric ANOVA: Kruskal  Wallis Test
 8.11. Example Data
 8.12. How to Proceed in R
 8.13. Reporting Analyses
 8.14. Recommended Reading
 9. TwoWay Analysis of Variance
 9.1. Use Case Examples
 9.2. Factorial Design
 9.3. Sum of Squares Decomposition and Test Statistics
 9.4. TwoWay ANOVA with and without Interactions
 9.5. TwoWay ANOVA with No Replicates
 9.6. Experimental Design
 9.7. Multiple Comparisons
 9.8. Nonparametric Methods
 9.9. Example Data
 9.10. How to Proceed in R
 9.11. Reporting Analyses
 9.12. Recommended Reading
 10. Data Transformations for Analysis of Variance
 10.1. Assumptions of ANOVA and their Possible Violations
 10.2. Logtransformation
 10.3. Arcsine Transformation
 10.4. SquareRoot and Box  Cox Transformation
 10.5. Concluding Remarks
 10.6. Example Data
 10.7. How to Proceed in R
 10.8. Reporting Analyses
 10.9. Recommended Reading
 11. Hierarchical ANOVA, SplitPlot ANOVA, Repeated Measurements
 11.1. Hierarchical ANOVA
 11.2. SplitPlot ANOVA
 11.3. ANOVA for Repeated Measurements
 11.4. Example Data
 11.5. How to Proceed in R
 11.6. Reporting Analyses
 11.7. Recommended Reading
 12. Simple Linear Regression: Dependency Between Two Quantitative Variables
 12.1. Use Case Examples
 12.2. Regression and Correlation
 12.3. Simple Linear Regression
 12.4. Testing Hypotheses
 12.5. Confidence and Prediction Intervals
 12.6. Regression Diagnostics and Transforming Data in Regression
 12.7. Regression Through the Origin
 12.8. Predictor with Random Variation
 12.9. Linear Calibration
 12.10. Example Data
 12.11. How to Proceed in R
 12.12. Reporting Analyses
 12.13. Recommended Reading
 13. Correlation: Relationship Between Two Quantitative Variables
 13.1. Use Case Examples
 13.2. Correlation as a Dependency Statistic for Two Variables on an Equal Footing
 13.3. Test Power
 13.4. Nonparametric Methods
 13.5. Interpreting Correlations
 13.6. Statistical Dependency and Causality
 13.7. Example Data
 13.8. How to Proceed in R
 13.9. Reporting Analyses
 13.10. Recommended Reading
 14. Multiple Regression and General Linear Models
 14.1. Use Case Examples
 14.2. Dependency of a Response Variable on Multiple Predictors
 14.3. Partial Correlation
 14.4. General Linear Models and Analysis of Covariance
 14.5. Example Data
 14.6. How to Proceed in R
 14.7. Reporting Analyses
 14.8. Recommended Reading
 15. Generalised Linear Models
 15.1. Use Case Examples
 15.2. Properties of Generalised Linear Models
 15.3. Analysis of Deviance
 15.4. Overdispersion
 15.5. Loglinear Models
 15.6. Predictor Selection
 15.7. Example Data
 15.8. How to Proceed in R
 15.9. Reporting Analyses
 15.10. Recommended Reading
 16. Regression Models for Nonlinear Relationships
 16.1. Use Case Examples
 16.2. Introduction
 16.3. Polynomial Regression
 16.4. Nonlinear Regression
 16.5. Example Data
 16.6. How to Proceed in R
 16.7. Reporting Analyses
 16.8. Recommended Reading
 17. Structural Equation Models
 17.1. Use Case Examples
 17.2. SEMs and Path Analysis
 17.3. Example Data
 17.4. How to Proceed in R
 17.5. Reporting Analyses
 17.6. Recommended Reading
 18. Discrete Distributions and Spatial Point Patterns
 18.1. Use Case Examples
 18.2. Poisson Distribution
 18.3. Comparing the Variance with the Mean to Measure Spatial Distribution
 18.4. Spatial Pattern Analyses Based on the Kfunction
 18.5. Binomial Distribution
 18.6. Example Data
 18.7. How to Proceed in R
 18.8. Reporting Analyses
 18.9. Recommended Reading
 19. Survival Analysis
 19.1. Use Case Examples
 19.2. Survival Function and Hazard Rate
 19.3. Differences in Survival Among Groups
 19.4. Cox Proportional Hazard Model
 19.5. Example Data
 19.6. How to Proceed in R
 19.7. Reporting Analyses
 19.8. Recommended Reading
 20. Classification and Regression Trees
 20.1. Use Case Examples
 20.2. Introducing CART
 20.3. Pruning the Tree and Crossvalidation
 20.4. Competing and Surrogate Predictors
 20.5. Example Data
 20.6. How to Proceed in R
 20.7. Reporting Analyses
 20.8. Recommended Reading
 21. Classification
 21.1. Use Case Examples
 21.2. Aims and Properties of Classification
 21.3. Input Data
 21.4. Similarity and Distance
 21.5. Clustering Algorithms
 21.6. Displaying Results
 21.7. Divisive Methods
 21.8. Example Data
 21.9. How to Proceed in R
 21.10. Other Software
 21.11. Reporting Analyses
 21.12. Recommended Reading
 22. Ordination
 22.1. Use Case Examples
 22.2. Unconstrained Ordination Methods
 22.3. Constrained Ordination Methods
 22.4. Discriminant Analysis
 22.5. Example Data
 22.6. How to Proceed in R
 22.7. Alternative Software
 22.8. Reporting Analyses
 22.9. Recommended Reading
 Appendix A First Steps with
 R Software
 A.1. Starting and Ending R, Command Line, Organising Data
 A.2. Managing Your Data
 A.3. Data Types in R
 A.4. Importing Data into R
 A.5. Simple Graphics
 A.6. Frameworks for R
 A.7. Other Introductions to Work with R.
(source: Nielsen Book Data)
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Marine Biology Library (Miller)
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QH323.5 .L46 2020  Unknown 
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