Computational statistics an introduction to R
Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming, and graphics. Integrating R code and examples throughout, the text only requires basic knowledge of s...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press
2009.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629752906719 |
Tabla de Contenidos:
- 1 Basic data analysis
- R programming conventions
- Generation of random numbers and patterns
- Random numbers
- Patterns
- Case study: distribution diagnostics
- Distribution functions
- Histograms
- Barcharts
- Statistics of distribution functions; Kolmogorov-Smirnov tests
- Monte Carlo confidence bands
- Statistics of histograms and related plots; X2-tests
- Moments and quantiles
- R complements
- Random numbers
- Graphical comparisons
- Functions
- Enhancing graphical displays
- R internals
- parse
- eval
- Executing files
- Packages
- Statistical summary
- Literature and additional references
- 2 Regression
- General regression model
- Linear model
- Factors
- Least squares estimation
- Regression diagnostics
- More examples for linear models
- Model formulae
- Gauss-Markov estimator and residuals
- Variance decomposition and analysis of variance
- Simultaneous inference
- Scheff́e's confidence bands
- Tukey's confidence intervals
- Case study: titre plates
- Beyond linear regression
- Transformations
- Generalised linear models
- Local regression
- R complements
- Discretisation
- External data
- Testing software
- R data types
- Classes and polymorphic functions
- Extractor functions
- Statistical summary
- Literature and additional references
- 3 Comparisons
- Shift/scale families, and stochastic order
- QQ plot, PP plot, and comparison of distributions
- Kolmogorov-Smirnov tests
- Tests for shift alternatives
- Road map
- Power and confidence
- Theoretical power and confidence
- Simulated power and confidence
- Quantile estimation
- Qualitative features of distributions
- Statistical summary
- Literature and additional references
- 4 Dimensions 1, 2, 3, ..., c
- R Complements
- Dimensions
- Selections
- Projections
- Marginal distributions and scatter plot matrices
- Projection pursuit
- Projections for dimensions 1, 2, 3, ... 7
- Parallel coordinates
- Sections, conditional distributions and coplots
- Transformations and dimension reduction
- Higher dimensions
- Linear case
- Partial residuals and added variable plots
- Non-linear case
- Example: cusp non-linearity
- Case study: Melbourne temperature data
- Curse of dimensionality
- Case study: body fat
- High dimensions
- Statistical summary
- R as a programming language and environment
- Help and information
- Names and search paths
- Administration and customisation
- Basic data types
- Output for objects
- Object inspection
- System inspection
- Complex data types
- Accessing components
- Data manipulation
- Operators
- Functions
- Debugging and profiling
- Control structures
- Input and output to data streams; external data
- Libraries, packages
- Mathematical operators and functions; linear algebra
- Model descriptions
- Graphic functions
- High-level graphics
- Low-level graphics
- Annotations and legends
- Graphic parameters and Llyout
- Elementary statistical functions
- Distributions, random numbers, densities...
- Computing on the language.
