Chapter 19 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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