This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkinson's Grammar of Graphics to create a powerful and flexible system for creating data ...graphics. It teaches how to create graphics in R using ggplot.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in ...an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions-including all R codes-that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.
Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers ...everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling) Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples
quickpsy is a package to parametrically fit psychometric functions. In comparison with previous R packages, quickpsy was built to easily fit and plot data for multiple groups. Here, we describe the ...standard parametric model used to fit psychometric functions and the standard estimation of its parameters using maximum likelihood. We also provide examples of usage of quickpsy, including how allowing the lapse rate to vary can sometimes eliminate the bias in parameter estimation, but not in general. Finally, we describe some implementation details, such as how to avoid the problems associated to round-off errors in the maximisation of the likelihood or the use of closures and non-standard evaluation functions
R Fischetti, Tony; Lantz, Brett; Abedin, Jaynal ...
2016
eBook
Master the art of building analytical models using R About This BookLoad, wrangle, and analyze your data using the world's most powerful statistical programming languageBuild and customize ...publication-quality visualizations of powerful and stunning R graphsDevelop key skills and techniques with R to create and customize data mining algorithmsUse R to optimize your trading strategy and build up your own risk management systemDiscover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with RWho This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will LearnDescribe and visualize the behavior of data and relationships between dataGain a thorough understanding of statistical reasoning and samplingHandle missing data gracefully using multiple imputationCreate diverse types of bar charts using the default R functionsFamiliarize yourself with algorithms written in R for spatial data mining, text mining, and so onUnderstand relationships between market factors and their impact on your portfolioHarness the power of R to build machine learning algorithms with real-world data science applicationsLearn specialized machine learning techniques for text mining, big data, and moreIn Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best
practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.
Practice R Treischl, Edgar J
2023, 2023-05-08
eBook
Many students learn to analyze data using commercial packages, even though there is an open-source software with cutting-edge possibilities: R, a programming language with countless cool features for ...applied empirical research. Practice R introduces R to social science students, inspiring them to consider R as an excellent choice. In a non-technical pragmatic way, this book covers all typical steps of applied empirical research. Learn how to prepare, analyze, and visualize data in R. Discover how to collect data, generate reports, or automate error-prone tasks. The book is accompanied by an R package. This provides further learning materials that include interactive tutorials, challenging you with typical problems of applied research. This way, you can immediately practice the knowledge you have learned. The package also includes the source code of each chapter and templates that help to create reports. Practice R has social science students in mind, nonetheless a broader audience may use Practice R to become a proficient R user.
PDF
