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

Narayanachart, Tattar Prabhanjan; Prabhanjan Narayanachart, Tattar

Brossura

ISBN 10: 1849519447 ISBN 13: 9781849519441

Casa editrice: Packt Publishing, 2013

Riassunto

R Statistical Application Development by Example Beginner's Guide

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
...

Dettagli dell'edizione

• Editore: Packt Publishing
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 1849519447
• ISBN 13: 9781849519441
• Rilegatura: Copertina flessibile
• Numero di pagine: 344
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile