(R-intro.info)Concept index
Appendix E Concept index
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[index ]
Accessing builtin datasets- Accessing builtin datasets.
(line 6)
- Additive models
- Some non-standard models.
(line 34)
- Analysis of variance
- Analysis of variance and model comparison.
(line 6)
- Arithmetic functions and operators
- Vector arithmetic. (line 21)
- Arrays
- Arrays. (line 6)
- Assignment
- Vectors and assignment.
(line 25)
- Attributes
- Objects. (line 6)
- Binary operators
- Defining new binary operators.
(line 6)
- Box plots
- One- and two-sample tests.
(line 28)
- Character vectors
- Character vectors. (line 6)
- Classes
- The class of an object.
(line 6)
- Classes <1>
- Object orientation. (line 6)
- Concatenating lists
- Concatenating lists. (line 6)
- Contrasts
- Contrasts. (line 6)
- Control statements
- Control statements. (line 6)
- CRAN
- Contributed packages and CRAN.
(line 6)
- Customizing the environment
- Customizing the environment.
(line 6)
- Data frames
- Data frames. (line 6)
- Default values
- Named arguments and defaults.
(line 6)
- Density estimation
- Examining the distribution of a set of data.
(line 49)
- Determinants
- Singular value decomposition and determinants.
(line 25)
- Diverting input and output
- Executing commands from or diverting output to a file.
(line 6)
- Dynamic graphics
- Dynamic graphics. (line 6)
- Eigenvalues and eigenvectors
- Eigenvalues and eigenvectors.
(line 6)
- Empirical CDFs
- Examining the distribution of a set of data.
(line 57)
- Factors
- Factors. (line 6)
- Factors <1>
- Contrasts. (line 11)
- Families
- Families. (line 6)
- Formulae
- Formulae for statistical models.
(line 6)
- Generalized linear models
- Generalized linear models.
(line 6)
- Generalized transpose of an array
- Generalized transpose of an array.
(line 6)
- Generic functions
- Object orientation. (line 6)
- Graphics device drivers
- Device drivers. (line 6)
- Graphics parameters
- The par() function. (line 6)
- Grouped expressions
- Grouped expressions. (line 6)
- Indexing of and by arrays
- Array indexing. (line 6)
- Indexing vectors
- Index vectors. (line 6)
- Kolmogorov-Smirnov test
- Examining the distribution of a set of data.
(line 100)
- Least squares fitting
- Least squares fitting and the QR decomposition.
(line 6)
- Linear equations
- Linear equations and inversion.
(line 6)
- Linear models
- Linear models. (line 6)
- Lists
- Lists. (line 6)
- Local approximating regressions
- Some non-standard models.
(line 17)
- Loops and conditional execution
- Loops and conditional execution.
(line 6)
- Matrices
- Arrays. (line 6)
- Matrix multiplication
- Multiplication. (line 6)
- Maximum likelihood
- Maximum likelihood. (line 6)
- Missing values
- Missing values. (line 6)
- Mixed models
- Some non-standard models.
(line 10)
- Named arguments
- Named arguments and defaults.
(line 6)
- Namespace
- Namespaces. (line 6)
- Nonlinear least squares
- Nonlinear least squares and maximum likelihood models.
(line 6)
- Object orientation
- Object orientation. (line 6)
- Objects
- Objects. (line 6)
- One- and two-sample tests
- One- and two-sample tests.
(line 6)
- Ordered factors
- Factors. (line 6)
- Ordered factors <1>
- Contrasts. (line 11)
- Outer products of arrays
- The outer product of two arrays.
(line 6)
- Packages
- R and statistics. (line 6)
- Packages <1>
- Packages. (line 6)
- Probability distributions
- Probability distributions.
(line 6)
- QR decomposition
- Least squares fitting and the QR decomposition.
(line 6)
- Quantile-quantile plots
- Examining the distribution of a set of data.
(line 71)
- Reading data from files
- Reading data from files.
(line 6)
- Recycling rule
- Vector arithmetic. (line 13)
- Recycling rule <1>
- The recycling rule. (line 6)
- Regular sequences
- Generating regular sequences.
(line 6)
- Removing objects
- Data permanency and removing objects.
(line 22)
- Robust regression
- Some non-standard models.
(line 25)
- Scope
- Scope. (line 6)
- Search path
- Managing the search path.
(line 6)
- Shapiro-Wilk test
- Examining the distribution of a set of data.
(line 91)
- Singular value decomposition
- Singular value decomposition and determinants.
(line 6)
- Statistical models
- Statistical models in R.
(line 6)
- Student's t test
- One- and two-sample tests.
(line 33)
- Tabulation
- Frequency tables from factors.
(line 6)
- Tree-based models
- Some non-standard models.
(line 46)
- Updating fitted models
- Updating fitted models.
(line 6)
- Vectors
- Simple manipulations numbers and vectors.
(line 6)
- Wilcoxon test
- One- and two-sample tests.
(line 87)
- Workspace
- Data permanency and removing objects.
(line 17)
- Writing functions
- Writing your own functions.
(line 6)
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