Modeling Psychophysical Data in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modeling Psychophysical Data in R write by Kenneth Knoblauch. This book was released on 2012-09-02. Modeling Psychophysical Data in R available in PDF, EPUB and Kindle. Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
Novel Applications of Bayesian and Other Models in Translational Neuroscience
Novel Applications of Bayesian and Other Models in Translational Neuroscience - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Novel Applications of Bayesian and Other Models in Translational Neuroscience write by Reza Rastmanesh . This book was released on 2024-05-06. Novel Applications of Bayesian and Other Models in Translational Neuroscience available in PDF, EPUB and Kindle. It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently publishe
Data Wrangling with R
Data Wrangling with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Wrangling with R write by Bradley C. Boehmke, Ph.D.. This book was released on 2016-11-17. Data Wrangling with R available in PDF, EPUB and Kindle. This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets
Psychophysics
Psychophysics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Psychophysics write by Frederick A.A. Kingdom. This book was released on 2016-01-04. Psychophysics available in PDF, EPUB and Kindle. Psychophysics: A Practical Introduction, Second Edition, is the primary scientific tool for understanding how the physical world of colors, sounds, odors, movements, and shapes translates into the sensory world of sight, hearing, touch, taste, and smell; in other words, how matter translates into mind. This timely revision provides a unique introduction to the techniques for researching and understanding how the brain translates the external physical world to the internal world of sensation. The revision expands and refines coverage of the basic tools of psychophysics research and better integrates the theory with the supporting software. The new edition continues to be the only book to combine, in a single volume, the principles underlying the science of psychophysical measurement and the practical tools necessary to analyze data from psychophysical experiments. The book, written in a tutorial style, will appeal to new researchers as well as to seasoned veterans. This introduction to psychophysics research methods will be of interest to students, scholars and researchers within sensory neuroscience, vision research, behavioral neuroscience, and the cognitive sciences. Presents a large variety of analytical methods explained for the non-expert Provides a novel classification scheme for psychophysics experiments Disseminates the pros and cons of different psychophysical procedures Contains practical tips for designing psychophysical experiments
Applied Survival Analysis Using R
Applied Survival Analysis Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Survival Analysis Using R write by Dirk F. Moore. This book was released on 2016-05-11. Applied Survival Analysis Using R available in PDF, EPUB and Kindle. Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.
