Index

A

• agglomerative clustering
  • inner working / The inner working of agglomerative clustering
  • hclust(), using / Agglomerative clustering with hclust(), Exploring the results of votes in Switzerland
  • vote results, exploring / Exploring the results of votes in Switzerland
• analyses
  • performing, in R / Performing the analyses in R
• analyses, performing
  • C4.5, used for classification / Classification with C4.5
  • C50 / C50
  • CART / CART
  • predictions on testing set, examining / Examining the predictions on the testing set
  • conditional inference trees, in R / Conditional inference trees in R
• anova() function / Random intercepts and fixed slopes
• Apriori
  • support-based pruning / Generating itemsets with support-based pruning
  • confidence-based pruning / Generating rules by using confidence-based pruning
• apriori
  • about / Apriori – basic concepts
  • association rule / Association rules
  • itemsets / Itemsets
  • itemset, support / Support
  • confidence / Confidence
  • lift / Lift
  • inner working / The inner working of apriori
  • support-based pruning / Generating itemsets with support-based pruning
  • used, for analyzing data in R / Analyzing data with apriori in R
  • used, for basic analysis / Using apriori for basic analysis
  • detailed analysis / Detailed analysis with apriori
• arithmetic mean / Covariance and correlation
• association rule / Association rules
• attribute
  • about / A quick look at the Misc menu

B

• bar plots
  • example / Histograms and bar plots
• between sum of squares (BSS) / Understanding the data with the all.us.city.crime.1970 dataset
• Big Five Inventory
  • URL / Chapter 6 – Dimensionality Reduction with Principal Component Analysis
• binary attributes
  • hierarchical clustering, using on / The use of hierarchical clustering on binary attributes
• binomial distribution / The binomial distribution
• bootstrapping
  • about / Bootstrapping
• boxplots
  • example / Boxplots

C

• C4.5
  • about / C4.5
  • gain ratio / The gain ratio
  • post-pruning / Post-pruning
  • installing / Installing C4.5
• C5.0
  • about / C5.0
  • installing / Installing C5.0
• C50 / C50
• caret / Caret – a unified framework for classification
• caret package
  • used, for bootstrapping of predictive models / Cross-validation and bootstrapping of predictive models using the caret package, Bootstrapping, Performing bootstrapping in R with caret
  • used, for cross-validation of predictive models / Cross-validation, Performing cross-validation in R with caret
• CART
  • about / CART, CART
  • installing / Installing CART
  • pruning / Pruning
  • random forests, in R / Random forests in R
• cbind() function / Computing the closest cluster for each case
• Class attribute / Understanding decision trees
• classification, with C4.5
  • about / Classification with C4.5
  • unpruned tree / The unpruned tree
  • pruned tree / The pruned tree
• classification performance
  • computing / Computing the performance of classification
• coef() function / The null model
• conditional inference trees
  • about / Conditional inference trees and forests
  • installing / Installing conditional inference trees
  • in R / Conditional inference trees in R
• confidence-based pruning
  • used, for generating rules / Generating rules by using confidence-based pruning
• confint.merMod() function / Random intercepts and fixed slopes
• cor() function / Pearson's correlation
• cor.test() function / Pearson's correlation
• corpus
  • loading / Loading the corpus
  • processing / Preprocessing and inspecting the corpus
  • inspecting / Preprocessing and inspecting the corpus
• corpus.update() function / Collecting news articles in R from the New York Times article search API
• correlation
  • about / Covariance and correlation, Correlation
  • Pearson’s correlation / Pearson's correlation
  • Spearman’s correlation / Spearman's correlation
• covariance
  • about / Covariance and correlation, Covariance
  • formula / Covariance
• createDataPartition() function / Loading and preparing the data
• ctree() function / Installing conditional inference trees
• cutoff parameter / Random forests in R

D

• data
  • preparing / Loading and preparing the data
  • loading / Loading and preparing the data
• data analysis
  • correlation / Analyzing data in R: correlation and regression
  • regression / Analyzing data in R: correlation and regression
  • about / Analyzing data in R: correlation and regression
  • steps / First steps in the data analysis
  • regression, performing / Performing the regression
  • normality of residuals, checking / Checking for the normality of residuals
  • variance inflation, checking / Checking for variance inflation
  • potential mediations, examining / Examining potential mediations and comparing models
  • models, comparing / Examining potential mediations and comparing models
  • new data, predicting / Predicting new data
• data frames
  • testing / Creating the training and testing data frames
• data preparation
  • about / Data preparation
  • corpus, preprocessing / Preprocessing and inspecting the corpus
  • attributes, computing / Computing new attributes
• decision trees
  • about / Understanding decision trees
• detailed analysis, with apriori
  • about / Detailed analysis with apriori
  • data, preparing / Preparing the data
  • data, analyzing / Analyzing the data
  • association rules, coercing to data frame / Coercing association rules to a data frame
  • association rules, visualizing / Visualizing association rules
• developer key
  • URL / Collecting news articles in R from the New York Times article search API
• discretize() function / Preparing the data
• dist() function / Chapter 5 – Agglomerative Clustering Using hclust()
• dist() function function / Distance measures
• distance measures
  • using / Distance measures
• dotplot() function / Dotplots

E

• eigen() function / The inner working of Principal Component Analysis
• entropy / Entropy
• exercises / Exercises, Chapter 5 – Agglomerative Clustering Using hclust(), Chapter 8 – Probability Distributions, Covariance, and Correlation, Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes, Chapter 13 – Text Analytics with R

F

• factor() function / Histograms
• Female branch / Understanding decision trees
• forests / Conditional inference trees and forests

G

• gain ratio / The gain ratio
• glm() function / Classification using logistic regression
• GNU R
  • installing / Installing GNU R
  • URL, for installation on Windows / Installing GNU R
  • URL, for installation on Mac OS X / Installing GNU R
  • URL, for installation on Linux / Installing GNU R
• graphics
  • updating / Updating graphics
• Grouped matrix-based visualization / Visualizing association rules

H

• hclust()
  • using, with agglomerative clustering / Agglomerative clustering with hclust(), Exploring the results of votes in Switzerland
  / Chapter 5 – Agglomerative Clustering Using hclust()
• hierarchical clustering
  • using, on binary attributes / The use of hierarchical clustering on binary attributes
• hist() function / Histograms
• histogram() function / Histograms
• histograms
  • example / Histograms and bar plots
  / Histograms

I

• ID3 / ID3
  • about / ID3
  • entropy / Entropy
  • information gain / Information gain
• ifelse() function / Integrating supplementary external data
• information gain / Information gain
• installing
  • packages, in R / Installing packages in R
• intercept, simple regression
  • computing / Computing the intercept and slope coefficient
• interestMeasure() function / Analyzing the data
• itemsets / Itemsets

K

• k-means
  • used, for partition clustering / Learning by doing – partition clustering with kmeans()
  • using, with public datasets / Using k-means with public datasets
• k-NN
  • about / Understanding k-NN
  • working, with in R / Working with k-NN in R
  • k, selecting / How to select k
  • used, for document classification / Document classification with k-NN
• Kaiser Meyer Olkin (KMO) / PCA diagnostics
• kay.means() function / Internal validation
• knn() function / Understanding k-NN

L

• lattice package
  • loading / Loading and discovering the lattice package
  • discovering / Loading and discovering the lattice package
  • URL / Loading and discovering the lattice package
• lattice plots
  • discovering / Discovering other lattice plots
  • histograms / Histograms
  • stacked bars / Stacked bars
  • dotplot() function / Dotplots
  • data points, displaying as text / Displaying data points as text
• life.expectancy.1971 dataset
  • best number of clusters, searching / Finding the best number of clusters in the life.expectancy.1971 dataset
  • external validation / External validation
• linear regression
  • URL / First steps in the data analysis
• line plots
  • example / Line plots
• llines() function / Loading and discovering the lattice package
• lmer() function / The null model
• logistic regression
  • used, for document classification / Classification using logistic regression
• lpoints() function / Loading and discovering the lattice package
• lrect() function / Loading and discovering the lattice package
• ls() function / Loading and discovering the lattice package
• ltext() function / Loading and discovering the lattice package

M

• Male branch / Understanding decision trees
• maximum likelihood (ML) / Random intercepts and fixed slopes
• mean() function / The null model
• Measure of Sample Adequacy(MSA) / PCA diagnostics
• melt() function / Understanding k-NN
• menu bar, R console
  • about / The menu bar of the R console
  • File menu / A quick look at the File menu
  • Misc menu / A quick look at the Misc menu
• models
  • exporting, with PMML / Exporting models using PMML
• mtry parameter / Random forests in R
• multilevel modeling
  • about / Multilevel modeling in R
  • null model / The null model
  • random intercepts and fixed slopes / Random intercepts and fixed slopes
  • random intercepts and random slopes / Random intercepts and random slopes
• multilevel models
  • used, for predicting work satisfaction / Predictions using multilevel models
  • predict() function, using / Using the predict() function
  • prediction quality, assessing / Assessing prediction quality
• multilevel regression
  • about / Multilevel regression
  • random intercepts and fixed slopes / Random intercepts and fixed slopes
  • random intercepts and random slopes / Random intercepts and random slopes
• multipanel conditioning
  • discovering, with xyplot() function / Discovering multipanel conditioning with xyplot()
• multiple regression
  • working / Working with multiple regression
• multivariate outliers
  • URL / First steps in the data analysis

N

• naiveBayes() function / Understanding Naïve Bayes
• Naïve Bayes
  • about / Understanding Naïve Bayes
  • working with, in R / Working with Naïve Bayes in R
  • used, for document classification / Document classification with Naïve Bayes
• nested data
  • about / Nested data
  • examples / Nested data
  • Robinson effect / Nested data
  • Simpson’s paradox / Nested data
• news mining
  • about / Mining the news with R
  • document classification / A successful document classification
  • article topics, extracting / Extracting the topics of the articles
  • news articles, collecting / Collecting news articles in R from the New York Times article search API
• New York Times article search AP
  • news articles, collecting in R / Collecting news articles in R from the New York Times article search API
• normal distribution / The normal distribution
• NursesML dataset / Chapter 12 – Multilevel Analyses

O

• Outlier detection
  • about / Application – Outlier detection

P

• package installation
  • C4.5 / Installing C4.5
  • C5.0 / Installing C5.0
  • CART / Installing CART
  • random forest / Installing random forest
  • conditional inference trees / Installing conditional inference trees
  • data, loading / Loading and preparing the data
  • data, preparing / Loading and preparing the data
• packages
  • about / Packages
  • installing, in R / Installing packages in R
  • loading, in R / Loading packages in R
• panel.loess() function / Integrating supplementary external data
• partition clustering
  • k-means, using / Learning by doing – partition clustering with kmeans()
  • centroids, setting / Setting the centroids
  • distances, computing to centroids / Computing distances to centroids
  • closest cluster, computing / Computing the closest cluster for each case
  • main function, task performed / Tasks performed by the main function
  • internal validation / Internal validation
• PCA
  • inner working / The inner working of Principal Component Analysis
  • using, in R / Learning PCA in R
  • missing values, dealing with / Dealing with missing values
  • relevant components, selecting / Selecting how many components are relevant
  • components with loadings, naming / Naming the components using the loadings
  • scores / PCA scores
  • diagnostics / PCA diagnostics
• PCA scores
  • accessing / Accessing the PCA scores
  • for analysis / PCA scores for analysis
• Pearson’s correlation / Pearson's correlation
• plot() function / Understanding the data with the all.us.city.crime.1970 dataset
• plotLMER.fnc() function / Random intercepts and random slopes
• plots
  • formatting / Formatting plots
• PMML
  • used, for exporting models / Exporting models using PMML
  • about / What is PMML?
  • URL / What is PMML?
  • object structure, describing / A brief description of the structure of PMML objects
  • predictive model exportation, examples / Examples of predictive model exportation
• predict() function / Understanding Naïve Bayes
  • using / Using the predict() function
• predictions
  • examining, on testing set / Examining the predictions on the testing set
• predictive model exportation, examples
  • about / Examples of predictive model exportation
  • k-means objects, exporting / Exporting k-means objects
  • hierarchical clustering / Hierarchical clustering
  • association rules (apriori objects), exporting / Exporting association rules (apriori objects)
  • Naïve Bayes objects, exporting / Exporting Naïve Bayes objects
  • decision trees (rpart objects), exporting / Exporting decision trees (rpart objects)
  • decision trees (rpart objects, exporting / Exporting decision trees (rpart objects)
  • random forest objects, exporting / Exporting random forest objects
  • logistic regression objects, exporting / Exporting logistic regression objects
  • support vector machine objects, exporting / Exporting support vector machine objects
• predictive models
  • cross-validation, with caret package / Cross-validation and bootstrapping of predictive models using the caret package, Cross-validation, Performing cross-validation in R with caret
  • bootstrapping, with caret package / Cross-validation and bootstrapping of predictive models using the caret package, Cross-validation, Bootstrapping
  • new data, predicting / Predicting new data
• preprocess() function / Chapter 13 – Text Analytics with R
• principal() function / Accessing the PCA scores
• princomp() function / The inner working of Principal Component Analysis
• probability distributions
  • about / Probability distributions, Introducing probability distributions
  • Discrete uniform distribution / Discrete uniform distribution
  • normal distribution / The normal distribution
  • Student’s t distribution / The Student's t-distribution
  • binomial distribution / The binomial distribution
  • importance / The importance of distributions
• public datasets
  • k-means, using with / Using k-means with public datasets
  • all.us.city.crime.1970 dataset / Understanding the data with the all.us.city.crime.1970 dataset
  • life.expectancy.1971 dataset / Finding the best number of clusters in the life.expectancy.1971 dataset

Q

• qqnorm() function / Random intercepts and random slopes
• Quantile-Quantile plot (Q-Q plot) / Obtaining the residuals

R

• R
  • packages, installing in / Installing packages in R
  • packages, loading in / Loading packages in R
  • PCA, using / Learning PCA in R
  • data, analyzing with apriori / Analyzing data with apriori in R, Using apriori for basic analysis
  • data, analyzing / Analyzing data in R: correlation and regression
  • k-NN, working with / Working with k-NN in R
  • Naïve Bayes, working with / Working with Naïve Bayes in R
  • analyses, performing / Performing the analyses in R
  • conditional inference trees / Conditional inference trees in R
  • multilevel modeling / Multilevel modeling in R
  • news mining / Mining the news with R
• r.squaredLR() function / Random intercepts and fixed slopes
• random forest
  • about / Classification and regression trees and random forest, Random forest
  • bagging / Bagging
  • installing / Installing random forest
• ranef() function / The null model
• R console
  • menu bar / The menu bar of the R console
• references / Chapter 1 – Setting GNU R for Predictive Modeling, Chapter 4 – Cluster Analysis, Chapter 7 – Exploring Association Rules with Apriori, Chapter 13 – Text Analytics with R
• regression / Covariance and correlation
• removeSparseTerms() function / Preprocessing and inspecting the corpus
• review classification
  • about / Classification of the reviews
  • document classification, with k-NN / Document classification with k-NN
  • document classification, with Naïve Bayes / Document classification with Naïve Bayes
  • document classification, with logistic regression / Classification using logistic regression
  • document classification, with support vector machines (SVM) / Document classification with support vector machines
• reviews
  • classifying / Classification of the reviews
• R graphic user interface (RGui)
  • about / The R graphic user interface
• Robinson effect / Nested data
• robust regression
  • using / Robust regression
• roulette case
  • about / The roulette case
• rpart() function / Installing CART
• RStudio
  • URL / The R graphic user interface
• {rJava} package
  • URL / Installing C4.5

S

• scatterplots
  • example / Scatterplots
• simple regression
  • about / Understanding simple regression
  • intercept / Understanding simple regression
  • slope coefficient / Understanding simple regression
  • intercept, computing / Computing the intercept and slope coefficient
  • slope coefficient, computing / Computing the intercept and slope coefficient
  • residuals, obtaining / Obtaining the residuals
  • coefficient significance, computing / Computing the significance of the coefficient
• sjp.lmer() function / Random intercepts and random slopes
• skmeans() function / Distance measures
• slope coefficient, simple regression
  • computing / Computing the intercept and slope coefficient
  • significance, computing / Computing the significance of the coefficient
• solutions / Chapter 1 – Setting GNU R for Predictive Modeling, Chapter 4 – Cluster Analysis, Chapter 6 – Dimensionality Reduction with Principal Component Analysis, Chapter 7 – Exploring Association Rules with Apriori, Chapter 8 – Probability Distributions, Covariance, and Correlation, Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes, Chapter 11 – Classification Trees, Chapter 13 – Text Analytics with R
• Spearman’s correlation / Spearman's correlation
• stacked bars / Stacked bars
• Student’s distribution / The Student's t-distribution
• support-based pruning
  • used, for generating rules / Generating itemsets with support-based pruning
• support vector machines (SVM)
  • used, for document classification / Document classification with support vector machines
• Swiss politics
  • URL / Exploring the results of votes in Switzerland
• Swiss Statistics Office
  • URL / Exploring the results of votes in Switzerland

T

• term-document matrix / An introduction to text analytics
• text analytics
  • about / An introduction to text analytics
  • textual documents, preprocessing / An introduction to text analytics
• tokenizing / An introduction to text analytics
• total sum of square (TSS) / Understanding the data with the all.us.city.crime.1970 dataset
• training
  • creating / Creating the training and testing data frames

U

• United States State Coordinates
  • URL / Integrating supplementary external data
• update() function / Updating graphics
• USCancerRates dataset
  • used, for exploring cancer related deaths / Case study – exploring cancer-related deaths in the US
  • discovering / Discovering the dataset
  • supplementary external data, integrating / Integrating supplementary external data

V

• variable
  • about / A quick look at the Misc menu
• vector
  • about / A quick look at the Misc menu
• vegdist() function / Distance measures

W

• writeCorpus() function / Collecting news articles in R from the New York Times article search API

X

• xyplot() function
  • used, for discovering multipanel conditioning / Discovering multipanel conditioning with xyplot()
  • about / Displaying data points as text