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Preface ix
Getting started 1
What is statistical programming? 1
Outline of the book 2
The R package 3
Why use a command line? 3
Font conventions 4
Installation of R 4
Introduction to the R language 5
Starting and quitting R 5
Recording your work 6
Basic features of R 7
Calculating with R 7
Named storage 7
Functions 9
Exact or approximate? 9
R is case-sensitive 12
Listing the objects in the workspace 12
Vectors 12
Extracting elements from vectors 13
Vector arithmetic 14
Simple patterned vectors 15
Missing values and other special values 16
Character vectors 16
Factors 17
More on extracting elements from vectors 18
Matrices and arrays 18
Data frames 19
Dates and times 21
Built-in functions and online help 21
Built-in examples 22
Finding help when you don't know the function name 23
Built-in graphics functions 23
Additional elementary built-in functions 25
Logical vectors and relational operators 26
Boolean algebra 26
Logical operations in R 27
Relational operators 28
Data input and output 29
Changing directories 29
dump () and source () 29
Redirecting R output 30
Saving and retrieving image files 31
Data frames and the read.table function 31
Lists 31
Chapter exercises 32
Programming statistical graphics 33
High-level plots 33
Bar charts and dot charts 34
Pie charts 35
Histograms 35
Box plots 36
Scatterplots 38
QQ plots 39
Choosing a high-level graphic 41
Low-level graphics functions 42
The plotting region and margins 42
Adding to plots 43
Setting graphical parameters 45
Chapter exercises 46
Programming with R 47
Flow control 47
The for () loop 47
The if () statement 50
The while () loop 54
Newton's method for root finding 55
The repeat loop, and the break and next statements 57
Managing complexity through functions 59
What are functions? 59
Scope of variables 62
Miscellaneous programming tips 63
Using fix () 63
Documentation using # 64
Some general programming guidelines 65
Top-down design 67
Debugging and maintenance 72
Recognizing that a bug exists 72
Make the bug reproducible 73
Identify the cause of the bug 73
Fixing errors and testing 75
Look for similar errors elsewhere 75
The browser () and debug () functions 75
Efficient programming 77
Learn your tools 77
Use efficient algorithms 78
Measure the time your program takes 79
Be willing to use different tools 80
Optimize with care 80
Chapter exercises 80
Simulation 82
Monte Carlo simulation 82
Generation of pseudorandom numbers 83
Simulation of other random variables 88
Bernoulli random variables 88
Binomial random variables 89
Poisson random variables 93
Exponential random numbers 97
Normal random variables 99
Monte Carlo integration 101
Advanced simulation methods 104
Rejection sampling 104
Importance sampling 107
Chapter exercises 109
Computational linear algebra 112
Vectors and matrices in R 113
Constructing matrix objects 113
Accessing matrix elements; row and column names 115
Matrix properties 117
Triangular matrices 118
Matrix arithmetic 118
Matrix multiplication and inversion 119
Matrix inversion 120
The LU decomposition 121
Matrix inversion in R 122
Solving linear systems 123
Eigenvalues and eigenvectors 124
Advanced topics 125
The singular value decomposition of a matrix 125
The Choleski decomposition of a positive definite matrix 126
The QR decomposition of a matrix 127
The condition number of a matrix 128
Outer products 129
Kronecker products 129
apply () 129
Chapter exercises 130
Numerical optimization 132
The golden section search method 132
Newton-Raphson 135
The Nelder-Mead simplex method 138
Built-in functions 142
Linear programming 142
Solving linear programming problems in R 145
Maximization and other kinds of constraints 145
Special situations 146
Unrestricted variables 149
Integer programming 150
Alternatives to lp () 151
Quadratic programming 151
Chapter exercises 157
Review of random variables and distributions 158
Index 161
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Add A First Course in Statistical Programming with R, This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R, A First Course in Statistical Programming with R to the inventory that you are selling on WonderClubX
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Add A First Course in Statistical Programming with R, This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R, A First Course in Statistical Programming with R to your collection on WonderClub |