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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
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Seitenzahl: 496
Veröffentlichungsjahr: 2015
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Table of Contents
Mastering Scientific Computing with R
Mastering Scientific Computing with R
Copyright © 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: January 2015
Production reference: 1270115
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78355-525-3
www.packtpub.com
Cover image by Jason Dupuis Mayer (<[email protected]>)
Credits
Authors
Paul Gerrard
Radia M. Johnson
Reviewers
Laurent Drouet
Ratanlal Mahanta
Mzabalazo Z. Ngwenya
Donato Teutonico
Commissioning Editor
Kartikey Pandey
Acquisition Editor
Greg Wild
Content Development Editor
Akshay Nair
Technical Editors
Rosmy George
Ankita Thakur
Copy Editors
Shivangi Chaturvedi
Pranjali Chury
Puja Lalwani
Adithi Shetty
Project Coordinator
Mary Alex
Proofreaders
Simran Bhogal
Martin Diver
Ameesha Green
Paul Hindle
Bernadette Watkins
Indexer
Priya Subramani
Graphics
Sheetal Aute
Disha Haria
Abhinash Sahu
Production Coordinator
Conidon Miranda
Cover Work
Conidon Miranda
About the Authors
Paul Gerrard is a physician and healthcare researcher who is based out of Portland, Maine, where he currently serves as the medical director of the cardiopulmonary rehabilitation program at New England Rehabilitation Hospital of Portland. He studied business economics in college. After completing medical school, he did a residency in physical medicine and rehabilitation at Harvard Medical School and Spaulding Rehabilitation Hospital, where he served as chief resident and stayed on as faculty at Harvard before moving to Portland. He continues to collaborate on research projects with researchers at other academic institutions within the Boston area and around the country. He has published and presented research on a range of topics, including traumatic brain injury, burn rehabilitation, health outcomes, and the epidemiology of disabling medical conditions.
I would like to thank my beautiful wife, Deirdre, and my son, Patrick. My work on this book is dedicated to the loving memory of Fiona.
Radia M. Johnson has a doctorate degree in immunology and currently works as a research scientist at the Institute for Research in Immunology and Cancer at the Université de Montréal, where she uses genomics and bioinformatics to identify and characterize the molecular changes that contribute to cancer development. She routinely uses R and other computer programming languages to analyze large data sets from ongoing collaborative projects. Since obtaining her PhD at the University of Toronto, she has also worked as a research associate at the University of Cambridge in Hematology, where she gained experience using system biology to study blood cancer.
I would like to thank Dr. Charlie Massie for teaching me to love programming in R and Dr. Phil Kousis for all his support through the years. You are both excellent mentors and wonderful friends!
About the Reviewers
Laurent Drouet holds a PhD in economics and social sciences from the University of Geneva, Switzerland, and a master's degree in applied mathematics from the Institute of Applied Mathematics of Angers, France. He was also a postdoctoral research fellow at the Research Lab of Economics and Environmental Management at the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. He was also a researcher at the Public Research Center Tudor, Luxembourg. He is currently a senior researcher at Fondazione Eni Enrico Mattei (FEEM) and a research affiliate at Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy.
His main research is related to integrated assessment modeling and energy modeling. For more than a decade, he designed scientific tools to perform data analysis for this type of modeling. He also built optimization frameworks to couple models of many kinds (such as climate models, air quality models, and economy models). He created and developed the bottom-up techno-economic energy model ETEM to study optimal energy policies at urban or national levels.
I want to thank my wife for her support every day both in my private life and professional life.
Ratanlal Mahanta holds an MSc in computational finance. He is currently working at GPSK Investment Group as a senior quantitative analyst. He has 4 years of experience in quantitative trading and strategies developments for sell side and risk consulting firms. He is an expert in high frequency and algorithmic trading. He has expertise in these areas: quantitative trading (FX, equities, futures and options, and engineering on derivatives); algorithms—partial differential equations, stochastic differential equations, the finite difference method, Monte Carlo, and Machine Learning; code—R programming, C++, MATLAB, HPC, and scientific computing; data analysis—Big Data analytic [EOD to TBT], Bloomberg, Quandl, and Quantopian; and strategies—vol-arbitrage, vanilla and exotic options modeling, trend following, mean reversion, co-integration, Monte Carlo simulations, ValueatRisk, stress testing, buy side trading strategies with high Sharpe ratio, credit risk modeling, and credit rating.
He has reviewed Mastering R for Quantitative Finance, Packt Publishing. He is currently reviewing two other books for Packt Publishing: Mastering Python for Data Science and Machine Learning with R Cookbook.
Mzabalazo Z. Ngwenya holds a postgraduate degree in mathematical statistics from the University of Cape Town. He has worked extensively in the field of statistical consulting, wherein he utilized varied statistical software including R. His area of interest are primarily centered around statistical computing. Previously, he was involved in reviewing Learning RStudio for R Statistical Computing, Mark P.J. van der Loo and Edwin de Jonge; R Statistical Application Development Example Beginner's Guide, Prabhanjan Narayanachar Tattar; Machine Learning with R, Brett Lantz; R Graph Essentials, David Alexandra Lillis, and R Object-oriented Programming, Kelly Black, all by Packt Publishing. He currently works as a biometrician.
Donato Teutonico has several years of experience in modeling and the simulation of drug effects and clinical trials in industrial and academic settings. He received his PharmD degree from the University of Turin, Italy, specializing in chemical and pharmaceutical technology, and his PhD in pharmaceutical sciences from Paris-Sud University, France.
He is the author of two R packages for pharmacometrics, CTStemplate and panels-for-pharmacometrics, which are both available on Google Code. He is also the author of Instant R Starter, Packt Publishing.
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Preface
As an open source computing environment, R is rapidly becoming the lingua franca of the statistical computing community. R's powerful base functions, powerful statistical tools, open source nature, and avid user community have led to R having an expansive library of powerful, cutting-edge quantitative methods not yet available to users of other high-cost statistical programs.
With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.
Beginning with an overview of fundamental R concepts, including data types, R program flow, and basic coding techniques, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks, including testing for statistically significant differences between groups and model relationships in data. You will also learn parametric and nonparametric techniques for both difference testing and relationship modeling.
You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding a structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation, learn about an advanced analytical method, and finish by going to the next level with advanced data management focused on dealing with messy and problematic datasets that serious analysts deal with daily.
By the end of this book, you will be able to undertake publication-quality data analysis in R.
What this book covers
Chapter 1, Programming with R, presents an overview of how data is stored and accessed in R. Then, we will go over how to load data into R using built-in functions and useful packages for easy import from Excel worksheets. We will also cover how to use flow control statements and functions to reduce complexity and help you program more efficiently.
Chapter 2, Statistical Methods with R, presents an overview of how to summarize your data and get useful statistical information for downstream analysis. We will show you how to plot and get statistical information from probability distributions and how to test the fit of your sample distribution to well-defined probability distributions.
Chapter 3, Linear Models, covers linear models, which are probably the most commonly used statistical methods to study the relationships between variables. The Generalized linear model section will delve into a bit more detail than typical R books, discussing the nature of link functions and canonical link functions.
Chapter 4, Nonlinear Methods, reviews applications of nonlinear methods in R using both parametric and nonparametric methods for both theory-driven and exploratory analysis.
Chapter 5, Linear Algebra, covers algebra techniques in R. We will also learn linear algebra operations including transposition, inversion, matrix multiplication, and a number of matrix transformations.
Chapter 6, Principal Component Analysis and the Common Factor Model, helps you understand the application of linear algebra to covariance and correlation matrices. We will cover how to use PCA to account for total variance in a set of variables and how to use EFA to model common variance among these variables in R.
Chapter 7, Structural Equation Modeling and Confirmatory Factor Analysis, covers the fundamental ideas underlying structural equation modeling, which are often overlooked in other books discussing SEM in R, and then delve into how SEM is done in R.
Chapter 8, Simulations, explains how to perform basic sample simulations and how to use simulations to answer statistical problems. We will also learn how to use R to generate random numbers, and how to simulate random variables from several common probability distributions.
Chapter 9, Optimization, explores a variety of methods and techniques to optimize a variety of functions. We will also cover how to use a wide range of R packages and functions to set up, solve, and visualize different optimization problems.
Chapter 10, Advanced Data Management, walks you through the basic techniques for data handling and some basic memory management considerations.
What you need for this book
The software that we require for this book is R Version 3.0.1 or higher, OpenMx Version 1.4, and RStudio.
Who this book is for
If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "You can also retrieve additional information on the objects stored in your environment using the str() function."
A block of code is set as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "To install R on Windows, click on Download R for Windows, and then click on base for the download link and installation instructions."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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Errata
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Questions
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