Appendix E — Concept index

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Index Entry   Section
A
Accessing builtin datasets:   Accessing builtin datasets
Additive models:   Some non-standard models
Analysis of variance:   Analysis of variance and model comparison
Arithmetic functions and operators:   Vector arithmetic
Arrays:   Arrays
Assignment:   Vectors and assignment
Attributes:   Objects
B
Binary operators:   Defining new binary operators
Box plots:   One- and two-sample tests
C
Character vectors:   Character vectors
Classes:   The class of an object
Classes:   Object orientation
Concatenating lists:   Concatenating lists
Contrasts:   Contrasts
Control statements:   Control statements
CRAN:   Contributed packages and CRAN
Customizing the environment:   Customizing the environment
D
Data frames:   Data frames
Default values:   Named arguments and defaults
Density estimation:   Examining the distribution of a set of data
Determinants:   Singular value decomposition and determinants
Diverting input and output:   Executing commands from or diverting output to a file
Dynamic graphics:   Dynamic graphics
E
Eigenvalues and eigenvectors:   Eigenvalues and eigenvectors
Empirical CDFs:   Examining the distribution of a set of data
F
Factors:   Factors
Factors:   Contrasts
Families:   Families
Formulae:   Formulae for statistical models
G
Generalized linear models:   Generalized linear models
Generalized transpose of an array:   Generalized transpose of an array
Generic functions:   Object orientation
Graphics device drivers:   Device drivers
Graphics parameters:   The par() function
Grouped expressions:   Grouped expressions
I
Indexing of and by arrays:   Array indexing
Indexing vectors:   Index vectors
K
Kolmogorov-Smirnov test:   Examining the distribution of a set of data
L
Least squares fitting:   Least squares fitting and the QR decomposition
Linear equations:   Linear equations and inversion
Linear models:   Linear models
Lists:   Lists
Local approximating regressions:   Some non-standard models
Loops and conditional execution:   Loops and conditional execution
M
Matrices:   Arrays
Matrix multiplication:   Multiplication
Maximum likelihood:   Maximum likelihood
Missing values:   Missing values
Mixed models:   Some non-standard models
N
Named arguments:   Named arguments and defaults
Namespace:   Namespaces
Nonlinear least squares:   Nonlinear least squares and maximum likelihood models
O
Object orientation:   Object orientation
Objects:   Objects
One- and two-sample tests:   One- and two-sample tests
Ordered factors:   Factors
Ordered factors:   Contrasts
Outer products of arrays:   The outer product of two arrays
P
Packages:   R and statistics
Packages:   Packages
Probability distributions:   Probability distributions
Q
QR decomposition:   Least squares fitting and the QR decomposition
Quantile-quantile plots:   Examining the distribution of a set of data
R
Reading data from files:   Reading data from files
Recycling rule:   Vector arithmetic
Recycling rule:   The recycling rule
Regular sequences:   Generating regular sequences
Removing objects:   Data permanency and removing objects
Robust regression:   Some non-standard models
S
Scope:   Scope
Search path:   Managing the search path
Shapiro-Wilk test:   Examining the distribution of a set of data
Singular value decomposition:   Singular value decomposition and determinants
Statistical models:   Statistical models in R
Student’s t test:   One- and two-sample tests
T
Tabulation:   Frequency tables from factors
Tree-based models:   Some non-standard models
U
Updating fitted models:   Updating fitted models
V
Vectors:   Simple manipulations numbers and vectors
W
Wilcoxon test:   One- and two-sample tests
Workspace:   Data permanency and removing objects
Writing functions:   Writing your own functions
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