R Statistical Application Development by Example Beginner's Guide - Brossura

Narayanachar Tattar, Prabhanjan

Brossura

ISBN 10: 1849519447 ISBN 13: 9781849519441

Casa editrice: Packt Publishing, 2013

Riassunto

A self-learning guide for the user who needs statistical tools for understanding uncertainty in computer science data
Essential descriptive statistics, effective data visualization, and efficient model building
Every method explained through real data sets enables clarity and confidence for unforeseen scenarios

Su questo libro

L'autore:

Prabhanjan Narayanachar Tattar

Prabhanjan Narayanachar Tattar has seven years of experience with R software and has also co-authored the book A Course in Statistics with R published by Narosa Publishing House. The author has built two packages in R titled gpk and ACSWR. He has obtained a PhD (Statistics) from Bangalore University under the broad area of Survival Analysis and published several articles in peer-reviewed journals. During the PhD program, the author received the young Statistician honors in IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007) and also held a Junior and Senior Research Fellowship of CSIR-UGC.

Prabhanjan is working as a Business Analysis Advisor at Dell Inc, Bangalore. He is working for the Customer Service Analytics unit of the larger Dell Global Analytics arm of Dell.

Contenuti:

Preface
Chapter 1: Data Characteristics
Chapter 2: Import/Export Data
Chapter 3: Data Visualization
Chapter 4: Exploratory Analysis
Chapter 5: Statistical Inference
Chapter 6: Linear Regression Analysis
Chapter 7: The Logistic Regression Model
Chapter 8: Regression Models with Regularization
Chapter 9: Classification and Regression Trees
Chapter 10: CART and Beyond
Appendix: References
Index

Preface

Up

Chapter 1: Data Characteristics
Questionnaire and its components
Understanding the data characteristics in an R environment
Experiments with uncertainty in computer science
R installation
Using R packages
RSADBE – the book's R package
Discrete distribution
Discrete uniform distribution
Binomial distribution
Hypergeometric distribution
Negative binomial distribution
Poisson distribution
Continuous distribution
Uniform distribution
Exponential distribution
Normal distribution
Summary

Up

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

Up

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

Up

Chapter 4: Exploratory Analysis
Essential summary statistics
Percentiles, quantiles, and median
Hinges
The interquartile range
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

Up

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

Up

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
Leverage points
Influential points
DFFITS and DFBETAS
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

Up

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

Up

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

Up

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
...

Su questa edizione

• Editore: Packt Publishing
• Data di pubblicazione: 2013
• ISBN 10: 1849519447
• ISBN 13: 9781849519441
• Rilegatura: Copertina flessibile
• Numero di pagine: 344