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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Previous: , Up: An Introduction to R   [Contents][Index]