R Statistical Application Development By Example Beginners Guide

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Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Data Characteristics
- Questionnaire and its components
  - Understanding the data characteristics in an R environment
- Experiments with uncertainty in computer science
- R installation
- Continuous distribution
- Summary
Chapter 2: Import/Export Data
- data.frame and other formats
  - Constants, vectors, and matrices
- Time for action – understanding constants, vectors, and basic arithmetic
- Time for action – matrix computations
  - The list object
- Time for action – creating a list object
  - The data.frame object
- Time for action – creating a data.frame object
  - The table object
- Time for action – creating the Titanic dataset as a table object
- read.csv, read.xls, and the foreign package
- Time for action – importing data from external files
  - Importing data from MySQL
- Exporting data/graphs
  - Exporting R objects
  - Exporting graphs
- Time for action – exporting a graph
- Managing an R session
- Time for action – session management
- Summary
Chapter 3: Data Visualization
- Visualization techniques for categorical data
  - Bar charts
    - Going through the built-in examples of R
- Time for action – bar charts in R
  - Dot charts
- Time for action – dot charts in R
  - Spine and mosaic plots
- Time for action – the spine plot for the shift and operator data
- Time for action – the mosaic plot for the Titanic dataset
  - Pie charts and the fourfold plot
- Visualization techniques for continuous variable data
  - Boxplot
- Time for action – using the boxplot
  - Histograms
- Time for action – understanding the effectiveness of histograms
  - Scatter plots
- Time for action – plot and pairs R functions
  - Pareto charts
- A brief peek at ggplot2
- Time for action – qplot
- Time for action – ggplot
- Summary
Chapter 4: Exploratory Analysis
- Essential summary statistics
- Time for action – the essential summary statistics for "The Wall" dataset
- The stem-and-leaf plot
- Time for action – the stem function in play
- Letter values
- Data re-expression
- Bagplot – a bivariate boxplot
- Time for action – the bagplot display for a multivariate dataset
- The resistant line
- Time for action – the resistant line as a first regression model
- Smoothing data
- Time for action – smoothening the cow temperature data
- Median polish
- Time for action – the median polish algorithm
- Summary
Chapter 5: Statistical Inference
- Maximum likelihood estimator
  - Visualizing the likelihood function
- Time for action – visualizing the likelihood function
  - Finding the maximum likelihood estimator
  - Using the fitdistr function
- Time for action – finding the MLE using mle and fitdistr functions
- Confidence intervals
- Time for action – confidence intervals
- Hypotheses testing
  - Binomial test
- Time for action – testing the probability of success
  - Tests of proportions and the chi-square test
- Time for action – testing proportions
  - Tests based on normal distribution – one sample
- Time for action – testing one-sample hypotheses
  - Tests based on normal distribution – two sample
- Time for action – testing two-sample hypotheses
- Summary
Chapter 6: Linear Regression Analysis
- The simple linear regression model
  - What happens to the arbitrary choice of parameters?
- Time for action – the arbitrary choice of parameters
  - Building a simple linear regression model
- Time for action – building a simple linear regression model
  - ANOVA and the confidence intervals
- Time for action – ANOVA and the confidence intervals
  - Model validation
- Time for action – residual plots for model validation
- Multiple linear regression model
  - Averaging k simple linear regression models or a multiple linear regression model
- Time for action – averaging k simple linear regression models
  - Building a multiple linear regression model
- Time for action – building a multiple linear regression model
  - The ANOVA and confidence intervals for the multiple linear regression model
- Time for action – the ANOVA and confidence intervals for the multiple linear regression model
  - Useful residual plots
- Time for action – residual plots for the multiple linear regression model
- Regression diagnostics
- The multicollinearity problem
- Time for action – addressing the multicollinearity problem for the Gasoline data
- Model selection
  - Stepwise procedures
    - The backward elimination
    - The forward selection
  - Criterion-based procedures
- Time for action – model selection using the backward, forward, and AIC criteria
- Summary
Chapter 7: The Logistic Regression Model
- The binary regression problem
- Time for action – limitations of linear regression models
- Probit regression model
- Time for action – understanding the constants
- Logistic regression model
- Time for action – fitting the logistic regression model
  - Hosmer-Lemeshow goodness-of-fit test statistic
- Time for action – The Hosmer-Lemeshow goodness-of-fit statistic
- Model validation and diagnostics
  - Residual plots for the GLM
- Time for action – residual plots for the logistic regression model
  - Influence and leverage for the GLM
- Time for action – diagnostics for the logistic regression
- Receiving operator curves
- Time for action – ROC construction
- Logistic regression for the German credit screening dataset
- Time for action – logistic regression for the German credit dataset
- Summary
Chapter 8: Regression Models with Regularization
- The overfitting problem
- Time for action – understanding overfitting
- Regression spline
  - Basis functions
  - Piecewise linear regression model
- Time for action – fitting piecewise linear regression models
  - Natural cubic splines and the general B-splines
- Time for action – fitting the spline regression models
- Ridge regression for linear models
- Time for action – ridge regression for the linear regression model
- Ridge regression for logistic regression models
- Time for action – ridge regression for the logistic regression model
- Another look at model assessment
- Time for action – selecting lambda iteratively and other topics
- Summary
Chapter 9: Classification and Regression Trees
- Recursive partitions
- Time for action – partitioning the display plot
  - Splitting the data
  - The first tree
- Time for action – building our first tree
- The construction of a regression tree
- Time for action – the construction of a regression tree
- The construction of a classification tree
- Time for action – the construction of a classification tree
- Classification tree for the German credit data
- Time for action – the construction of a classification tree
- Pruning and other finer aspects of a tree
- Time for action – pruning a classification tree
- Summary
Chapter 10: CART and Beyond
- Improving CART
- Time for action – cross-validation predictions
- Bagging
  - The bootstrap
- Time for action – understanding the bootstrap technique
  - The bagging algorithm
- Time for action – the bagging algorithm
- Random forests
- Time for action – random forests for the German credit data
- The consolidation
- Time for action – random forests for the low birth weight data
- Summary
Appendix: References
Index

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