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Full Product Details

Author:   Owen Jones ,  Robert Maillardet ,  Andrew Robinson
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Volume:   21
Dimensions:   Width: 15.60cm , Height: 3.30cm , Length: 23.40cm
Weight:   0.794kg
ISBN:  

9781420068726

ISBN 10:   1420068725
Pages:   474
Publication Date:   17 March 2009
Audience:   Professional and scholarly ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Replaced By:   9781466569997
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Part I: PROGRAMMING Setting Up Installing R Starting R Working Directory Writing Scripts Help Supporting Material R as a Calculating Environment Arithmetic Variables Functions Vectors Missing data Expressions and assignments Logical expressions Matrices The workspace Basic Programming Introduction Branching with if Looping with for Looping with while Vector-based programming Program flow Basic debugging Good programming habits I/O: Input and Output Text Input from a file Input from the keyboard Output to a file Plotting Programming with Functions Functions Scope and its consequences Optional arguments and default values Vector-based programming using functions Recursive programming Debugging functions Sophisticated Data Structures Factors Dataframes Lists The apply family Better Graphics Introduction Graphics parameters: par Graphical augmentation Mathematical typesetting Permanence Grouped graphs: lattice 3D-plots Pointers to Further Programming Techniques Packages Frames and environments Debugging again Object-oriented programming: S3 Object-oriented programming: S4 Compiled code Further reading Part II: NUMERICAL TECHNIQUES Numerical Accuracy and Program Efficiency Machine representation of numbers Significant digits Time Loops versus vectors Memory Caveat Root-Finding Introduction Fixed-point iteration The Newton-Raphson method The secant method The bisection method Numerical Integration Trapezoidal rule Simpson’s rule Adaptive quadrature Optimisation Newton’s method for optimisation The golden-section method Multivariate optimisation Steepest ascent Newton’s method in higher dimensions Optimisation in R and the wider world A curve fitting example Part III: PROBABILITY AND STATISTICS Probability The probability axioms Conditional probability Independence The Law of Total Probability Bayes’ theorem Random Variables Definition and distribution function Discrete and continuous random variables Empirical cdf’s and histograms Expectation and finite approximations Transformations Variance and standard deviation The Weak Law of Large Numbers Discrete Random Variables Discrete random variables in R Bernoulli distribution Geometric distribution Negative binomial distribution Poisson distribution Continuous Random Variables Continuous random variables in R Uniform distribution 282 Lifetime models: exponential and Weibull The Poisson process and the gamma distribution Sampling distributions: normal, x2, and t Parameter Estimation Point Estimation The Central Limit Theorem Confidence intervals Monte-Carlo confidence intervals Part IV: SIMULATION Simulation Simulating iid uniform samples Simulating discrete random variables Inversion method for continuous rv Rejection method for continuous rv Simulating normals Monte-Carlo Integration Hit-and-miss method (Improved) Monte-Carlo integration Variance Reduction Antithetic sampling Importance sampling Control variates Case Studies Introduction Epidemics Inventory Seed dispersal Student Projects The level of a dam Roulette Buffon’s needle and cross Insurance risk Squash Stock prices Glossary of R commands Programs and functions developed in the text Index

Reviews

It is not often that I think that a statistics text is one that most scientifc statisticians should have in their personal libraries. Introduction to Scientific Programming and Simulation Using R is such a text. ! This text provides scientific researchers with a working knowledge of R for both reviewing and for engaging in the statistical evaluation of scientific data. !It is particularly useful for understanding and developing modeling and simulation software. I highly recommend the text, finding it to be one of the most useful books I have read on the subject. --Journal of Statistical Software, September 2010, Volume 36 The authors have written an excellent introduction to scientific programming with R. Their clear prose, logical structure, well-documented code and realistic examples made the book a pleasure to read. One particularly useful feature is the chapter of cases studies at the end, which not only demonstrates complete analyses but also acts as a pedagogical tool to review and integrate material introduced throughout the book. ! I would strongly recommend this book for readers interested in using R for simulations, particularly for those new to scientific programming or R. It is also very student-friendly and would be suitable either as a course textbook or for self-study. --Significance, September 2009 !I think that the techniques of scientific programming presented will soon enable the novice to apply statistical models to real-world problems. The writing style is easy to read and the book is suitable for private study. If you have never read a book on scientific programming and simulation, then I recommend that you start with this one. --International Statistical Review, 2009

It is not often that I think that a statistics text is one that most scientiifc statisticians should have in their personal libraries. Introduction to Scientific Programming and Simulation Using R is such a text. ! This text provides scientific researchers with a working knowledge of R for both reviewing and for engaging in the statistical evaluation of scientific data. !It is particularly useful for understanding and developing modeling and simulation software. I highly recommend the text, finding it to be one of the most useful books I have read on the subject. --Journal of Statistical Software, September 2010, Volume 36 The authors have written an excellent introduction to scientific programming with R. Their clear prose, logical structure, well-documented code and realistic examples made the book a pleasure to read. One particularly useful feature is the chapter of cases studies at the end, which not only demonstrates complete analyses but also acts as a pedagogical tool to review and integrate material introduced throughout the book. ! I would strongly recommend this book for readers interested in using R for simulations, particularly for those new to scientific programming or R. It is also very student-friendly and would be suitable either as a course textbook or for self-study. --Significance, September 2009 !I think that the techniques of scientific programming presented will soon enable the novice to apply statistical models to real-world problems. The writing style is easy to read and the book is suitable for private study. If you have never read a book on scientific programming and simulation, then I recommend that you start with this one. --International Statistical Review, 2009

Author Information

University of Melbourne, Parkville, Australia

Tab Content 6

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