A First Course in Statistical Programming with R,2nd Edition


A First Course in Statistical Programming with R
by 作者: W. John Braun - Duncan J. Murdoch
ISBN-10 书号: 1107576466
ISBN-13 书号: 9781107576469
Edition 版本: 2
Publisher Finelybook 出版日期: 2016-07-18
Pages: 230


Book Description
This new color edition of Braun and Murdoch’s bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems,extensive exploration of Markov chain Monte Carlo,expert advice on common error messages,motivating applications of matrix decompositions,and numerous new examples and exercises. This is the only introduction needed to start programming in R,the computing standard for analyzing data. Co-written by an R core team member and an established R author,this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the R system,this book emphasizes programming,including the principles that apply to most computing languages,and techniques used to develop more complex projects. Solutions,datasets,and any errata are available from the book’s website. The many examples,all from real applications,make it particularly useful for anyone working in practical data analysis.
Contents
Preface to the second edition page xi
Preface to the first edition xiii
1 Getting started 1
1.1 What is statistical programming? 1
1.2 Outline of this book 2
1.3 The R package 3
1.4 Why use a command line? 3
1.5 Font conventions 4
1.6 Installation of R and RStudio 4
1.7 Getting started in RStudio 5
1.8 Going further 6
2 Introduction to the R language 7
2.1 First steps 7
2.2 Basic features of R 11
2.3 Vectors in R 13
2.4 Data storage in R 22
2.5 Packages,libraries,and repositories 27
2.6 Getting help 28
2.7 Logical vectors and relational operators 34
2.8 Data frames and lists 37
2.9 Data input and output 43
3 Programming statistical graphics 49
3.1 High level plots 50
3.2 Choosing a high level graphic 62
3.3 Low level graphics functions 63
3.4 Other graphics systems 70
4 Programming with R 76
4.1 Flow control 76
4.2 Managing complexity through functions 91
4.3 The replicate() function 97
4.4 Miscellaneous programming tips 97
4.5 Some general programming guidelines 100
4.6 Debugging and maintenance 107
4.7 Efficient programming 113
5 Simulation 120
5.1 Monte Carlo simulation 120
5.2 Generation of pseudorandom numbers 121
5.3 Simulation of other random variables 126
5.4 Multivariate random number generation 142
5.5 Markov chain simulation 143
5.6 Monte Carlo integration 147
5.7 Advanced simulation methods 149
6 Computational linear algebra 158
6.1 Vectors and matrices in R 159
6.2 Matrix multiplication and inversion 166
6.3 Eigenvalues and eigenvectors 171
6.4 Other matrix decompositions 172
6.5 Other matrix operations 178
7 Numerical optimization 182
7.1 The golden section search method 182
7.2 Newton–Raphson 185
7.3 The Nelder–Mead simplex method 188
7.4 Built-in functions 191
7.5 Linear programming 192

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